Collaborative Exploration Research Articles

Renovating Traditional Craft

This review article evaluates theories and on the entanglement of matter and meaning, elaborating and exploring the potential outcomes of craft in research processes. Exemplified through a collaborative exploration between me as a silversmith and programmers at the Mechatronic Innovation Lab (MIL) in Grimstad, Norway, I explore, through these theories, how renovation work served as a diffraction apparatus in exploring the relation between traditional craft and emerging mechatronic technologies in light of the concept of sustainability. Renovation work is, in this setting, understood as cutting, filing, grinding and polishing, often the final but nevertheless essential step in a production process. This type of surface work accentuates here how it is possible to not only see traditional crafts as carriers of intangible value linked to identity but also as a type of knowledge powerful of providing insights into the entanglement of matter and meaning. Through uniting different knowledge systems, like the subjective knowledge in the situated understanding of material, tools, setting and processes, and more objective knowledge, typically for observation, readings and purely cognitive activities, renovation work exposed, in this case, some of the limitations of automation and provided unexpected findings on the relationship between body and machine.

Multi-Robot Path Planning Algorithm for Collaborative Mapping under Communication Constraints

In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction. However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions. Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments. This paper introduces a path planning approach specifically designed for distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints. We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA). This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps. Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities. Field experiment results highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.

Collaborative Exploration of the Harmony of Science and Religion (Part 2)

Collaborative Exploration of the Harmony of Science and Religion (Part 2)

LECES: A Low-Bandwidth and Efficient Collaborative Exploration System With Distributed Multi-UAV

LECES: A Low-Bandwidth and Efficient Collaborative Exploration System With Distributed Multi-UAV

Faculty perceptions of the impact of professional development on online teaching practices

The COVID-19 pandemic prompted a global shift toward online education, underscoring the need for quality faculty professional development to support educators in designing and delivering effective online courses. This study explores the impact of professional development utilizing the Universal Design for Learning (UDL) framework on faculty’s pedagogical practices in online learning environments. The Online Teaching Fellows Program, a 14-week professional development series, engaged 20 graduate and undergraduate university faculty members from diverse disciplines (Engineering, Education, Law, Occupational Therapy, Nursing, Humanities, Social Science, Business, and Criminal Justice) in a collaborative exploration of UDL principles to create authentic and equitable learning experiences. Qualitative data collection included interviews, exit tickets, faculty professional development artifacts (CANVAS course sites), and researcher notes. Faculty were introduced to diverse teaching strategies, including effective online information presentation, collaboration facilitation, and online assessment enhancement. The study underscores the potential of UDL principles to promote equitable educational opportunities and create transformative spaces in higher education globally.

Designing activities and tools to support university students' creative and collaborative exploration of physical computing

Today’s digital world requires students to gain skills in collaborative problem solving and digital literacy. One approach is to teach people how to design computational artefacts that require both electronics and programming. Physical computing platforms offer an endless amount of possible opportunities for people to design and develop technological artefacts. However, many times students are overwhelmed when trying to learn both software and hardware simultaneously. The students struggle to be innovative and creative in their projects. Also, they focus on mastering the tool and following instructions for existing projects rather than being able to creatively explore the tool and understand the process of designing and developing new artefacts. For that reason, we aimed to answer the question: What type of tools and activities can be developed to support university students creative exploration of physical computing? Programming and electronics are fundamental design disciplines in today's digital world, and therefore they should be taught through design activities rather than limiting education to textbook readings and laboratory exercises. We introduce our process of designing activities combined with a supportive tool to ease these challenges. The activities and tools were developed iteratively in three phases with a series of workshops with 126 students and teachers. The tool consists of a set of paper cards that provide necessary details (hints) about the electronics and software and help provide structure for the students to conceptualise how their artefact interacts. We additionally, introduced a learning Jigsaw pattern (orchestration script) for the later intervention that enabled individual students in the groups to focus on design, hardware, or software. For evaluation, we used the Creativity Support Index (CSI), which is a psychometric survey designed to assess the support of the creative process. The instrument investigates collaboration, efforts worth the result, exploration, immersion, enjoyment, and expressiveness. The results between the phases showed improvement with the use of the refined versions of the cards and orchestration of the learning activity. This study has demonstrated that design activities can provide a more accessible approach for the introduction of physical computing to students from various majors. Moreover, learning physical computing through design activities allows the learner to develop computational and design thinking skills for collaboratively solving problems.

A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning.

State-of-the-art (SOTA) multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences. However, most of them still totally inherit the single-agent exploration-exploitation strategy. Naively inheriting this strategy from single-agent algorithms causes potential collaboration failures, in which the agents blindly follow mainstream behaviors and reject taking minority responsibility. We name this problem the responsibility diffusion (RD) as it shares similarities with the same-name social psychology effect. In this work, we start by theoretically analyzing the cause of this RD problem, which can be traced back to the exploration-exploitation dilemma of multiagent systems (especially large-scale multiagent systems). We address this RD problem by proposing a policy resonance (PR) approach which modifies the collaborative exploration strategy of agents by refactoring the joint agent policy while keeping individual policies approximately invariant. Next, we show that SOTA algorithms can equip this approach to promote the collaborative performance of agents in complex cooperative tasks. Experiments are performed in multiple test benchmark tasks to illustrate the effectiveness of this approach.

Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres

Security Operations Centres (SOCs) play a pivotal role in defending organisations against evolving cyber threats. They function as central hubs for detecting, analysing, and responding promptly to cyber incidents with the primary objective of ensuring the confidentiality, integrity, and availability of digital assets. However, they struggle against the growing problem of alert fatigue, where the sheer volume of alerts overwhelms SOC analysts and raises the risk of overlooking critical threats. In recent times, there has been a growing call for human-AI teaming, wherein humans and AI collaborate with each other, leveraging their complementary strengths and compensating for their weaknesses. The rapid advances in AI and the growing integration of AI-enabled tools and technologies within SOCs give rise to a compelling argument for the implementation of human-AI teaming within the SOC environment. Therefore, in this article, we present our vision for human-AI teaming to address the problem of alert fatigue in the SOC. We propose the 𝒜 2 𝒞 Framework, which enables flexible and dynamic decision making by allowing seamless transitions between automated, augmented, and collaborative modes of operation. Our framework allows AI-powered automation for routine alerts, AI-driven augmentation for expedited expert decision making, and collaborative exploration for tackling complex, novel threats. By implementing and operationalising 𝒜 2 𝒞, SOCs can significantly reduce alert fatigue while empowering analysts to efficiently and effectively respond to security incidents.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Artificial intelligence (AI) within manufacturing: An investigative exploration for opportunities, challenges, future directions

<p>Artificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.<strong></strong></p>

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

AbstractThis review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

Teasing in Outpatient Clinical Interaction in China: Managing Epistemic and Deontic Authorities

ABSTRACTIntroductionIn response to evolving patient‐centred care initiatives, this study investigates teasing in Chinese outpatient interactions. By exploring how teasing impacts knowledge exchange and decision‐making processes, the research underscores its utility in enhancing patient engagement and communication efficacy, aligning with China's healthcare reform objectives.MethodsUtilizing conversation analysis, the study meticulously examines a series of outpatient interactions, identifying and analyzing instances of teasing. Through a detailed investigation of the sequential organization, design features, and interactional tasks accomplished by teasing, the research unravels the complex mechanisms through which teasing operates within clinical settings.ResultsFindings reveal that teasing is a sequential accomplishment that is intertwined with institutional contingencies and embedded in overarching sequential projects of clinics. Teases often arise from subtle cues that may lead to delicate moments and are crafted with a blend of playfulness and seriousness, serving to bridge the gap between patient experiences and medical expertise. This study highlights how teasing facilitates the exchange of knowledge and influences collaborative decision‐making, marking a departure from its more casual use in everyday interactions. Teasing acts as a catalyst for a more profound comprehension of medical facts, encouraging patients to actively participate in their care by expressing concerns and preferences, thus aligning medical decisions with patient autonomy. The responses to teases vary, reflecting the complexity of clinical communication and the need for a delicate balance between humour and professionalism.ConclusionsThe study underscores the significance of teasing as a multifaceted communicative practice within outpatient clinical interactions. It highlights how teasing can contribute to a more nuanced and patient‐centred approach to healthcare, enhancing the collaborative exploration of medical advice and fostering a more engaging and empathetic clinical environment.Patient or Public ContributionThis study utilized real outpatient interactions, with participants' informed consent. The analysis focused on authentic teasing exchanges between clinicians and patients, ensuring the findings reflect genuine communication practices in healthcare settings. Participants, including both healthcare professionals and patients, were informed about the study's goals and their essential role in providing insights into clinician–patient communication, contributing to a deeper understanding of interaction dynamics in outpatient clinics.

Transforming evidence-based clinical guidelines into implementable clinical decision support services: the CAREPATH study for multimorbidity management.

The CAREPATH Project aims to develop a patient-centered integrated care platform tailored to older adults with multimorbidity, including mild cognitive impairment (MCI) or mild dementia. Our goal is to empower multidisciplinary care teams to craft personalized holistic care plans while adhering to evidence-based guidelines. This necessitates the creation of clear specifications for clinical decision support (CDS) services, consolidating guidance from multiple evidence-based clinical guidelines. Thus, a co-creation approach involving both clinical and technical experts is essential. This paper outlines a robust methodology for generating implementable specifications for CDS services to automate clinical guidelines. We have established a co-creation framework to facilitate collaborative exploration of clinical guidelines between clinical experts and software engineers. We have proposed an open, repeatable, and traceable method for translating evidence-based guideline narratives into implementable specifications of CDS services. Our approach, based on international standards such as CDS-Hooks and HL7 FHIR, enhances interoperability and potential adoption of CDS services across diverse healthcare systems. This methodology has been followed to create implementable specifications for 65 CDS services, automating CAREPATH consensus guideline consolidating guidance from 25 selected evidence-based guidelines. A total of 296 CDS rules have been formally defined, with input parameters defined as clinical concepts bound to FHIR resources and international code systems. Outputs include 346 well-defined CDS Cards, offering clear guidance for care plan activities and goal suggestions. These specifications have led to the implementation of 65 CDS services integrated into the CAREPATH Adaptive Integrated Care Platform. Our methodology offers a systematic, replicable process for generating CDS specifications, ensuring consistency and reliability across implementation. By fostering collaboration between clinical expertise and technical proficiency, we enhance the quality and relevance of generated specifications. Clear traceability enables stakeholders to track the development process and ensure adherence to guideline recommendations.

An optimisation study for leaching synthetic scheelite in H2SO4 and H2O2 solution

ABSTRACT Tungsten production primarily relies on scheelite, a secondary resource due to its complex ore composition and lower grade compared to high-grade wolframite. Synthetic scheelite gains significance for its low impurity content and accessibility in ongoing laboratory-based investigations. Recent advancements offer a feasible environment-friendly leaching method using a mixed solution of H2SO4 and H2O2 under normal pressure and moderate temperatures. However, comprehensive research on this novel method is lacking, emphasising the need for collaborative exploration and operational optimisation. The present work is to provide insights and improvements in reagent usage to promote economic and environmental sustainability. A detailed investigation into the thermal decomposition process was also conducted without compromising leaching efficiency. The findings suggest the potential for eco-friendly lixivium recycling with reduced levels of chemicals, decreasing operational costs. Notably, optimising the thermal decomposition duration to 6 hours at an L/S (mL/g) ratio of 10 enhances H2WO4 crystallization. Furthermore, experiments without H2SO4 supplementation highlight the system's optimisation potential. Finally, the leaching process was optimised by decreasing H2SO4 concentration to 1 mol/L from 3 mol/L, increasing the temperature to 60°C, and extending the leaching duration to 120 minutes. This leads to a cost-effective synthetic scheelite leaching process with environmental benefits.

Innovation in Nutrition Promotion : Literature Analysis to Increase Community Awareness and Healthy Practices

The important role of health promotion in improving community welfare has encouraged effective innovation to increase nutritional awareness and healthy lifestyle practices. This research explores recent innovations in health promotion strategies, particularly in nutrition and health, to increase awareness and encourage positive behavioral change. Through comprehensive literature analysis, this research identifies and evaluates approaches such as technology interventions, community-based initiatives, and other new strategies. The results show that innovations such as nutrition promotion media (pop-up books, PGS cards, leaflets, nutrition puzzles, and educational videos) as well as active community participation (FGD and lunch menu practices) and empowering health workers and teachers, have succeeded in increasing knowledge. , attitudes and nutritional behavior of the community. Case studies and successful programs show real improvements in community nutrition practices. However, challenges such as limited sample size, short intervention duration, and methodological obstacles need to be overcome. Based on these findings, further research is recommended focusing on: (1) longitudinal research with larger sample sizes to understand long-term impacts; (2) exploration of contextual factors that influence program effectiveness; (3) evaluate the effectiveness of innovation for vulnerable groups such as people with disabilities or people with low education; (4) evaluation of community empowerment strategies and their impact on local health policies; and (5) exploration of cross-sectoral collaboration for holistic and sustainable nutrition promotion programs. These findings provide valuable insights for practitioners, researchers, and policymakers, and serve as a comprehensive resource for designing programs that increase nutrition awareness and encourage healthy practices in communities.

Advances of NOTCH2NLC Repeat Expansions and Associated Diseases: A Bibliometric and Meta-analysis.

The unclear pathogenic mechanisms of neurodegenerative disorders stemming from NOTCH2NLC GGC repeat expansions drive focused research. Thus, a bibliometric and meta-analysis was conducted to uncover research trends and positivity rates in NOTCH2NLC. We conducted systematic searches in the Web of Science, PubMed, Embase, and Scopus databases for studies related to NOTCH2NLC up until August 2, 2023. Information regarding countries, institutions, authors, journals, and keywords of studies included in the Web of Science was analyzed and visualized. The positivity rates of NOTCH2NLC GGC repeat expansions across all screened patients and patients' families were pooled under the random-effects model. Publication bias and its impact were examined using funnel plots, Egger's linear regression, and trim-and-fill method. The bibliometric analysis, revealing pronounced publication growth, comprised 119 studies, which came from China and Japan particularly. "Neuronal intranuclear inclusion disease" emerged as a frequently used keyword. The meta-analysis comprised 36 studies, indicating global positivity rates of 1.79% (95% CI, 0.75-3.17) for all patients and 2.00% (95% CI, 0.26-4.78) for patients' families. Subgroup analyses based on region and phenotype suggested the highest NOTCH2NLC positivity rates in Taiwan population (5.42%, 95% CI 0.08-16.89) and in leukoencephalopathy-dominant patients (8.25%, 95% CI, 3.01-15.60). Sensitivity analysis affirmed the robustness of results. In conclusion, NOTCH2NLC GGC repeat expansions exhibit rare globally, primarily in East Asia, and leukoencephalopathy-dominant patients, emphasizing regional and phenotypic distinctions. Emerging focal points in NOTCH2NLC researches underscore the need for collaborative exploration.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

Preface

Greetings and a warm welcome to the expansive compilation of research and scholarly contributions presented in the Proceedings of the ICEMINE 2023. In the spirit of intellectual exploration and collaboration, this voluminous collection encapsulates the diverse and profound discussions that unfolded during the conference. As we delve into the following pages, readers will encounter a comprehensive exploration of knowledge, innovation, and interdisciplinary collaboration within the overarching theme of ICEMINE 2023.ICEMINE 2023 is the 6th International Conference hosted by the Faculty of Mineral Technology, Universitas Pembangunan Nasional “Veteran” Yogyakarta, Indonesia. The conference was held at Grand Keisha Hotel, Yogyakarta, Indonesia, on the 9th of November 2023. The theme of this year’s program is “Accelerating the advancements in lower carbon energy for a sustainable environment”. We extend our appreciation to our esteemed partner university, whose unwavering dedication and scholarly contributions have significantly enriched the contents of this conference proceedings. In collaboration with our partner universities, Trisakti University and PEM Akamigas, UPN Veteran Yogyakarta creates an academic platform that fosters diverse perspectives, innovative ideas, and interdisciplinary exchange. Their insightful research and collaborative spirit have undeniably elevated the quality of discourse within our academic community, fostering an environment conducive to intellectual growth and innovation.Furthermore, we would like to express our profound gratitude to our sponsors, whose generous support has been pivotal in bringing this event to success. Their unwavering commitment to advancing research and cultivating intellectual exchange underscores the importance of their role in shaping the trajectory of our academic disciplines.List of Reflecting on Sustainability in Indonesia, Navigating the future: a vision for what lies ahead, Steering Committee, Organizing Committee, Scientific Committee, Keynote Speakers and Best Presenters are available in this Pdf.

DEVELOPING METHODS FOR AUTOMATIC PROCESSING SYSTEMS OF KAZAKH LANGUAGE

The linguistic diversity of the Kazakh language poses unique challenges and opportunities for the development of automatic processing systems. This research explores a comprehensive array of methodologies employed in advancing automatic processing systems tailored for the Kazakh language. The study encompasses morphological analysis, sentiment analysis, speech recognition, machine translation, and other key facets crucial for understanding and enhancing linguistic interactions. Additionally, the research delves into the creation of lexical resources, sentiment analysis tools, and named entity recognition models specific to the Kazakh language. Drawing on interdisciplinary approaches, the study investigates the integration of Kazakh language processing into e-learning systems, social media analysis, and explores challenges and opportunities in developing a Kazakh language corpus. The incorporation of cutting-edge technologies, such as neural networks and transformer-based models, is examined to address emerging trends in language processing. Through an exploration of cross-cultural collaboration, ethical considerations, and policy implications, this research contributes to the global dialogue on inclusive language technology. The findings provide insights into the current state of Kazakh language processing research, highlight persisting challenges, and suggest avenues for future exploration and development in the field.

Learning hybrid dynamic transformers for underwater image super-resolution

Underwater image super-resolution is vital for enhancing the clarity and detail of underwater imagery, enabling improved analysis, navigation, and exploration in underwater environments where visual quality is typically degraded due to factors like water turbidity and light attenuation. In this paper, we propose an effective hybrid dynamic Transformer (called HDT-Net) for underwater image super-resolution, leveraging a collaborative exploration of both local and global information aggregation to help image restoration. Firstly, we introduce a dynamic local self-attention to adaptively capture important spatial details in degraded underwater images by employing dynamic weighting. Secondly, considering that visual transformers tend to introduce irrelevant information when modeling the global context, thereby interfering with the reconstruction of high-resolution images, we design a sparse non-local self-attention to more accurately compute self-similarity by setting a top-k threshold. Finally, we integrate these two self-attention mechanisms into the hybrid dynamic transformer module, constituting the primary feature extraction unit of our proposed method. Quantitative and qualitative analyses on benchmark datasets demonstrate that our approach achieves superior performance compared to previous CNN and Transformer models.