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This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

With the rapid development of railway-intelligent video technology, scene understanding is becoming more and more important. Semantic segmentation is a major part of scene understanding. There is an urgent need for an algorithm with high accuracy and real-time to meet the current railway requirements for railway identification. In response to this demand, this paper aims to explore a variety of models, accurately locate and segment important railway signs based on the improved SegNeXt algorithm, supplement the railway safety protection system and improve the intelligent level of railway safety protection.

This paper studies the performance of existing models on RailSem19 and explores the defects of each model through performance so as to further explore an algorithm model dedicated to railway semantic segmentation. In this paper, the authors explore the optimal solution of SegNeXt model for railway scenes and achieve the purpose of this paper by improving the encoder and decoder structure.

This paper proposes an improved SegNeXt algorithm: first, it explores the performance of various models on railways, studies the problems of semantic segmentation on railways and then analyzes the specific problems. On the basis of retaining the original excellent MSCAN encoder of SegNeXt, multiscale information fusion is used to further extract detailed features such as multihead attention and mask, solving the problem of inaccurate segmentation of current objects by the original SegNeXt algorithm. The improved algorithm is of great significance for the segmentation and recognition of railway signs.

The model constructed in this paper has advantages in the feature segmentation of distant small objects, but it still has the problem of segmentation fracture for the railway, which is not completely segmented. In addition, in the throat area, due to the complexity of the railway, the segmentation results are not accurate.

The identification and segmentation of railway signs based on the improved SegNeXt algorithm in this paper is of great significance for the understanding of existing railway scenes, which can greatly improve the classification and recognition ability of railway small object features and can greatly improve the degree of railway security.

This article introduces an enhanced version of the SegNeXt algorithm, which aims to improve the accuracy of semantic segmentation on railways. The study begins by investigating the performance of different models in railway scenarios and identifying the challenges associated with semantic segmentation on this particular domain. To address these challenges, the proposed approach builds upon the strong foundation of the original SegNeXt algorithm, leveraging techniques such as multi-scale information fusion, multi-head attention, and masking to extract finer details and enhance feature representation. By doing so, the improved algorithm effectively resolves the issue of inaccurate object segmentation encountered in the original SegNeXt algorithm. This advancement holds significant importance for the accurate recognition and segmentation of railway signage.

This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this paper is to analyse the metaverse platform in a social context to better understand the future of this tool in tourism cities and how this can help to improve the well-being of residents in both digital and physical scenarios.

In this paper, the current and probable developments in the metaverse, and its use in tourism cities and companies have been investigated. Moreover, this study develops, collects and examines the main metaverse definitions by expert authors and organizations as a methodology to ensure the transparency and credibility of the metaverse analysis.

Findings suggest that the fusion of the metaverse and tourism cities must create residents’ services and experiences in the new MetaTourPolis to help interact and connect citizens with the city’s institutions and companies, as well as make tourism cities more attractive, innovative, environmentally friendly and healthier places to live. Metaverse will bring new changes for residents and tourists, in fact, this virtual platform is already changing and improving the residents’ quality of life and people with disabilities in tourism cities. For instance, the metaverse platform has been implemented in Seoul, Santa Monica and Dubai MetaTourPolis to interact with their residents, including people with disabilities, to resolve bureaucratic and administrative problems, avoiding this group and the rest of the residents travelling by bus or car to the city’s institutions. In addition, several metaverse applications based on softbot tutors or metaverse virtual social centres have been developed to improve blind and impaired people, and elderly people’ quality of life, respectively.

A new concept called “MetaTourPolis” has been included to stage the relationship between tourism cities and the metaverse platform, where the fusion of metaverse and the new tourism polis of the 21st century will be at the service of citizens, tourists and companies, to create more sustainable, efficient, quantitative and environmental tourism cities.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

Local cultures under the nation-states have evolved and produced new concepts. Migrations and cultural interactions have also influenced the language. Thus, different conceptualisations of migration have developed in the “Malay World.” Language is not only a product of ecological and geographical environment, but it is also a result of human mobilisations and cultural intercourses. The differences caused by various Malay World conceptualisations are also related to different interactions, histories and social realities. In the “Malay World,” Bahasa Malaysia and Bahasa Indonesia are threatened by English hegemony. At this juncture, demonstrating these interrelationships helps one to understand local cultural intercourses between culture and social reality.

This paper implements the critical discourse analysis, notably, the dialectical–relational approach to different conceptualisations in the “Malay World.” Thus, it aims to discuss the reasons behind the usage of migration-related terms in Indonesia and Malaysia. How migration terms mainly shape social forms, process and circulate in the social world is argued in this paper. The authors target to investigate the local meaning sets for migration concepts and explore self-reflectivity in and critique of the usage of established images between Bahasa Indonesia and Malay concepts, using a Critical Discourse Analysis framework. The discussion section attempts to analyse and explain dialectical relations between social norms and social elements.

Trade diasporas, culture and identities of immigrants, cultural richness and multi-ethnicity have continuously diversified the conceptualisations. These concepts also indicate cultural dominations and exclusions. Thus, the migration dynamics in Southeast Asia might be understood by scrutinising these boundaries and patterns. This study showed that migration conceptualisations in the Malay world are substantially locally determined. Local wisdom and cultural codifications prevail in the migration context. A deeper understanding of the local context is essential in evaluating the international refugee protection mechanisms in these countries as neither Indonesia nor Malaysia are parties to the Geneva Convention Relating to the Status of Refugees.

Definitions of various types of migration are referred to in multiple terminologies throughout the Nusantara. While in Bahasa Melayu, migrant refers to pendatang, nomadic refers to nomad; refugee refers to pelarian, to migrate refers to berhijrah; in Bahasa Indonesia, migrant refers to migran, nomadic or nomad refers pengembara, refugee refers to pengungsi and migrate, or migration refers migrasi. At the same time, in the Nusantara, many communities define themselves according to their geographical features, such as Orang Sungai, Orang Laut or Orang Kuala. In this context, defining leads to a significant impact on shaping the discourses and approaches.

Definitions determine the borders, flexibilities, plausibility and flows of terms. Critical Discourse Analysis especially placed emphasis on languages and their impacts on social reality. According to Critical Discourse Analysis, terms and their genealogies, transformations and limits are vital in order to comprehend social facts and truth. To understand the change and continuity of migration flow in Southeast Asia, scrutinising the migration-related terms is crucial. This study aims to examine those main terms, their connection with other words, and their transformation in the Malay world sociologically. This as such will shed more light on the social changes regarding genealogical relationships and their usage in daily life within the Critical Discourse Analysis.

This study aims to fulfil the gap between authentic concepts of migration literature in Nusantara. Although Bahasa Melayu and Bahasa Indonesia have a strong connection and interaction, migration definitions in these languages are separated from each other. Cultural and social elements greatly influence migration-connected terms in Bahasa Melayu and Bahasa Indonesia. Without this understanding and the absence of local context, any assessment of their refugee protection systems may be incomplete. This study deals with different cultural nuances and refinements and their migration conceptualisations and histories are based on the ground of their social realities.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

In recent years, railway systems worldwide have faced challenges such as the modernization of engineering projects, efficient management of intelligent digital railway equipment, rapid growth in passenger and freight transport demands, customized transport services and ubiquitous transport safety. The transformation toward intelligent digital transformation in railways has emerged as an effective response to the formidable challenges confronting the railway industry, thereby becoming an inevitable global trend in railway development.

This paper, therefore, conducts a comprehensive analysis of the current state of global railway intelligent digital transformation, focusing on the characteristics and applications of intelligent digital transformation technology. It summarizes and analyzes relevant technologies and applicable scenarios in the realm of railway intelligent digital transformation, theoretically elucidating the development process of global railway intelligent digital transformation and, in practice, providing guidance and empirical examples for railway intelligence and digital transformation.

Digital and intelligent technologies follow a wave-like pattern of continuous iterative evolution, progressing from the early stages, to a period of increasing attention and popularity, then to a phase of declining interest, followed by a resurgence and ultimately reaching a mature stage.

The results offer reference and guidance to fully leverage the opportunities presented by the latest wave of the digitalization revolution, accelerate the overall upgrade of the railway industry and promote global collaborative development in railway intelligent digital transformation.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.