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Shortest path problem has always been a hot topic in the study of graph theory, because of its wide application field, extending from operational research to the disciplines of geography, automatic control, computer science and traffic. According to its concrete application, scholars in the relevant field have presented many algorithms, but most of them are solely improvements based on Dijkstra algorithm. The purpose of this paper is to enrich the kinds of (and improve the efficiency of) the shortest path algorithms.

This paper puts forward an improved calculation method of shortest path using cellular automata model, which is designed to search the shortest path from one node to another node. Cellular state set is adjusted with combination of breeding and mature states. Evolution rule is improved to enhance its parallelism. At the same time, recording manner of cellular state turnover is modified to record all information sources.

The result indicates that the improved algorithm is correct and more efficient, in that it could reduce the times of cellular state turnover; meanwhile, it can solve multi‐paths problem.

In this paper, cellular state set in exiting shortest path algorithm based on cellular automata theory is adjusted; evolution rule is improved; and recording manner of cellular state turnover is modified to record all information sources. All of which make the parallelism of this algorithm enhanced and the multi‐paths problem solved.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This paper provides the latest systematic literature review (SLR) of prevailing studies on the interrelationship among executive compensation, financial performance and sustainable business practices. This SLR is done in three parts: (1) examine the theories employed by previous studies; (2) identify the unique variables employed by researchers in analysing this interrelationship and (3) explore potential opportunities for further study in the field.

This study conducted an SLR analysing studies from the Web of science, Scopus and EBSCO in over 20 countries from 2009 to 2022 published in several top-ranked journals. We utilised various search strings using the key phrases “executive compensation”, “CEO Pay”, “financial performance” and “sustainable business practices”. The initial sample of 27,210 was filtered with our meticulous inclusion and exclusion criteria to produce a list of 161 studies.

Our findings are as follows: first, most studies encompassing this subject area lack multi-theoretical perspectives with agency theory being the most dominant theoretical viewpoint; second, we observed the use of monotonous quantitative research methods, with studies heavily lacking qualitative and mixed-method research approaches; finally, there is a palpable gap in cross-country studies.

There are a few limitations that must be acknowledged. First, the inclusion criteria ensured that only articles published in the CABS journal ranking of three star and above. Thus, this review may not be a precise reflection of the EC, FP and SBPs literature scope. The inclusion criteria also limit our review to only accounting, finance, management and business-related studies about the topic. Therefore, future studies could explore studies ranked three star and below and from other subject areas.

This study contributes to the existing literature by conducting a comprehensive SLR that examines both the theoretical underpinnings and empirical evidence on this topic. It builds upon previous research and extends our understanding of the interrelationship among executive compensation, financial performance and sustainable business practices.

The combination of improved PSO (IPSO) algorithm and artificial neural network (ANN) model for intelligent monitoring of the bearing performance of the hydrostatic turntable.

This paper proposes an artificial neural network model based on IPSO algorithm for intelligent monitoring of hydrostatic turntables.

The theoretical model proposed in this paper improves the accuracy of the working performance of the static pressure turntable and provides a new direction for intelligent monitoring of the static pressure turntable. Therefore, the theoretical research in this paper is novel.

Theoretical novelties: an ANN model based on the IPSO algorithm is designed to monitor the load-bearing performance of a static pressure turntable intelligently; this study show that the convergence accuracy and convergence speed of the IPSO-NN model have been improved by 52.55% and 10%, respectively, compared to traditional training models; and the proposed model could be used to solve the multidimensional nonlinear problem in the intelligent monitoring of hydrostatic turntables.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0081/

This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations.

In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction.

Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.

The purpose of this paper is to formulate frameworks for the drivers and barriers of integrated sustainable solid waste management (ISSWM) with reference to conditions prevailing in India.

A multi-phased approach was adopted in this paper to come up with the conceptual framework of the drivers and barriers of ISSWM. In the first phase, drivers and barriers of ISSWM were identified based on a systematic literature review process. In the second phase, 25 experts having 15 plus years of experience in the field of sustainable development and environmental management were consulted to get their opinion. Validation and understanding of the interrelationship among the selected drivers and barriers were done based on the insights from expert interviews. And in the final phase, structural self-interaction matrix and transitive links are defined based on the expert opinion to come up with the theoretical frameworks of drivers and barriers of ISSWM.

Findings reveal the importance to have a system view point approach by giving equal importance to social, environmental and economic pillars of sustainability along with the technology component to effectively and sustainably manage the solid waste disposal. Institutional effectiveness and the robust policy and frameworks are the two variables found to have the highest driving power. Poor social values and ethics, huge population and illiteracy are the three most critical barriers faced by developing nations in achieving the sustainability practices in the solid waste management. The proposed frameworks of drivers and barriers of ISSWM will definitely help policy makers to effectively manage the sustainable waste management practices for developing economies by focusing on the key variables listed out.

One of the limitations is in the use of very limited sample size in the study. Another limitation is that total interpretive structural modeling fails to come up with the relative weightings of drivers and barriers used in the study. These limitations can be overcome by extending the research by using a semi-structured questionnaire survey with higher sample size for the empirical validation of the model.

This research will help to clearly understand the framework of drivers and barriers of variables and their hierarchical level based on the driving power and dependence. Since such articles focusing on the conceptual frameworks of drivers and barriers of ISSWM are found to be very scant, this paper will equally help academicians and waste management professionals to understand the concepts deeply, by getting answers to the fundamental questions of “what,” “why” and “how.” Developed framework of drivers explicitly shows the need to attain financial stability through the commercialization of the waste management initiatives, which will help to reduce burden on various governmental institutions. Commercialization opportunities will also help to have more successful start-up ventures in solid waste management domain that can provide improved employment opportunities and hygiene environment in the developing nations like India.

Based on the authors’ best knowledge, there is hardly any article that explicitly explains the conceptual frameworks of the drivers and barriers of ISSWM by considering the conditions prevailing in developing countries like India. And thus, this can be considered as one of the unique research attempts to build a clear conceptual framework of ISSWM. The study contributes significantly to the existing literature body by clearly interpreting the interrelationships and the driving power and dependence of variables of ISSWM.

This study aims to analyze those factors affecting the rural resident’s willingness to adopt solar photovoltaic (PV) which is important for accelerating the popularization of clean energy in China.

This study contained a sample of 653 households in 8 provinces/regions by stratified, and random sampling in rural China. Descriptive analysis, exploratory factor analysis and confirmatory factor analysis techniques have been used for analytical purposes.

The empirical results indicate that financial incentive and social interaction have positive effects on rural residents’ adoption willingness, while village leaders’ engagement can indirectly influence their adoption willingness through social interaction and residents’ cognition.

This study mainly considers external and internal factors but ignores the effect of technical factors. In addition, the samples are just selected from the residents who have adopted solar PV.

This study is expected to be useful for the government, regulators, village leaders, village leaders and other institutions.

This study conducts a systematic analysis and clarifies the relationship between factors (external and internal) and rural residents’ adoption willingness. The village leaders’ engagement is first added to the conceptual model as an external factor, which is very essential in rural residents’ adoption in China.

This paper aims to extract the comment targets in Chinese online shopping platform.

The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment.

The extracting comment target method the authors proposed in this paper is effective.

First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information.

Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients.

The extracting comment target method the authors proposed in this paper is effective.