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This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set.

A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship.

Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness.

This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.

Goal setting is a crucial aspect of client-centered practice in occupational therapy (OT) for mental health conditions. However, it remains to be seen how goal-setting has been delivered in mental health, particularly the OT process. The purpose of this scoping review was to explore the nature and extent of goal setting delivered in mental health and informed OT practice.

The authors followed the guidelines of Arksey and O’Malley (2005) and searched three databases using key search terms: “mental disorder,” “goal setting,” and “occupational therapy” and their synonyms.

After excluding duplicate records, the authors initially screened 883 records and resulted in 20 records in total after the screening process. Most of the identified articles used goal-setting delivered by both a health professional and a client (n = 14), and focused on people with schizophrenia or schizoaffective disorder (n = 13), but three interventions were delivered by occupational therapists. Further research needs on goal-setting in mental health OT, exploring the reliability and validity of different goal-setting strategies and investigating the effectiveness of goal-setting for promoting behavior change and client engagement across various mental health conditions and settings.

The scoping review has some limitations, such as not investigating the validity and reliability of goal-setting strategies identified, and excluding conference papers and non-English articles.

This scoping review presents a mapping of how goal-setting has been delivered in mental health and informed OT practice. The findings suggest limited research in OT and highlight the need for more studies to address the evidence gap in individualized client-centered OT.

This study aims to examine the effect of the different forms of attitudes on the behavioural intentions to adopt solar energy technologies (SETs) in Uganda. Although commonly examined, the effect of attitudes on people’s behavioural intentions to adopt SETs ought to be more distinctively examined to have a clear picture of how each of the identified sets of attitudes influences the adoption of SETs.

Based on a sample of 360 households from three urban districts in Uganda sampled using a multi-stage sampling technique, data were collected using a self-administrated structured questionnaire. The data were then analysed using partial least square–structural equation model with SmartPLS 3.0 software.

The study establishes that more specific attitudes affect behavioural intentions to adopt SETs than general pro-technology attitudes. Results reveal that both pro-environment and application-specific attitudes matter for behaviour intentions to adopt SETs amongst households. However, the general pro-technology attitudes are not significantly associated with behavioural intentions to adopt SETs.

The results are important for producers and promoters of solar technology to craft appropriate promotion campaigns intended to increase the acceptance and usage of SETs. This means focussing on creating positive attitudes specific to particular applications and popularising specific uses of solar technologies.

The study provides an alternative approach to the general representation of the attitudes–intentions relationships by examining the differences in the attitudes developed towards the different aspects of these technologies as a substantial source of variations in adoption behaviour, which is rarely addressed.

This study aims at assessing item fairness in students' evaluation of teaching based on students' academic college using measurement invariance analysis (MI).

The sample of this study consists of 17,270 undergraduate students from 12 different academic colleges. SET survey consists of 20 Likert-type items distributed to four factors: planning, instruction, management and assessment was used to collect the data. The Lavaan R package with confirmatory factor analysis (CFA) was used to evaluate measurement invariance (MI). Four models of CFA were investigated and assessed: the configural model, the metric model, the scalar model and the residual invariance model. ANOVA was used to test the differences in SET according to academic colleges.

MI analysis showed that the four levels of MI models are supported. ANOVA test showed that means of SET total scores are statistically different according to students' academic colleges. College of “Education” has the highest SET mean (88.64 out of 100), and all the differences between the College of Education’s SET mean and other colleges' SET means are statistically significant.

The study recommends that higher education institutions test the MI of SET according to academic colleges and then use colleges with the highest SET at the university level as internal benchmarking to develop and enhance their teaching practices.

This study is probably the only study that tested MI according to students' colleges before testing the differences between colleges in SET. If MI is not supported, then the comparisons between academic colleges are not applicable.

This paper aims to provide empirical evidence on formal stakeholder participation (or “lobbying”) in the early phase of the International Sustainability Standards Board’s (ISSB’s) standard-setting.

Drawing on a rational-choice framework, this paper conducts a content analysis of comment letters (CLs) submitted to the ISSB in response to its first two exposure drafts (published in 2022) to investigate stakeholder participation across different groups and jurisdictional origins. The analyses examine participation in terms of frequency (measured using the number of participating stakeholders) and intensity (measured using the length of CLs).

Preparers and users of sustainability reports emerge as the largest participating stakeholder groups, while the accounting/sustainability profession participates with high average intensity. Surprisingly, preparers do not outweigh users in terms of participation frequency and intensity; and large preparers outweigh smaller ones in terms of participation intensity but not participation frequency. Internationally, stakeholders from countries with a private financial accounting standard-setting system participate more frequently and intensively than others. In addition, country-level economic wealth and sustainability performance are positively associated with more participating stakeholders.

This study is of interest for organizations and stakeholders involved in or affected by standard-setting in the field of sustainability reporting. The finding of limited participation by investors and from developing countries suggests the ISSB take actions to enhance the voice of those stakeholders.

The imbalances in stakeholder participation that were found pose potential threats to an important aspect of the input legitimacy of the ISSB’s standard-setting process.

To the best of the authors’ knowledge, this paper is the first to explore stakeholder participation by means of CLs with the ISSB in terms of frequency and intensity.

This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.