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As a resource input in enterprise technological innovation, patents play an important role in influencing innovation performance. The purpose of this paper is to investigate the impact of fuzzy front end (FFE) patent management on innovation performance, and the mediating role of patent commercialization and the moderating effect of technological lock-in.

This paper adopts a questionnaire survey from a sample of 203 high-tech Chinese enterprises across multiple industries. Structural equation modeling and the hierarchical regression method were used to test the hypothesis.

The results show that the FFE of patent management, namely, patent acquisition and patent protection, positively affect innovation performance. Specifically, patent commercialization mediates the relationship between FFE patent management and innovation performance. Moreover, technological lock-in moderates the relationship between patent management and innovation performance.

This study puts forward suggestions relating to institution innovation and mechanism innovation for effective patent management in firms, and provides some guidelines for firms to efficiently utilize patents to improve innovation performance.

This paper provides certain empirical evidence for the study of organizational structure, strategic management, and knowledge governance. As the main participators in technology innovation, high-tech enterprises should utilize both inside and outside resources to acquire patents.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

This paper aims to study the removal of wide-stripe noise in hyperspectral remote sensing images. There is a great deal of stripe noises in short-wave infrared hyperspectral remote sensing image, especially wide-stripe noise, which brings great challenge to the interpretation and application of hyperspectral images.

To remove the noise and to reduce the impact based on in-depth study of the mechanism of the stripe noise generation and its distribution characteristics, this paper proposed two statistical local processing and moment matching algorithms for the elimination of wide-stripe noise, namely, the gradient mean moment matching (GMMM) algorithm and the gradient interpolation moment matching (GIMM) algorithm.

The experiments were carried out with the practical short-wave infrared hyperspectral image data and good experiment results were obtained. Experiments show that both can reduce the impact of wide-stripe noise, and the filtering effect and the application range of the GIMM algorithm is better than that of the GMMM algorithm.

Using new methods to deal with the hyperspectral remote sensing images, it can effectively improve the quality of hyperspectral images and improve their utilization efficiency and value.

The investigations could guide the structural design and fatigue life prediction of air-conditioning compressor valve plates.

The High-Cycle Fatigue (HCF) and Very-High-Cycle Fatigue (VHCF) behaviors of stainless steel used for air-conditioning compressor valve plates were investigated. Monotonic and cyclic loading conditions were designed to explore the fatigue responses according to the load characteristics of the structure.

The crack initiation can be observed as the arc-shaped cracks at both sides of specimens and Y-shaped crack bifurcation in the specimens. Moreover, the middle section and the cracks at both ends are not connected to the surface of the specimen. The stress-life results of the materials under two directions (vertical and horizontal) were provided to examine the difference in fatigue strength.

Monotonic and cyclic loading conditions were designed to explore the fatigue responses according to the load characteristics of the structure. Based on the experimental data, the results indicate that specimens under cyclic loading conditions could demonstrate better mechanical performance than static loadings.

The purpose of this investigation was to study the function mechanisms of a corrosion inhibitor package used for martensitic stainless steel tubulars in acid solution at high temperatures.

The inhibition performance was evaluated by means of an acid corrosion test at high temperature and high pressure, and the functional mechanism of the inhibitor package at different temperatures was investigated using polarization curve and electrochemical impedance spectroscopy measurements.

The results showed that the corrosion inhibitor package chosen for high-temperature and high-pressure gas well applications was very suitable for use with 13Cr super martensitic stainless steel. At lower temperatures, the function mechanism of the corrosion inhibitor package was characterized as a type of negative catalytic effect. As the temperature was increased, the effect of the intensifier in the package became more significant and the function mechanism changed to be the geometric covering effect type.

This study has the important practical value for guiding the oil field to conduct reasonable screening and using the acidizing corrosion inhibitor for martensite stainless steel tubulars.

Blockchain and Internet of Things (IoT) technologies have recently gained much attention for Industry 4.0. With the emergence of disruptive technologies, it has become essential to redesign the business for innovations based on blockchain–IoT integrated architecture that helps organizations to improve agility in their operations. The paper aims to discuss this issue.

An industrial pump was Sensorized and IoTized to monitor its operations on real time and take predictive measures for managing these assets with more agility. The developed architecture was further extended for proposing the use of blockchain and how it can benefit the organization.

The known features of blockchain such as increasing the capacity of decentralization, trust-less transactions, security and allowing autonomous coordination of the devices along with the boons of IoT will help achieve the motto of improving agility in Industry 4.0.

This paper gives a new dimension to utilization of blockchain technology. blockchain along with IoT that gives a way forward for industries like manufacturing, oil and gas, engineering and construction, utilities, etc. to re-designing the business organization in a more agile way.

In this paper, the authors aim to propose an effective method to indirectly determine nonlinear elastic shear stress-strain constitutive relationships for nonlinear elasticity materials, and then study the nonlinear free torsional vibration of Al–1%Si shaft.

In this study the authors use BoxLucas1 model to fit the determined-experimentally nonlinear elastic normal stress–strain constitutive relationship curve of Al–1%Si, a typical case of isotropic nonlinear elasticity materials, and then derive its nonlinear shear stress-strain constitutive relationships based on the fitting constitutive relationships and general equations of plane-stress and plane-strain transformation. Hamilton’s principle is utilized to gain nonlinear governing equation and boundary conditions for free torsional vibration of Al–1%Si shaft. Differential quadrature method and an iterative algorithm are employed to numerically solve the gained equations of motion.

The effect of four variables, namely dimensionless fundamental vibration amplitude ϑmax, radius α and length β, and nonlinear-elasticity intensity factor δ, on frequencies and mode shapes of the shafts is obtained. Numerical results are in good agreement with reference solutions, and show that compared with linearly elastic shear stress-strain constitutive relationships of the shafts made of the nonlinear elasticity materials, its actual nonlinearly elastic shear stress-strain constitutive relationships have smaller torsion frequencies. In addition, but β having opposite hardening effect, the rest of the four variables have softening effect on nonlinearly elastic torsion frequencies. Eventually, taking into account nonlinearly elastic shear stress-strain constitutive relationships, changes of the four factors, i.e. ϑmax, α, β and δ, cause inflation and deflation behaviors of mode shapes in nonlinear free torsional vibration.

The study could provide a reference for indirectly determining nonlinear elastic shear stress-strain constitutive relationships for nonlinear elasticity materials and for structure design of torsional shaft made of nonlinear elasticity materials.

In the last a few years a number of highly publicized incidents of Distributed Denial of Service (DDoS) attacks against high‐profile government and commercial websites have made people aware of the importance of providing data and services security to users. A DDoS attack is an availability attack, which is characterized by an explicit attempt from an attacker to prevent legitimate users of a service from using the desired resources. This paper introduces the vulnerability of web applications to DDoS attacks, and presents an active distributed defense system that has a deployment mixture of sub‐systems to protect web applications from DDoS attacks. According to the simulation experiments, this system is effective in that it is able to defend web applications against attacks. It can avoid overall network congestion and provide more resources to legitimate web users.

The purpose of this study is to examine the role played by coercive, normative and mimetic pressures in stimulating timeliness of corporate internet reporting (TCIR).

This study uses content analysis technique to track the TCIR practices of top 100 non-financial companies listed on the Dhaka Stock Exchange. A disclosure index of 14 items is developed to capture the extent of TCIR. The authors collected the relevant data from multiple sources, such as corporate websites, monthly review reports and corporate annual reports for the year-end 2019. This study uses Poisson regression models to explore the association between institutional pressures and TCIR.

Consistent with the predictions of institutional isomorphism theory, the authors find that coercive isomorphic pressures through ownership by foreign investors, government, general public and connection with parent multinational corporations have positive associations with TCIR. The authors also find that normative pressures resulting from cross-directorships have positive influence on TCIR. The authors provide evidence of mimetic pressures through industry memberships (i.e. companies operating in technology-based industry) positively impacting TCIR. The additional analysis suggests that institutional pressures are rather associated with the extent of voluntary TICR and to a lesser extent to regulatory TICR.

To the best of the authors’ knowledge, this study is the first to show the positive impacts of coercive, normative and mimetic isomorphic pressures on TCIR in an emerging economy characterized by weak institutional environment and mixed prospects for TCIR.

Background: This paper examines the impact of coronavirus COVID-19 outbreak on employees’ mental health, specifically psychological distress and depression. It aims at identifying the main stressors during and post COVID-19, examining the main moderating factors which may mitigate or aggravate the impact of COVID-19 on employees’ mental health and finally to suggest recommendations from a human resource management perspective to mitigate COVID-19’s impact on employees’ mental health.

Methods: This paper is a literature review. The search for articles was made in Google scholar, Web of Science and Semantic scholar. We used a combination of terms related to coronavirus OR COVID-19, workplace and mental health. Due to the paucity of studies on the COVID-19 impact on employees’ mental health, we had to draw on studies on recent epidemics.

Results: The identified literature reports a negative impact of COVID-19 on individual’s mental health. Stressors include perception of safety, threat and risk of contagion, infobesity versus the unknown, quarantine and confinement, stigma and social exclusion as well as financial loss and job insecurity. Furthermore, three dimensions of moderating factors have been identified: organizational, institutional and individual factors. In addition, a list of recommendations has been presented to mitigate the impact of COVID-19 on the employee’s mental health, during and after the outbreak, from a human resource management perspective.

Conclusions: Coronavirus is new and is in a rapid progress while writing this paper. Most of current research are biomedical focusing on individuals’ physical health. In this context, mental health issues seem overlooked. This paper helps to broaden the scope of research on workplace mental health, by examining the impact of a complex new pandemic: COVID-19 on employees’ mental health, from social sciences perceptive, mobilizing psychology and human resource management.