Search Results (92)

Project-based learning provides a common context for STEM education at all educational levels. However, before future chemistry teachers can implement it in their teaching, they need to have experience in completing complex projects by themselves. According to previous research, an engineering perspective in STEM projects has been difficult to implement. Therefore, this design-based research project focuses on producing pedagogical resources for conducting STEM projects based on authentic engineering practices. Through three-cycle design research, we crafted Excel templates that support a step-by-step framework for completing complex engineering projects and an evaluation matrix that includes formative and summative tools. The design solutions were validated through empirical problem analysis, which yielded qualitative insights into the possibilities and challenges of the produced tools. From this data, we formulated five best practices for teachers to focus on achieving successful project outcomes, with priority being to support the progress of the engineering approach and support it via guidance and peer collaboration. For future chemistry teachers, artificial intelligence tools offer support, especially for hardware assembly and software coding. The research produced educational artifacts that support conducting STEM projects in higher education and insights into their best practices. Since design solutions are based on research and real-life engineering practices, they are useful for all fields in higher education that conduct STEM projects and aim to teach authentic engineering skills. Full article

Prompt engineering (PE) has emerged as a transformative paradigm in software engineering (SE), leveraging large language models (LLMs) to support a wide range of SE tasks, including code generation, bug detection, and software traceability. This study conducts a systematic literature review (SLR) combined with a co-citation network analysis of 42 peer-reviewed journal articles to map key research themes, commonly applied PE methods, and evaluation metrics in the SE domain. The results reveal four prominent research clusters: manual prompt crafting, retrieval-augmented generation, chain-of-thought prompting, and automated prompt tuning. These approaches demonstrate notable progress, often matching or surpassing traditional fine-tuning methods in terms of adaptability and computational efficiency. Interdisciplinary collaboration among experts in AI, machine learning, and software engineering is identified as a key driver of innovation. However, several research gaps remain, including the absence of standardized evaluation protocols, sensitivity to prompt brittleness, and challenges in scalability across diverse SE applications. To address these issues, a modular prompt engineering framework is proposed, integrating human-in-the-loop design, automated prompt optimization, and version control mechanisms. Additionally, a conceptual pipeline is introduced to support domain adaptation and cross-domain generalization. Finally, a strategic research roadmap is presented, emphasizing future work on interpretability, fairness, and collaborative development platforms. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering. Full article

In the domain of multi-agent reinforcement learning, conventional algorithms such as VDN, QMIX, and QTRAN have exhibited favorable performance in static task scenarios. However, these algorithms encounter challenges due to their limited capacity to model elastic collaboration scenarios, wherein the number of agents and the state-action space undergo dynamic changes over time. To address this limitation, this paper proposes a novel multi-agent collaboration mechanism: the Dual-Stream Dynamic Weight Compensation Network (DDWCN). This method employs a dual-stream action modeling network for the classification and processing of actions. It integrates an information compensation network and a dynamic weight fusion network. These additions have been demonstrated to enhance the network’s robustness and generalization capabilities for complex collaborative tasks. Extensive experimentation on a variety of benchmark tasks in StarCraft II has validated the effectiveness of DDWCN. The findings indicate that this approach exhibits commendable scalability and reliability in authentic multi-agent collaboration scenarios, underscoring its extensive applicability. Full article

Virtual reality interaction design and story co-creation design for multiple users is an interdisciplinary research field that merges human–computer interaction, creative design, and virtual reality technologies. Story co-creation design enables multiple users to collectively generate and share narratives, allowing them to contribute to the storyline, modify plot trajectories, and craft characters, thereby facilitating a dynamic storytelling experience. Through advanced virtual reality interaction design, collaboration and social engagement can be further enriched to encourage active participation. This study investigates the facilitation of narrative creation and enhancement of storytelling skills in virtual reality by leveraging existing research on story co-creation design and virtual reality technology. Subsequently, we developed and evaluated the virtual reality card-based collaborative storytelling platform Co-Relay. By analyzing interaction data and user feedback obtained from user testing and experimental trials, we observed substantial enhancements in user engagement, immersion, creativity, and fulfillment of emotional and social needs compared to a conventional web-based storytelling platform. The primary contribution of this study lies in demonstrating how the incorporation of story co-creation can elevate storytelling proficiency, plot development, and social interaction within the virtual reality environment. Our novel methodology offers a fresh outlook on the design of collaborative narrative creation in virtual reality, particularly by integrating participatory multi-user storytelling platforms that blur the traditional boundaries between creators and audiences, as well as between fiction and reality. Full article

Mining activities in Pakistan’s mineral-rich provinces threaten freshwater security through groundwater depletion, contamination, and flood-induced pollution. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework integrating governance, social, environmental, and technical (GSET) dimensions to holistically assess mining-induced water hazards across Balochistan, Khyber Pakhtunkhwa, and Punjab. Using GIS-based spatial risk mapping with multi-layer hydrological modeling, we combine computational analysis and participatory validation to identify vulnerability hotspots and prioritize high-risk mines. Community workshops involving women water collectors, indigenous leaders, and local experts enhanced map accuracy by translating indigenous knowledge into spatially referenced mitigation plans and integrating gender-sensitive metrics to address gendered water access disparities. Key findings reveal severe groundwater depletion, acid mine drainage, and gendered burdens near Saindak and Cherat mines. Multi-sectoral engagements secured corporate commitments for water stewardship and policy advances in inclusive governance. The framework employs four priority-ranked risk categories (Governance-Economic 15%, Social-Community 30%, Environmental 40%, Technical-Geological 15%) derived via local stakeholder collaboration, enabling context-specific interventions. Despite data limitations, the GIS-driven methodology provides a scalable model for regions facing socio-environmental vulnerabilities. The results demonstrate how community participation directly shaped village-level water management alongside GSET analysis to craft equitable risk reduction strategies. Spatially explicit risk maps guided infrastructure upgrades and zoning regulations, advancing SDG 6 and 13 progress in Pakistan. This work underscores the value of inclusive, weighted frameworks for sustainable mining–water nexus management in Pakistan and analogous contexts. Full article

Proclamation of the gospel is a perennial practice of congregational leadership demanding responsiveness to issues, trends, and events impacting congregations, their local and regional communities, and the challenges of the world. How do congregational leaders equip themselves for the important and ever-changing task of preaching? Lifelong learning, the fastest-growing and least-resourced aspect of theological education in North America, provides this opportunity. Through a 2024 survey, this quantitative study provides insight into the lifelong learning needs of Methodist preachers, including differences based on gender and race/ethnicity. Time for additional learning is the major perceived obstacle for preachers desiring to improve their craft. Thus, lifelong learning programs must make the case for how the required time and energy will benefit the preacher participating in such programs. Specifically, the activities of reviewing recordings of sermons (both one’s own and those of other preachers), receiving constructive feedback on sermons, and realizing the collaborative potential of preaching must be structured in ways that prove the value of these investments for preachers. This data on the lifelong learning needs of Methodist preachers has implications on multiple levels: conceptual, institutional, congregational, and personal. Full article

The widespread deployment of service robots in industries such as hospitality has significantly transformed service delivery, influencing not only customers but also employees. This study examines the multi-dimensional impact of service robots on hotel employees, focusing on their attitudes, emotional responses, and willingness to collaborate, as shaped by perceived benefits (service reliability, process efficiency, and job crafting) and risks (inefficiency, insufficient intelligence, and privacy concerns). Data were collected from 471 hotel employees in South Korea with experience working alongside service robots, and Hayes’ Process Macro Model 4 was employed for hypothesis testing. The findings reveal that perceived benefits positively influence employees’ attitudes, emotions, and willingness to collaborate, while perceived risks exert a negative impact. Furthermore, attitudes and emotional responses mediate these relationships. These findings provide theoretical and practical insights for managers, policymakers, and service robot manufacturers to address employee concerns, improve human–robot collaboration, and promote sustainable technological integration within the service industry. Full article

Background: Strategies are needed to address the elevated prevalence of chronic kidney disease (CKD) in socioeconomically disadvantaged regions where obesity, smoking, and type 2 diabetes rates are high. Methods: Recognising the inadequacy of generic health approaches in complex contexts, this study employed a participatory action research (PAR) framework to design and deliver five co-design community workshops in two stages over one year. Stage one workshops identified key matters of concern and stage two focussed on problem solving and co-creating solutions. The goal was to inform health service delivery in a region with high CKD prevalence and explore strategies to overcome barriers to individualised, collaborative care, and promote self-management. Results: The workshops identified three themes: 1. achieving person/family-centred care; 2. multimorbidity and siloed care (stage one); and 3. a kidney wellness framework (stage two). Conclusions: The findings reinforce the need for enhanced care coordination, and highlight the importance of consistent information sources, clear referral pathways, and centralised data sharing among health professionals. The proposed kidney healthcare framework aims to support various professionals, fostering linkages between primary and tertiary care, with an emphasis on professional development, especially in communicating complex information to individuals with multimorbidities. While co-designed healthcare models show promise, challenges persist in effective self-management amidst complex disease information and multimorbidity. Full article

Recent cognitive literary studies of fiction have begun to reveal patterns in the ways authors engage readers’ bodily and environmentally grounded imaginations. This study brings fiction writers’ craft knowledge into conversation with neuroscientific, cognitive, and literary studies of multimodal imagery and other embodied responses to fiction reading. Developed through years of literary experiments, craft knowledge involves using language not just to engage readers’ senses but to broaden their understandings of how senses work. A close analysis of Edwidge Danticat’s craft techniques in Breath, Eyes, Memory (1994) affirms some recent literary and scientific findings on how language can activate readers’ sensory and motor systems. Danticat’s cues to readers’ imaginations present a relational, environmentally engaged kind of sensorimotor experience that may widen scientific understandings of how sensory and motor systems collaboratively ground cognition. By helping diverse readers imagine a young Haitian American woman’s movements, sensations, and emotions, Danticat’s craft also does political work, depicting the inner lives of characters under-represented in widely published fiction. Full article

Background and Objectives: Stroke units rely on interdisciplinary teams. Professionals with complementary alternative practices may join the team since such approaches are increasingly supporting the stroke recovery process. The aim of this study was to develop a better understanding of how a dance and somatic-informed movement intervention could be utilized in an inpatient setting as an adjunct to post-stroke therapy. We sought to identify (1) what knowledge we could draw on to develop the content and pedagogy for the intervention, (2) what helped/hindered the intervention aimed at functional recovery, as perceived by the practitioner-researchers, and (3) the relationships experienced with the various stakeholders. Materials and Methods: This exploratory qualitative study used the enhanced critical incident technique to collect retrospective self-report data from two practitioner-researchers engaged in delivering the intervention over two months. The data underwent thematic analysis. Patients (n = 6) in a stroke unit were selected within ≤72 h of hospital admission. The intervention was conducted four to six times a week until the vascular neurologist (co-researcher) authorized their transfer to a rehabilitation hospital. Results: The intervention evolved from crafting content and pedagogy at the intersection of different areas of knowledge (dance, somatics, neuroscience, and stroke). It was based on active, assisted, and passive movements. Verbal, tactile, visual, and imaginary inputs used to enhance body awareness were perceived as potentially helping patients recover some range of motion, quality of movements, and voluntary movement control, and fostering calmness and motivation. The intervention was well received by stakeholders. Conclusions: Dance and somatic-informed movement can be a complementary therapy in stroke units, although it requires a delicate juggling of time allocation within the interdisciplinary team. Further studies should be conducted with a larger number of patients and different practitioners. Collaboration between qualitative and quantitative researchers is needed to make a robust case for such interventions. Full article

Embodied Conversational Agents (ECAs) serve as digital twins (DTs), visually and behaviorally mirroring human counterparts in various roles, including healthcare coaching. While existing research primarily focuses on single-coach ECAs, our work explores the benefits of multi-coach virtual health sessions, where users engage with specialized diet, physical, and cognitive coaches simultaneously. ECAs require verbal relational cues—such as empowerment, affirmation, and empathy—to foster user engagement and adherence. Our study integrates Generative AI to automate the embedding of these cues into coaching dialogues, ensuring the advice remains unchanged while enhancing delivery. We employ ChatGPT to generate empathetic and collaborative dialogues, comparing their effectiveness against manually crafted alternatives. Using three participant cohorts, we analyze user perception of the helpfulness of AI-generated versus human-generated relational cues. Additionally, we investigate whether AI-generated dialogues preserve the original advice’s semantics and whether human or automated validation better evaluates their lexical meaning. Our findings contribute to the automation of digital health coaching. Comparing ChatGPT- and human-generated dialogues for helpfulness, users rated human dialogues as more helpful, particularly for working alliance and affirmation cues, whereas AI-generated dialogues were equally effective for empowerment. By refining relational cues in AI-generated dialogues, this research paves the way for automated virtual health coaching solutions. Full article

With the development of value decomposition methods, multi-agent reinforcement learning (MARL) has made significant progress in balancing autonomous decision making with collective cooperation. However, the collaborative dynamics among agents are continuously changing. The current value decomposition methods struggle to adeptly handle these dynamic changes, thereby impairing the effectiveness of cooperative policies. In this paper, we introduce the concept of latent interaction, upon which an innovative method for generating weights is developed. The proposed method derives weights from the history information, thereby enhancing the accuracy of value estimations. Building upon this, we further propose a dynamic masking mechanism that recalibrates history information in response to the activity level of agents, improving the precision of latent interaction assessments. Experimental results demonstrate the improved training speed and superior performance of the proposed method in both a multi-agent particle environment and the StarCraft Multi-Agent Challenge. Full article

The clustering and collaborative development among cities, as a strategic measure concerning regional inclusivity and sustainable development, is both a current practice and a future trend, as evidenced by numerous successful cases worldwide. Scholarly research has extensively validated, from a macro-level perspective, its positive effects on social and economic aspects. However, relatively little research has examined its impact on the behavior of micro-level enterprises. To bridge this gap, this study leverages China’s urban integration strategy as a natural experimental framework and employs a two-way fixed effects model to conduct an in-depth investigation. The empirical results indicate that coordinated urban agglomeration development significantly enhances the technological complexity of enterprises within the region. The primary mechanism underlying this relationship is that the urban integration strategy facilitates regional financial concentration, fosters industrial collaborative agglomeration, and advances firm specialization, thereby enhancing the innovation capacity of enterprises. Furthermore, the heterogeneity analysis indicates that the findings are particularly pronounced for core cities within urban clusters, cities with lower levels of industrial specialization, and those with higher levels of financial development. Additionally, the effects are more significant for firms that face fewer financing constraints and inherently possess strong innovation capabilities. These findings not only augment the existing research on the micro-level effects of urban cluster development but also provide valuable insights for local governments in crafting policies to further enhance the sustainable innovation capacity of enterprises. Full article

Ulster University’s School of Medicine embraces a problem-based learning (PBL) approach, yet crafting scenarios for this method poses challenges, requiring collaboration among medical and academic experts who are often difficult to convene. This obstacle can compromise scenario quality and ultimately impede students’ learning experiences. To address this issue, the school trialed the use of AI technology to develop a case scenario focusing on headaches caused by cerebral haemorrhage. The process involved a dialogue between a single “author” and ChatGPT, with their outputs combined into a complete clinical case adhering to the school’s standard template. Six experienced PBL tutors conducted quality checks on the scenario. The tutors did not immediately endorse its use, recommending further enhancements. Suggestions included updating terminology, names, spelling, and protocols to align with current best practices, providing additional explanations such as interventions and improvements post-initial stability, incorporating real scans instead of descriptions, reviewing symptoms and timelines for realism, and addressing comprehension issues by refraining from directly providing answers and including probing questions instead. From this trial, several valuable lessons were learned: AI can assist a single author in crafting medical scenarios, easing the challenges of organizing expert teams. However, the author’s role shifts to reviewing and enhancing depth, guided by a template, with clinician input crucial for authenticity. ChatGPT respects patient data privacy and confidentiality by abstaining from providing scanned images, and while AI can generate discussion questions for tutorials, it may require modification to enhance specificity and provoke critical thought. Furthermore, AI can generate multiple-choice questions and compile reading resources to support self-directed learning. Overall, adopting AI technology can improve efficiency in the case-writing process. Full article

Since 1969, 55 years ago, Lunar Laser Ranging (LLR) has provided accurate and precise (down to ~1 cm RMS) measurements of the Moon’s orbit thanks to the Apollo and Lunokhod Cube Corner Retroreflector (CCR) Laser Retroreflector Arrays (LRAs) deployed on the Moon. Nowadays, the current level of precision of these measurements is largely limited by the lunar librations affecting the old generation of LRAs. To improve this situation, next-generation libration-free retroreflectors are necessary. To this end, the Satellite/lunar/GNSS laser ranging/altimetry and cube/microsat Characterization Facilities Laboratory (SCF_Lab) at the Istituto Nazionale di Fisica Nucleare—Laboratori Nazionali di Frascati (INFN-LNF), in collaboration with the University of Maryland (UMD) and supported by the Italian Space Agency (ASI), developed MoonLIGHT (Moon Laser Instrumentation for General relativity High-accuracy Tests), a single large CCR with a front face diameter of 100 mm, nominally unaffected by librations, and with optical performances comparable to the Apollo/Lunokhod LRAs of CCRs. Such a big CCR (hereafter, ML100) is mounted into a specifically devised, designed, and manufactured robotic actuator, funded by the European Space Agency (ESA), the so-called MoonLIGHT Pointing Actuator (MPAc), which, once its host craft has landed on the Moon, will finely align the front face of the ML100 towards the Earth. The (optical) performances of such a piece of hardware, MoonLIGHT+MPAc, were tested in/by the SCF_Lab in order to ensure that it was space flight ready before its integration onto the deck of the host craft. After its successful deployment on the Moon, additional and better-quality LLR data (down to ~ 1 mm RMS or better for the contribution of the laser retroreflector instrument, MoonLIGHT, to the total LLR error budget) will be available to the community for future and enhanced tests of gravitational theories. Full article