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This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

As humans are influenced by their environment, this study explores how different construal levels of ambient scent temperature affect consumers’ food choices.

This study employed a series of experimental methods from three studies, totalling five experiments. The experiments involved both laboratory and field settings, as well as neuroscientific techniques, thus generating empirical evidence.

Three studies were conducted to investigate how construal levels of both ambient scent temperature and tasks influenced food choice. Study 1 found that the construal level of ambient scent temperature significantly affected the type of food consumed. Study 2 included the task’s construal level as another factor to examine whether it interacted with the ambient scent temperature construal level. Both factors were significant, but only when perceived by the participants simultaneously. If the task’s construal level was manipulated before exposure to the ambient scent temperature, the latter did not have a significant effect. Study 3 employed a neuroscientific method to explore the mechanism behind the match between ambient scent temperature and food choices based on construal levels. The congruence of ambient scent temperature and food choice based on construal level enhanced positive emotions.

The sample size, although in line with other neuroscientific studies, was not sufficiently large for robust generalizability. This limitation can encourage future research to increase the number of participants and thus enhance the accountability of the findings. Another limitation is the participants’ cultural background.

This study’s practical implications are twofold. First, odour intensity was perceived to be the strongest in hot samples (Kähkönen et al., 1995), and we confirmed how ambient scent temperature can influence one’s food choice. Thus, food business operators can use warm ambient scent temperatures to promote hedonic food or snacks. Second, participants’ positive emotions were enhanced by the congruence of ambient scent temperature and food choice.

The association between ambient scent temperature and food choice has been extensively researched. However, this study provides an empirical explanation for the application of CLT. Accordingly, we performed a series of laboratory and field experiments using behavioural and neuroscientific approaches. The results confirmed that the construal level of ambient scent temperature significantly affected food choice. Moreover, the FAA revealed that one’s positive emotions would be prompted if there was congruence in the construal levels of ambient scent temperature and food choice.

This study has theoretical and managerial value because people’s poor understanding of food selection is affected by ambient scent temperature. Moreover, its novelty lies in the application of a neuroscientific approach to one experiment.

Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments.

This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future.

Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential.

In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures.

Compared to typical brand extension research focusing on functional and symbolic attributes, this paper aims to examine brand extensions based on a brand's primary sensory attributes. Specifically, this paper investigates the interplay between brand equity and primary sensory attributes in shaping consumers' evaluations of brand extensions.

This study investigates the impact of primary or central sensory attributes on brand extension evaluations for brands with differing brand equities. The authors conducted two experiments preceded by seven pretests to develop and validate the stimulus materials. The authors aim to contribute to understanding how sensory and brand-related factors influence consumers' evaluations of brand extensions.

In these experiments, the authors find that a parent brand's central/dominant sensory attribute allows the parent brand to successfully extend into functionally unrelated categories. For example, Dove’s central attribute of touch allows it to extend successfully into categories such as towels and shaving razor. However, it does not perform as well as Irish Spring (known for smell) in categories such as cologne and scented fabric softener, where Irish Spring's central attribute of smell is more relevant. Interestingly, Irish Spring, a lower equity brand, outperforms Dove in smell-related extensions, indicating that sensory attributes can counter the impact of lower brand equity if the sensory attribute is relevant to the extension category.

This study investigates brand extensions based on sensory attributes such as smell and touch instead of typical brand extensions based on functional and symbolic attributes. In particular, the authors examine whether the perceived fit between the parent brand’s dominant sensory attribute and the extended category (i.e. sensory fit) is more important than the parent brand's equity in the evaluation of brand extensions.

Within the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.

The study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.

This study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.

This study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.

The purpose of this paper is to consider the interface of research methodology paradigms (which are commonly conventionally perceived as being juxtaposed when conducting research) through the application of textual analytical software.

The argument develops matrices and applies these to the human resource (HR) context of individual and career group sense-making so as to better understand the inherent career dynamics and sense-making in a sample derived from a French media agency context.

The paper’s findings comprise a range of insights from the respondent managers (n = 26). The results suggest that a better understanding of methodologies facilitates the development of theories based on textual data analysis.

The limits of the approach are important. Indeed, it is necessary to make compromises and arrangements with epistemological orthodoxy. Indeed, at first and to be able to process the data with the Alceste© and Tropes©, the interviews were recorded, transcribed and considered as “definitive”. This potentially contradicts the methods of collecting data prescribed by the thematic analysis or the method of cognitive maps. These two interpretive approaches imply a process of co-construction of the results that would not theoretically rely solely on a recording.

It provides insights on how, and to what extent, can data analytical software packages facilitate a better understanding of paradigm (interpretive-positivistic) interoperation and commensurability.

Importantly, the study provides a novel means by which to study the important HR issue of career perception and trajectory.

The methodology-driven approach shows that there is potent scope to map and develop valuable and complementary data perspectives on research issues by interfacing paradigms and textual data analysis tools.

The growth of social media globally has led to brands adopting memes as a tool for communication. They have influenced digital culture, politics and marketing. This study aims to highlight the research gap in meme literature by profiling and science mapping.

This study has performed a bibliometric analysis of research papers on memes from the Scopus database. The authors profiled the literature and found the most relevant journal, author and document. Science mapping techniques such as thematic analysis, thematic evolution and co-citation of papers with content analysis were used.

Profiling of literature revealed that the most relevant journal, based on a number of papers and citations, is New Media and Society. The top researcher on memes is Shiffman L (seven papers), and the most cited author is Menczer F (717 citations). The paper titled “Towards A Unified Science of Cultural Evolution” has the highest number of citations (402). Thematic mapping revealed “Social Movements” and “Twitter” are the currently trending themes. The co-citation analysis clustered the literature into two. The first cluster elucidates the theoretical underpinnings of memes, while the second cluster explicates the various contexts in which memes are studied.

This study will be vital to researchers hoping to study internet memes by providing directions for future research. Furthermore, the insights from this study will enable social media managers to use memes effectively.

To the best of the authors’ knowledge, this study seems to be the first to use bibliometric analysis to profile and map meme literature. This study provides a new perspective for meme researchers to explore memes and suggests marketers implement novel meme-based strategies in their marketing communications.

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

The purpose of this paper is to propose an accurate and practical imitation learning for robotics. The modified dynamic movement primitives (DMPs), global fitting DMPs (GLDMPs), is presented. Framework design, theoretical derivation and stability proof of GLDMPs are discussed in the paper.

Based on the DMPs, the hierarchical iterative parameter adaptive framework is developed as the hierarchical iteration stage of the GLDMPs to tune the designed parameters adaptively to extract richer features. Inspired by spatial transformations, the coupling analytical module which can be regarded as a reversible transformation is proposed to analyze the high-dimensional coupling information and transfer it to trajectory.

With the proposed framework and module, DMPs derive majority features of the demonstration and cope with three-dimensional rotations. Moreover, GLDMPs achieve favorable performance without specialized knowledge. The modified method has been demonstrated to be stable and convergent through inference.

GLDMPs have an advantage in accuracy, adaptability and practicality for it is capable of adaptively computing parameters to extract richer features and handling variations in coupling information. With demonstration and simple parameter settings, GLDMPs can exhibit excellent and stable performance, accomplish learning and generalize in other regions. The proposed framework and module in the paper are useful for imitation learning in robotics and could be intuitive for similar imitation learning methods.

This paper aims to analyze the scientific trend of research on Islamic banking sustainability (IBS) through a bibliometric study. In particular, the paper extensively investigates all the articles issued through the Scopus database regarding the IBS.

The authors discovered 76 papers that met the function, subject and set requirements by using the phrase IBS. The authors used VOSviewer as an analytical tool and the Scopus website.

IBS publications were found in the period 2005–2022, and the publication trend of IBS research demonstrates that it is growing exponentially after 2018. Malaysia is the leading country in terms of productive authors, universities, number of documents, citations and collaboration research on IBS. The current research trends are summarized into five cluster maps for future research directions: sustainability measurement, sustainability practices, risk and governance, corporate social responsibility (CSR) and IBS theory. The Maqashid al Shariah approach conceptually influences the framework for constructing the dimensions and indicators used to measure the IBS.

The authors retrieved data for their research from the Scopus database; using other databases might result in totally different research patterns with this IBS bibliometric research.

The research encompasses valuable implications for Islamic banking as it offers valuable insights on how to assess the performance of IBS. Particularly, it contributes to identifying the dimensions and indicators needed to measure IBS performance. Furthermore, this research provides strategic initiatives to promote sustainable practices in Islamic banking in terms of green financing taxonomy, services, operations, risk management and governance.

This research is valuable for other scholars as it offers a foundation for the future growth of IBS research, focusing on important sustainability clusters obtained from selected reputable journals. This research is beneficial for regulators in enhancing the roadmap for establishing and enhancing long-term IBS with impacts on socio-economic, environmental and governance.

The study presents a concise review of the bibliometric study in IBS and provides recommendations for future research directions in cluster mapping of themes and subthemes. There is still insufficient research that examines the IBS, in particular, complete insights into the IBS literature review.