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Closing water loops in industry and the reuse of wastewater for irrigation purposes should not only be practiced in industrialised countries but it is extremely important in arid or semi‐arid regions. This paper shows, on the basis of three case studies, the possibilities of environmental protection on the one hand, and the achievement of financial benefits on the other hand by introducing membrane technology. The case studies are about three different fields, in order to describe that the application of membrane technology leads to an effective water treatment, which makes it possible – depending on the application – to recover valuable materials or to provide suitable service water. In the case of combining biological wastewater treatment and membrane technology the potential of reusing municipal wastewater for irrigation purposes will fit the increasing demands in this sector.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

The contemporary business environment is characterised by an increasing reliance on artificial intelligence, automation, optimisation, efficient communication and data-driven decision making. Based on the limited academic literature that examines the managerial perspective on enterprise chatbots, the paper aims to explore organisational needs and expectations for enterprise chatbots from a managerial perspective, assesses the relationship between managerial knowledge and managerial opinion regarding enterprise chatbots, and delivers a framework for integrating chatbots into the digital workforce.

The paper presents a quantitative design. An online, self-administered survey yielded 111 valid responses from managers in service and manufacturing organisations based on convenience and snowball sampling strategies. Given the nature of the data and the research questions, the research was conducted using principal component analysis, parallel analysis, correlation, internal consistency and difference in means tests.

This research explores the managerial perspective on enterprise chatbots from multiple perspectives (i.e., adoption, suitability, development requirements, benefits, barriers, performance and implications), presents a heat map of the average level of chatbot need across industries and business units, highlights the urgent need for education and training initiatives targeted at decision makers, and provides a strategic framework for successful chatbot implementation.

This study equips managers and practitioners dealing with enterprise chatbots with knowledge to effectively leverage the expected benefits of investing in this technology for their organisations. It offers direction for developers in designing chatbots that align with organisational expectations, capabilities and skills.

Insights for managers, researchers and chatbot developers are provided. The work complements the few academic studies that examine enterprise chatbots from a managerial perspective and enriches related commercial studies with more rigourous statistical analysis. The paper contributes to the ongoing discourse on decision-making in the context of technology development, integration and education.

This paper aims to contribute to the discussion on integrating humans and technology in customer service within the framework of Society 5.0, which emphasizes the growing role of artificial intelligence (AI). It examines how effectively new generative AI-based chatbots can handle customer emotions and explores their impact on determining the point at which a customer–machine interaction should be transferred to a human agent to prevent customer disengagement, referred to as the Switch Point (SP).

To evaluate the capabilities of new generative AI-based chatbots in managing emotions, ChatGPT-3.5, Gemini and Copilot are tested using the Trait Emotional Intelligence Questionnaire Short-Form (TEIQue-SF). A reference framework is developed to illustrate the shift in the Switch Point (SP).

Using the four-intelligence framework (mechanical, analytical, intuitive and empathetic), this study demonstrates that, despite advancements in AI’s ability to address emotions in customer service, even the most advanced chatbots—such as ChatGPT, Gemini and Copilot—still fall short of replicating the empathetic capabilities of human intelligence (HI). The concept of artificial emotional awareness (AEA) is introduced to characterize the intuitive intelligence of new generative AI chatbots in understanding customer emotions and triggering the SP. A complementary rather than replacement perspective of HI and AI is proposed, highlighting the impact of generative AI on the SP.

This study is exploratory in nature and requires further theoretical development and empirical validation.

The study has only an exploratory character with respect to the possible real impact of the introduction of the new generative AI-based chatbots on collaborative approaches to the integration of humans and technology in Society 5.0.

Customer Relationship Management managers can use the proposed framework as a guide to adopt a dynamic approach to HI–AI collaboration in AI-driven customer service.

Travel websites allow tourists to share their thoughts, beliefs and experiences regarding various travel destinations. In this paper, the researchers demonstrated an approach for destination marketing organisations to explore online tourist-generated content and understand tourists' perceptions of the destination image (DI). Specifically, the researchers initiated an investigation examining how the destination image of Macau changed during the period of 2014–2018 based on user-generated content on travel websites.

Web crawlers developed by Python were employed to collect tourists' reviews from both Ctrip and TripAdvisor regarding the theme of “Macau attraction”. A total of 51,191 reviews (41,352 from Ctrip and 9,839 from TripAdvisor) were collected and analysed using the text-mining technique.

The results reveal that the frequency of casino-related words decreased in reviews by both international and mainland Chinese tourists. Additionally, international and mainland Chinese tourists perceive the DI of Macau differently. Mainland Chinese tourists are more sensitive to new attractions, while international tourists are not. The study also shows that there are differences between the government-projected DI and the tourist-perceived DI. Only the “City of Culture” and “A World Centre of Tourism and Leisure” have built recognition with tourists.

Given the easy accessibility of online information from various sources, it is important for destination marketing organisations to analyse and monitor different DI perspectives and adjust their branding strategies for greater effectiveness. This study uncovered the online DI of Macau by using text mining and content analysis of two of the largest travel websites. By analysing and comparing the differences and relationships among the frequently used words of tourist-generated content on these websites, the researchers revealed some interesting findings with important marketing implications.

The purpose of this paper is to describe a smart city initiative presenting a mobile tourist guide developed for Natal, Brazil.

This study has employed an exploratory case study approach to gain more knowledge about a smart city initiative and a mobile tourist guide in Brazil. The city of Natal was selected for this case study since it was one of the host cities during the FIFA World Cup 2014. The collected data for this research came from government (e.g. Natal Smart City plan), academic sources (e.g. Metropole Digital Institute (IMD)), and tourists’ information from the mobile tourist guide application.

The smart city initiative of Natal, and the mobile tourist guide (named Find Natal) responsible for collecting, processing, sharing, storing and analysing the tourist behaviour were detailed in the paper. The Smart City Consortium in Natal is developing an interoperable and distributed infrastructure that is advancing the state-of-the-art in information and communication technologies (ICT) for planning and managing smart cities. The IMD implemented an application, which aims to enhance the traveller’s experience through software programs designed to leverage the infrastructure mechanisms behind the city. The data gathered by the application was analysed to show how it was used during the 2014 FIFA World Cup.

The results show a developed ICT initiative in a Latin American country. This study offers a starting point for destinations willing to implement and deploy a smart city initiative.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.