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Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This article aims to examine the manifestation of coloniality within social studies curricula and explore strategies for rejecting colonial paradigms through teaching praxis. The author presents a curriculum that unveils the narratives of Linnentown, a local Black community, to examine the impacts of colonial legacies on people's everyday lives.

Using the teaching and learning cycle framework, the author demonstrates the integration of sequenced embodied and multimodal activities. Furthermore, the author utilizes the concept of critical place-based education (CPBE) to demonstrate how critical pedagogies within the context of place can cultivate transformative learning experiences.

By guiding students to critically analyze the sociopolitical conditions of Linnentown residents, CPBE, paired with multimodal activities, helps challenge dominant narratives and empowers students to become agents of change in their local communities.

This curriculum fosters a nuanced understanding of structural oppression, empowers students to develop critical awareness and social agency and guides youth in confronting settler colonialism within and beyond their communities.

This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.

This paper aims to investigate the impact of China’s Green Credit Guidelines (GCG) policy on the environmental, social and governance (ESG) scores of restricted enterprises and examine firm’s speculative behavior in response to the policy.

This paper views the GCG policy proposed in 2012 as a quasinatural experiment and uses difference-in-differences (DID) model to evaluate its influence on the ESG scores of Chinese nonfinancial A-share listed enterprises from 2007 to 2019. Robustness tests include the propensity score matching (PSM)–DID method and permutation tests.

The GCG policy significantly increases the ESG scores of restricted enterprises, particularly enhancing environmental (E) performance. However, it only improves the social (S) and governance (G) performance of firms heavily reliant on bank credit, indicating speculative behavior by enterprises. Increased Government attention, a higher proportion of female executives and more developed local green finance reduce speculative behavior, while executives with financial backgrounds promote it.

Governments should mandate standardized ESG reporting and monitor restricted enterprises, banks should monitor speculative behavior and firms should integrate ESG into their long-term strategies to support sustainable development.

The results provide evidence of the effectiveness of implementing the GCG policy in China and offer guidance for better promoting green credit policy in developing countries, contributing to the transition toward a more sustainable future.

To the best of the authors’ knowledge, this paper is the first to explore if the GCG policy’s asymmetric effects on ESG components are due to enterprise speculative behavior and examines the factors influencing this behavior, providing insights for regulators to better implement the GCG policy to promote sustainable development.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

The purpose of this study is to examine the influence of knowledge creation (KC) process on customer relations management (CRM) in Palestinian commercial banks, taking into consideration which factors of KC process support the CRM system.

The study uses a quantitative research design wherein questionnaires have been used to collect data from 345 respondents in the Palestinian banking sector. Research hypotheses have been tested using multiple regression analysis.

The findings unveil that socialisation and combination processes have a positive impact on CRM. In contrast, internalisation process negatively affects CRM system, but outsourcing knowledge does not significantly affect CRM.

Past studies empirically validated the success of CRM adaptation in the context of different industries. This study provides a new conceptual model which validates the influence of KC on CRM in the banking sector. It also affirms the integral role of KC in supporting CRM from an emerging country perspective like Palestine.

This study offers new insights into creating of knowledge by employees in supporting CRM. It will encourage future scholars to further explore the key dimensions of the KC process for a more detailed investigation at a workplace. This study suggests that banks’ directors and employees should behave in a social manner to support relationship with customers. This study also suggests facilitating knowledge from different resources in innovative ways, through encouraging creative thinking from experiences, using technology in sharing knowledge, focussing on appropriate training to resolve customers' problems and disseminating new knowledge among employees.

This study expands the body of knowledge on KC process in supporting CRM from an emerging country perspective. This study validates the influence of KC on CRM in the Palestinian banking sector. This sheds light on the integration of these two concepts.