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Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Existing research on collaborative innovation mechanisms from the perspective of global operation is very limited. This paper aims to address the research gap by studying the factors influencing globally distributed teams’ innovation performance, especially how effective knowledge sharing between distributed teams promotes collaborative team innovation.

This research proposes a model to investigate how collaborative knowledge sharing affects global operations [team dispersion, task orientation, information and communication technology (ICT) usage] and innovation performance based on the data collected from 167 managers in 40 local Chinese IT and offshoring firms. Using the theory of Cognitive Diversity and Innovation Diffusion and Synergy, separate hierarchical regression analysis was used to test the proposed model.

The findings of this study demonstrate that effective collaborative knowledge sharing plays a crucial role in enhancing innovation performance in a global operation. Specifically, innovation capacity can be improved by task orientation, ICT usage and team dispersion.

This research study contributes to the development of global distributed operations and innovation among distributed teams in multinational corporations.

This paper aims to explore the adsorption kinetics of syringin from Syringa oblata Lindl. leaves on macroporous resin and develop an efficient, simple and recyclable technology for the separation and purification of syringin.

Static adsorption and desorption properties of six resins were tested to select a suitable resin for the purification of syringin. Langmuir and Freundlich isotherm models were used to estimate the adsorption behavior of syringin on AB-8 resin. Breakthrough point and eluent volume were determined by dynamic adsorption and desorption tests. High-performance liquid chromatography-electrospray ionization-mass spectrometry was applied to identify the syringin in the purified product [syringin product (SP)]. Antioxidant and antibacterial activities of SP in vitro were evaluated by free radical scavenging ability and biofilm formation inhibitory tests.

AB-8 exhibited the most suitable adsorption and desorption capacity. Adsorption isotherm parameters indicated favorable adsorption between AB-8 and syringin. The optimal results were as follows: for adsorption, the sample concentration was 1.85 mg/mL, the sample volume was 3.5 bed volume (BV), the flow rate was 0.5 mL/min; for desorption, the ethanol concentration was 70%, the elution volume was 2.5 BV, the elution velocity was 1.0 mL/min. SP with 80.28% syringin displayed the potent antioxidant activities and inhibitory effects on biofilm formation of Streptococcus suis.

To the best of authors’ knowledge, there are no reports on purifying syringin from Syringa oblata Lindl. leaves using macroporous resins. This paper may also provide a theoretical reference for the purification of other phenylpropanoid glucosides.

Current practices for inspecting highway construction predominantly rely on manual processes, which result in subjective assessments, errors and time inefficiencies. The purpose of this study is to address the inefficiencies and potential inaccuracies inherent in manual highway construction inspections. By leveraging computer vision and ontology reasoning, the study seeks an automated and efficient approach to generate structured construction inspection knowledge in the format of checklists for construction activities on highway construction job sites.

This study proposes a four-module framework based on computer vision and ontology reasoning to enable the automatic generation of checklists for quality inspection. The framework includes: (1) the interpretation of construction scenes based on computer vision, (2) the representation of inspection knowledge into structured checklists through specification processing, (3) the connection of construction scenes and inspection knowledge via ontology reasoning and (4) the development of a prototype for the automatic generation of checklists for highway construction.

The proposed framework is implemented across four distinct highway construction scenarios. The case demonstrations show that the framework can interpret construction scenes and link them with relevant inspection knowledge automatically, resulting in the efficient generation of structured checklists. Therefore, the proposed framework indicates considerable potential for application in the automatic generation of inspection knowledge for the quality inspection of highway construction.

The scientific and practical values of this study are: (1) the establishment of a new method that promotes the automated generation of structured inspection knowledge for highway construction by integrating computer vision and ontology reasoning and (2) the development of a novel framework that provides efficient and immediate access to inspection knowledge related to what needs to be inspected at highway construction job sites.

This paper aims to study the impact of external fairness of executive compensation on M&A premium, and examine the moderate role of institutional investors. The high M&A premium is the main factors that induce the huge impairment of listed companies’ goodwill and the plummeting performance. Executives are the decision-makers of M&As, and their decision-making process is inevitably affected by the psychological factors. In recent years, institutional investors have become an important external force that can affect the governance of listed companies.

The authors use M&A data of listed companies from 2008 to 2018 and use OLS regression to test the relationship between executive compensation fairness and M&A premium.

The results show that the lower the external fairness of executive compensation, the greater the M&A premium. Institutional investors can effectively reduce the impact of external compensation unfairness on M&A premiums. The mechanism tests show that executives' psychological perception of fairness induced by external unfairness reduces their motivation to work and prompts them to use high premium to seek alternative compensation incentives. Further examinations of executive characteristics and corporate characteristics show that the role of external unfairness in executive compensation in driving M&A premiums is more pronounced in companies with longer executive tenure, weaker executive reputation incentives and private property.

This paper enriches the research on the pre-factors of M&A premiums from the perspective of executives’ psychological perception of fairness, provides evidence that institutional investors play a positive governance role and provides decision-making references for companies to take corresponding measures to reduce M&A premium risks.

The current study aims to ascertain how green entrepreneurial orientation (GEO) affects green innovation performance (GIP) through the mediating mechanism of the knowledge creation process (KCP) and whether or not these associations can be strengthened or hampered by the moderating impacts of resources orchestration capabilities (ROC).

The research used data from managers at various levels in 154 manufacturing enterprises in Pakistan to evaluate the relationships among the constructs using hierarchical regression analysis and moderated mediation approach.

The study indicates that GEO substantially impacts firms' GIP. GEO and GIP's relationship is partially mediated by two KCP dimensions: knowledge integration (KI) and knowledge exchange (KE). Furthermore, ROC amplifies not only the effects of GEO on KE but also the effects of KE on GIP. The moderated mediation results demonstrate that KE has a greater mediating influence on GEO and GIP when ROC is higher.

To better understand GEO's advantages and significance, future studies should look into the possible moderating mechanisms of environmental, organizational culture/green capability in the association between GEO, KCP and GIP.

The research helps expand the field of green entrepreneurship and GIP literature by providing a deeper knowledge of GEO and offering insight into how to boost GI in manufacturing firms.

This research helps fill in knowledge gaps in the field by delving further into the mechanisms by which GEO promotes GIP, both directly and indirectly, via the mediating role of KCP and the moderating impacts of ROC.