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The purpose of this article is to provide an overview of agent-based modeling as an alternative method for public administration research. It is focused on encouraging public administration scholars to come to better understanding of the method.

This article performed a comprehensive review of methodological issues relative to agent-based modeling.

After reviewing the current research themes in public administration and the methodological nature of agent-based modeling, we found that agent-based modeling can help researchers to advance theories by means of sophisticated thought experiment which is not possible by formal modeling and verbal reasoning. We also pointed out that agent-based modeling does not substitute empirical research, but can add much value through being part of a mixed-method and multidisciplinary research.

We suggested that interested researchers may need to take a strategic approach in developing and describing a pertinent model and reporting its results.

Agent-based modeling has rarely been used in public administration research. The article provides an introductory overview for researchers not familiar with ABM and suggests to the academic community future venues that would unfold from agent-based modeling.

The purpose of this paper is to examine how robotics program developers perceived the role of emulation of human ethics when programming robots for use in educational settings. A purposive sampling of online robotics program developer professional sites which focused on the role of emulation of human ethics used when programming robots for use in educational settings was included in the study. Content related to robotics program developers’ perceptions on educational uses of robots and ethics were analyzed.

The design for this study was a qualitative summative content analysis. The researchers analyzed keywords related to a phenomenon. The phenomenon was the emulation of human ethics programmed in robots. Articles selected to be analyzed in this study were published by robotics program developers who focused on robots and ethics in the education. All articles analyzed in this study were posted online, and the public has complete access to the studies.

Robotics program developers viewed the importance of situational human ethics interpretations and implementations. To facilitate flexibility, robotics program developers programmed robots to search computer-based ethics related research, frameworks and case studies. Robotics program developers acknowledged the importance of human ethics, but they felt more flexibility was needed in the role of how classroom human ethical models were created, developed and used. Some robotic program developers expressed questions and concerns about the implementations of flexible robot ethical accountability levels and behaviors in the educational setting. Robotics program developers argued that educational robots were not designed or programmed to emulate human ethics.

One limitation of the study was 32 online, public articles written by robotics program designers analyzed through qualitative content analysis to find themes and patterns. In qualitative content analysis studies, findings may not be as generalizable as in quantitative studies. Another limitation was only a limited number of articles written by robotics programs existed which addressed robotics and emulation of human ethics in the educational setting.

The significance of this study is the need for a renewed global initiative in education to promote debates, research and on-going collaboration with scientific leaders on ethics and programming robots. The implication for education leaders is to provide ongoing professional development on the role of ethics in education and to create best practices for using robots in education to promote increased student learning and enhance the teaching process.

The implications of this study are global. All cultures will be affected by the robotics’ shift in how students are taught ethical decision making in the educational setting. Robotics program developers will create computational educational moral models which will replace archetypal educational ethics frameworks. Because robotics program developers do not classify robots as human, educators, parents and communities will continue to question the use of robots in educational settings, and they will challenge robotics ethical dilemmas, moral standards and computational findings. The examination of robotics program developers’ perspectives through different lens may help close the gap and establish a new understanding among all stakeholders.

Four university doctoral faculty members conducted this content analysis study. After discussions on robotics and educational ethics, the researchers discovered a gap in the literature on the use of robots in the educational setting and the emulation of human ethics in robots. Therefore, to explore the implications for educators, the researchers formed a group to research the topic to learn more about the topic. No personal gains resulted from the study. All research was original. All cultures will be affected by the robotics’ shift in how students are taught ethical decision making in the educational setting. Robotics program developers will create computational educational moral models which will replace archetypal educational ethics frameworks. Because robotics program developers do not classify robots as human, educators, parents and communities will continue to question the use of robots in educational settings, and they will challenge robotics ethical dilemmas, moral standards, and computational findings. The examination of robotics program developers’ perspectives through different lens may help close the gap and establish a new understanding among all stakeholders.

This study investigates a novel educational strategy in science, technology, engineering and mathematics (STEM) teaching that integrates the engineering design process (EDP) as a framework. The strategy aims to help teachers explain STEM concepts in a simplified way. We employed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to enable a systematic review that evaluated the effectiveness of this approach in improving both teaching and learning in STEM subjects.

In order to fulfill the objectives of the review, key data were extracted from each of the 400 articles that were reviewed from three databases: Scopus, ProQuest Central and EBSCO. Two types of analysis were conducted, namely descriptive analysis and literature classification.

This systematic review analyzed 44 articles on the EDP, focusing on 18 detailed studies mainly from ProQuest, SCOPUS and EBSCO. It revealed a limited focus on gender’s impact on EDP and a trend toward interdisciplinary use and integrated research approaches. The study underscores the need for further exploration of demographic influences and preparation programs in EDP across various disciplines, aiming to inform future research and educational policies.

The study’s value lies in its comprehensive assessment of engineering design (ED) research over the past decade, serving as a key reference point. It highlights progress in the field, consolidates findings and provides insights into the field’s evolution, guiding future research directions in ED.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

This paper aims to examine the role of Blockchain in the accounting and auditing literature and profession. Specifically, the paper investigates auditors' perceptions about the role of blockchain in accounting and auditing and the perceived potential benefits and challenges of blockchain-based accounting systems in Egypt. Moreover, what are the capabilities required for successfully implementing blockchain-based accounting systems?

A mixed-method approach was adopted to achieve the research objectives. The qualitative study included 11 in-depth interviews with external auditors, and the results of the interviews and the literature review helped develop a survey collected from 58 auditors.

The findings revealed low-to-moderate awareness of Blockchain-based accounting systems. Also, there were significant differences between auditors from large audit firms and small-and-medium audit firms regarding the benefits and challenges associated with Blockchain-based accounting systems.

The results provide valuable insights for practitioners, researchers and policymakers.

Understanding blockchain-based accounting systems and the benefits and challenges associated with their application is crucial for developing effective strategies and frameworks to overcome barriers and realize the transformative potential of blockchain in the accounting and audit market.

The purpose of this paper is to analyse the relation between occupational segregation and the gender wage differences using data on three-digit occupational level of classification. The authors examine whether a statistically significant relation between the share of men in employment and the size of the unexplained part of the gender wage gap exists.

Traditional Oaxaca (1973) – Blinder (1973) decomposition is performed to examine the differences in the gender wage gaps among minor occupational groups. Two types of reweighted decomposition – based on the parametric estimate of the propensity score and non-parametric proposition presented by Barsky et al. (2002) – are used as the robustness check. The analysis is based on individual data available from Poland.

The results indicate no strong relation between occupational segregation and the size of unexplained differences in wages. The unexplained wage differences are the smallest in strongly female-dominated and mixed occupations; the highest are observed in male-dominated occupations. However, they are probably to a large extent the result of other, difficult to include in the econometric model, factors rather than the effects of wage discrimination: differences in the psychophysical conditions of men and women, cultural background, tradition or habits. The failure to take them into account may result in over-interpreting the unexplained parts as gender discrimination.

The highest accuracy of the estimated gender wage gap is obtained for the occupational groups with a similar proportion of men and women in employment. In other male- or female-dominated groups, the size of the estimated gender wage gaps depends on the estimation method used.

The results suggest that decreasing the degree of segregation of men and women in different occupations could reduce the wage differences between them, as the wage discrimination in gender balanced occupations is the smallest.

To the best of the authors’ knowledge, this study is one of the few conducted at such a disaggregated level of occupations, and one of few studies focused on Central and Eastern European countries and the first one for Poland.

Ranulph Glanville has argued that ambitions of strict control are misplaced in epistemic processes such as learning and designing. Among other reasons, he has presented quantitative arguments for this ethical position. As a part of these arguments, Glanville claimed that strict control even of modest systems transcends the computational limits of our planet. The purpose of this paper is to review the related discourse and to examine the soundness of this claim.

Related literature is reviewed and pertinent lines of reasoning are illustrated and critically examined using examples and straightforward language.

The claim that even modest epistemic processes transcend the computational means of our planet is challenged. The recommendation to assume out-of-control postures in epistemic processes, however, is maintained on ethical rather than on quantitative grounds.

The presented reasoning is limited in as far as it is ultimately based on an ethical standpoint.

This paper summarizes an important cybernetic discourse and dispels the notion therein that epistemic processes necessarily involve computational demands of astronomical proportions. Furthermore, this paper presents a rare discussion of Glanville’s Corollary of Ashby’s Law of Requisite Variety.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.

In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.

Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.

This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.