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The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

National policies suggest that service users and carers should be involved in health care planning and delivery. Initiatives to involve service users and carers within the education of mental health professionals have been reported. However, there has been no initiative to involve such individuals in the selection of clinical psychologists. This study examines the experiences of service users, carers and members of the Doctorate of Clinical Psychology programme in the implementation of a new interview task for the selection of trainee clinical psychologists at the University of Surrey. This new initiative involves service users, carers and staff members working collaboratively to assess candidates in a discussion based task. The study employed two focus groups, one pre‐selection and one post‐selection, and used interpretative phenomenological analysis (IPA) to evaluate participants' expectations and experiences of the task. The findings suggest that there was genuine collaboration between service users, carers and programme team members that was deeply engrained in the programme ethos and was a step forward in normalising and empowering service users and their carers. Interviewers felt that this task helped select a different calibre of applicants who had an awareness of the perspective of service users and carers and were able to communicate effectively. Interviewers viewed applicants who were able to disclose and take ownership of their views favourably. The introduction of a successful new interview task at Surrey has set a marker for future collaboration with service users and carers in selection, which will have implications for other doctorate programmes in clinical psychology and the broader health care training community.

This paper aims to propose some suggestions for libraries and other digital reading service institutions to improve the utilization rate of e-books, based on the theoretical and empirical analysis of the perception behaviour characteristics of e-book selection under the allocation of limited cognitive resources.

From the perspective of key perception points, this paper studies the key perception points of selecting e-books through the experimental method and explores the influence of subject factors (users’ characteristics, users’ needs) on users’ e-book perception behaviour.

College students have selective attention in the process of selecting e-books. They will choose some important contents of e-books, such as title, book introduction, author, catalogue, reader comments, others’ recommendations, read leaderboard, to perceive and there is an obvious difference in perception intensity. Different personal traits and reading needs have a great influence on users’ perception points. Libraries and other digital reading service institutions should provide promotion information based on key perception points of e-books, develop personalized e-book service and promotion and optimize the expression of key perception points of e-books.

This paper presents a valuable study attempting to introduce cognitive resource theory into the field of digital reading service, which proves that users also have limited cognitive resource allocation in the process of selecting digital books.

The major challenges in the modern-day wireless communication systems are increased co-channel interference owing to large number of users and the increased energy consumption owing to high circuit and/or hardware power consumption. Hence, the purpose of this paper is to present a practical approach involving linear precoding, channel estimation, user selection (US) and transmit antenna selection (AS) for enhanced reliability in multiuser multiple-input multiple output (MU-MIMO) system.

The proposed technique considers systematic and optimum deployment of users and transmits antennas for each selected user which enhances the sum rate or the system capacity. The comparison of algorithms, namely, norm-based and capacity-based US is presented with its implementation with precoding techniques, namely, block-diagonalization (BD) and zero-forcing with combining (ZFC) which is used to minimize co-channel interference. In this paper, a power consumption model is proposed for energy efficiency calculation in MU-MIMO system. Also, post analysis, the variant of US and AS algorithms optimizing the performance of BD and ZFC precoding technique is proposed.

It is seen that the proposed MU-MIMO system with norm-based US and norm-based AS improves over existing US-based systems by 43% in terms of sum rate and 19% in terms of energy efficiency for 100 users.

It is seen that the proposed MU-MIMO system with norm-based US and norm-based AS improves over existing US-based systems by 43% in terms of sum rate and 19% in terms of energy efficiency for 100 users.

The purpose of this paper is to examine changes in relevance assessments, specifically the selection of relevance criteria by subjects as they move through the information search process.

The paper examines the relevance criteria choices of 39 subjects in relation to search stage. Subjects were assigned a specific search task in a controlled test. Statistics were collected and analyzed using descriptive statistics and the chi‐square goodness‐of‐fit tests.

The statistically significant findings identified a number of commonly reported relevance criteria, which varied over an information search process for relevant and partially relevant judgments. These results provide statistical confirmations of previous studies, and extend these findings identifying specific criteria for both relevant and partially relevant judgments.

The study only examines a short duration search process and since the convenience sample of subjects were from similar backgrounds and were assigned similar tasks, the study did not explicitly examine the impact of contextual factors such as user experience, background or task in relation to relevance criteria choices.

The paper has implications for the development of search systems which are adaptive and recognize the cognitive changes which occur during the information search process. Examining and identifying relevance criteria beyond topicality and the importance of those criteria to a user can help in the generation of better search queries.

The paper adds more rigorous statistical analysis to the study of relevance criteria and the information search process.

Additive manufacturing (AM) can reduce the process supply chain and encourage manufacturing innovation in remote or austere environments by producing an array of replacement/spare parts from a single raw material source. The wide variety of AM technologies, materials, and potential use cases necessitates decision support that addresses the diverse considerations of deployable manufacturing. The paper aims to discuss these issues.

Semi-structured interviews with potential users are conducted in order to establish a general deployable AM framework. This framework then forms the basis for a decision support tool to help users determine appropriate machines and materials for their desired deployable context.

User constraints are separated into process, machine, part, material, environmental, and logistical categories to form a deployable AM framework. These inform a “tiered funnel” selection tool, where each stage requires increased user knowledge of AM and the deployable context. The tool can help users narrow a database of candidate machines and materials to those appropriate for their deployable context.

Future work will focus on expanding the environments covered by the decision support tool and expanding the user needs pool to incorporate private sector users and users less familiar with AM processes.

The framework in this paper can influence the growth of existing deployable manufacturing endeavors (e.g. Rapid Equipping Force Expeditionary Lab – Mobile, Army’s Mobile Parts Hospital, etc.) and considerations for future deployable AM systems.

This work represents novel research to develop both a framework for deployable AM and a user-driven decision support tool to select a process and material for the deployable context.

This paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.

Cloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.

The obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.

Although the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.

The outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.

Although service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.

This article reports on the creation of a prototype, Web‐based, expert system utility that helps end‐users better navigate the range of library databases available at the University of Illinois at Urbana‐Champaign (UIUC). Both librarian‐assigned database descriptors and terms drawn from the controlled vocabularies of the databases themselves are used to thoroughly characterize resources. End‐users then utilize keyword searches and/or menu selections to identify resources most relevant to their information needs. In addition to reporting on the UIUC prototype and the work done to create it, the concerns that gave rise to the project are discussed. Previous work and research elsewhere are summarized, and the more common approaches currently in place in academic libraries today are noted. Plans for testing the UIUC prototype with librarians and end‐users, for evaluating the results of those tests, and for iteratively refining the tool based on those evaluations are then briefly described.

Many types of services involve a sequence in which customers choose a service provider followed by selection of service specifications, that is selecting when and how the service will be performed. Specifications selection can be dominated by the provider, the customer or the customer and provider can jointly select specifications. Customer satisfaction results if specifications selection meets customer expectations of the provider‐customer role. Specifications selection unfolds as a process where information is exchanged between the customer and provider and the provider can be more or less customer oriented. Effective information exchange and a strong customer orientation by the provider contribute to customer satisfaction. Customers make attributions of provider or customer responsibility for specifications selection depending on the type of specifications selection that occurs and provider provision of specifications information. Customers who attribute specification selection to their decisions assume responsibility for the specifications selected.

This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.

This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.

The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.

This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.