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The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The current study examines the effects of race, agency and environment on traffic stops in rural and non-rural spaces.

Using traffic stop data collected in a Midwest US County from January 1, 2020 to December 31, 2021, the current study uses logistic regression to examine racial disparities in traffic stops.

The results indicate that police decision-making in traffic stops may be influenced by other factors besides a driver’s race or ethnicity. In other words, the police officer’s decision making in a traffic stop varies between small and large agencies as well as rural and non-rural places.

This study provides one of the few examinations of racial disparities in traffic stops in rural places.

Introduction: As Internet usage increases, so does widespread concern about surveillance and privacy. While most of the research primarily focuses on a particular digital setting, these problems cut beyond national boundaries and impact economies everywhere.

Purpose: This study critically analyses the Data Protection Bill 2019’s effectiveness within the context of surveillance and privacy in India’s digital economy. Investigating critical provisions of the bill, comparing it to international privacy laws and standards, and identifying potential gaps and weaknesses, this study provides insights into the bill’s ability to protect personal data and limit surveillance practices.

Methodology: The chapter is based on secondary sources of data, including academic articles, government reports, and news articles on the topics of surveillance, privacy, and the Data Protection Bill 2019 in India, involving content and critical discourse analyses.

Findings: The Data Protection Bill 2019 evaluation reveals a set of provisions with the overarching intent to safeguard citizens’ privacy worldwide and curtail undue surveillance practices exercised by both governmental bodies and private enterprises. Intricately delineates the entitlements of individuals concerning their data, encompassing vital aspects such as the right to access, rectify, and erase their data, the bill mandates stringent adherence to the principle of explicit consent when collecting and processing personal data.

Nevertheless, a comprehensive analysis also reveals several gaps and constraints inherent in the bill’s framework. One such area is the inclusion of exemptions for governmental entities, an aspect that raises international concerns regarding potential disparities in data protection practices.

It has been demonstrated that AI-powered, data-driven tools’ usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy.

The authors introduce the results of a case study based on a large educational intervention at a fully online university. The views of the participants from degrees representing different knowledge areas and contexts of technology adoption (work, education and leisure) were explored after engaging in the analysis of the terms and conditions of use about privacy and data usage. After consultation, 27 course instructors (CIs) integrated the activity and worked with 823 students (702 of whom were complete and correct for analytical purposes).

The results of this study indicated that the intervention increased privacy-conscious online behaviour among most participants. Results were more contradictory when looking at the tools’ daily usage, with overall positive considerations around the tools being mostly needed or “indispensable”.

Though appliable only to the authors’ case study and not generalisable, the authors’ results show both the complexity of privacy views and the presence of forms of renunciation in the trade-off between data protection and the need of using a specific software into a personal and professional context.

This study provides an example of teaching and learning activities that supports the development of data literacy, with a focus on data privacy. Therefore, beyond the research findings, any educator can build over the authors’ proposal to produce materials and interventions aimed at developing awareness on data privacy issues.

Developing awareness, understanding and skills relating to data privacy is crucial to live in a society where digital technologies are used in any area of our personal and professional life. Well-informed citizens will be able to obscure, resist or claim for their rights whenever a violation of their privacy takes place. Also, they will be able to support (through adoption) better quality apps and platforms, instead of passively accepting what is evident or easy to use.

The authors specifically spot how students and educators, as part of a specific learning and cultural ecosystem, need tailored opportunities to keep on reflecting on their degrees of freedom and their possibilities to act regarding evolving data systems and their alternatives.

Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerism’s impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability.

A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023.

The findings reveal four prominent factors – behavioural, environmental, socioeconomic and technological – in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways.

Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour.

Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends.

The Internet has encouraged rural tourism experience providers to develop a new management strategy that opts for disintermediation to access the market. In this context, incoming travel agencies (destination management companies [DMCs]), despite the local component, lose capacity to promote the rural tourism. The main question is what kind of relationship between stakeholders would enhance effective intermediation processes between them. The paper examines such constraints and limitations of existing relationships between small local rural tourism producers and DMCs.

Using the Basque Country region of northern Spain as a case study, the authors used a mixed qualitative and quantitative methodology based on semi-structured in-depth interviews and an online survey. The data analysis strategy used incorporated descriptive and exploratory factor analysis (EFA).

In this research, most of the tourism disintermediation factors identified in previous literature were reinforced when tested in the rural context. The results confirm that power, value, product differentiation, digitisation and stakeholder collaboration are key elements. The value provided in the international segment by DMCs, though, was found to be irrelevant.

This article contributes to filling a gap in the literature on rural tourism destination management from a holistic view of the destination understanding the business-to-business (B2B) relationship among stakeholders in rural tourism. This paper focuses on those elements that create value for local producers to sell the products through intermediaries and provides a framework for understanding the factors involved in value creation in rural tourism intermediation, which is applicable to further empirical studies and provides interesting managerial implications.

The purpose of this study is to examine the benefits and challenges of using logistics service providers (LSPs) for small and medium-sized enterprises (SMEs) and large corporations.

This study uses the focus group interview method with eight LSP companies of varying sizes in the UAE. The aim is to obtain detailed insights into the advantages and obstacles associated with using LSPs.

The interview results reveal differences in the services and payment options offered by large and small-medium-sized LSPs, leading to varying impacts on SMEs. Additionally, both large and small-medium-sized LSPs encounter challenges in delivering services to SMEs, including sustaining logistic excellence in a competitive market, meeting the high customer expectations from SMEs and large enterprises, difficulty in recruiting skilled and competent employees and high costs of business technology.

This study is limited to the UAE context and the perspectives of eight LSP companies. The findings may not be generalizable to other regions or industries.

The findings of this research provide valuable insights for SMEs and large corporations considering the use of LSPs. Understanding the benefits and challenges associated with outsourcing logistics services can assist in making informed decisions and developing effective collaborations with LSPs.

This research contributes to the existing literature by specifically examining the benefits and challenges of using LSPs for SMEs and large corporations. This study provides a comprehensive analysis of the factors that influence logistics outsourcing decisions and highlights the unique challenges faced by LSPs and SMEs in the context of the UAE.

Introduction: The ethical implications of deoxyribonucleic acid (DNA) profiling or DNA fingerprinting or forensic genetics in criminal investigations have gained significant attention worldwide. In India, DNA profiling in criminal investigations has increased over the years. However, the ethical considerations of DNA profiling in India have yet to be examined adequately.

Purpose: The study aimed to examine the ethical considerations of DNA profiling in India and compare them with international guidelines. By examining the ethical considerations of DNA profiling in India, this study seeks to contribute to the ongoing discourse on the responsible use of DNA profiling in forensic investigations.

Methodology: The study used a qualitative research design, and data were collected by reviewing relevant literature and laws.

Findings: The findings indicate that the Indian legal framework has gaps in addressing the ethical considerations raised by international guidelines, such as the admissibility of DNA evidence in court, oversight of DNA laboratories, safeguards against discrimination, and privacy and confidentiality protections.

The comparative analysis highlights the need for strengthening the legal framework in India, adopting best practices from international guidelines, and incorporating safeguards to protect against discrimination and ensure the privacy and confidentiality of individuals. By adopting these recommendations, India can ensure that DNA profiling is conducted ethically and responsibly, promoting public trust in the criminal justice system and upholding the rights of all individuals.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.