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The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Strategy scholars have widely recognized the central role that narratives play in the construction of organizational identities. Moreover, storytelling is an important strategic asset that firms can leverage to inspire employees, excite investors and engage customers' attention. This chapter illustrates how advancements in computational linguistic may offer opportunities to analyze the stylistic elements that make a story more convincing. Specifically, we use a topic model to examine how narrative conventionality influences the performance of 78,758 craftsmen selling their handmade items in the digital marketplace of Etsy. Our findings provide empirical evidence that effective narratives display enough conventional features to align with audience expectations, yet preserve some uniqueness to pique audience interest. By elucidating our approach, we hope to stimulate further research at the interface of style, language and strategy.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Touch plays an important role in the evaluations of products. However, prior quantitative touch research does not present a holistic perspective of haptics. This study aims to provide a more comprehensive understanding of haptic information that consumers attain from touch.

Adopting a qualitative research approach, semi-structured, depth interviews were conducted with industry experts.

Based on the findings, this research proposes a conceptual model of consumer haptic perception consisting of seven key influences, namely, the effects of haptic sensation, haptic perception, the influence of individual factors, the influence of external environmental factors, the multi-dimensionality of haptics, haptic cue congruity and haptic dominance.

This study suggests firms to understand the importance of consumers’ haptic perception as an opportunity to engage an individual’s heart and mind through information attained through touch.

This is the first study in marketing to propose a conceptual model of haptics comprising several new haptic influences. This model contributes to the emerging literature of sensory marketing by providing directions for future touch research by capturing the dynamic nature of haptics.

El tacto desempeña un papel importante en nuestras evaluaciones de los productos. Sin embargo, las investigaciones cuantitativas anteriores sobre el tacto no presentan una perspectiva holística de la percepción táctil. Este artículo pretende ofrecer una comprensión más completa de la información táctil que los consumidores obtienen del contacto.

Adoptando un enfoque de investigación cualitativa, se realizaron entrevistas semiestructuradas en profundidad con expertos del sector.

A partir de los resultados, esta investigación propone un modelo conceptual de la percepción táctil del consumidor que consta de siete influencias clave: Los efectos de la sensación háptica, la percepción háptica, la influencia de los factores individuales, la influencia de los factores ambientales externos, la multidimensionalidad de la sensación táctil, la congruencia de las pistas táctiles y la dominancia de la sensación táctil.

Este estudio sugiere a las empresas que comprendan la importancia de la percepción táctil de los consumidores como una oportunidad para captar el corazón y la mente del individuo a través de la información obtenida mediante el tacto.

Este es el primer estudio en marketing que propone un modelo conceptual de la percepción táctil que incluye varias influencias táctiles nuevas. Este modelo contribuye a la literatura emergente del marketing sensorial proporcionando direcciones para la futura investigación táctil al capturar la naturaleza dinámica del sentido del tacto.

触摸在我们对产品的评价中起着重要作用。然而, 在先前定量的触觉研究中并未呈现触觉的整体视角。本文旨在更全面地了解消费者通过触摸获得的触觉信息。

本文采用定性研究方法, 与行业专家进行了半结构化的深度访谈。

本研究提出了一个消费者触觉感知的概念模型, 其中包括七个关键影响因素：触觉的影响、触觉感知、个人因素的影响、外部环境因素的影响、触觉的多维性、触觉线索的一致性和触觉的主导性。

这项研究建议企业了解消费者触觉感知的重要性, 并将触觉信息传达作为吸引消费者心灵的机会。

这是营销领域首次提出触觉概念模型的研究, 该模型包含几种新的触觉影响因素。这个触觉概念模型通过捕捉触觉的动态特性为未来的触觉研究提供方向, 从而为新兴的感官营销文献做出了贡献。

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.