Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis

Underdogs in the game: how sport start-ups navigate collaboration with external actors

What socioecological conditions nurture the ingenuity and collaborative interactions underlying transformative technological innovations? We compiled a dataset on more than 400 major technological inventions from 1690 to 1990 spanning seven categories (agriculture, armaments, information and communication, household, industry, medical, and transportation) and performed inductive macroecological analyses to address how attributes of inventors, teams, and their social and geographic environments contributed to the innovation of new technologies that have changed the way people live. The vast majority of inventions were attributed to single inventors of various ages. The frequency, size, and cultural diversity of teams increased in the 20th Century, as did the proportion of inventions attributed to women and immigrants. A wide variety of environments acted as innovation hubs, including rural areas as well as specialized institutions and large cities. Invention rate (number of inventions per time period) peaked during the 1800s in cities, rural areas, and most categories but continued increasing over time in institutional environments and medical and communication technologies. The overall pattern across ages, genders, team attributes, environments, technological categories, and time periods supports the conclusion that opportunity, ingenuity, and environment all play key roles in the inventiveness phase of the innovation process: creative individuals in particular ecological environments and social settings come up with novel solutions to specific practical problems. Similar innovation patterns have been observed for tool use among nonhuman primates, highlighting unifying processes of innovation. Our macroecological approach offers valuable insights into the emergence and drivers of innovation and material culture.

This study examines how scaffolding strategies improve the development of oral language skills, particularly speaking and listening, of Kurdish English as a Foreign Language (EFL) university students. Based on Vygotsky’s sociocultural theory and the notion of the Zone of Proximal Development (ZPD), the current study uses a quasi-experimental design to compare and contrast the outcomes of two groups, a control and an experimental. While the control group received regular instruction, the experimental group was exposed to structured scaffolding-based instruction. Moreover, during a four-month experimental course, 86 students completed IELTS-based pre- and post-tests. The data collected from the tests of both groups were analyzed using a combination of descriptive statistics and Paired–Samples t test. The findings revealed that while both groups showed improvement, the experimental group significantly outperformed the control group in both speaking and listening post-tests. Based on this, the experimental group exhibited statistically significant improvement in speaking (t(84) = 3.87, p <.001) and listening (t(84) = 3.45, p <.01). As such, the findings also affirmed the effectiveness of scaffolding strategies in enhancing oral skills, when applied consistently through gradual release of support, collaborative interaction, and constructive feedback from the teacher. This study contributes to a deeper understanding of how scaffolding supports EFL university students in developing their speaking and listening skill

ABSTRACT Generative AI is becoming an important intelligent tool for collaborative learning interactions and promoting collective cognition. However, the pathways and effects of promoting group awareness in collaboration remain unclear. To reveal how generative AI influences interactive behavior in collaborative learning, this study, grounded in the situated expectancy-value theory framework, examines the critical role of environmental support and expectancy-value beliefs (self-efficacy and perceived usefulness) in enhancing group awareness within generative AI-assisted collaborative learning. Specifically, group awarenessis conceptualized in three subdimensions: cognitive awareness, behavioral awareness, and social awareness.A systematic sampling method was employed to survey 334 students from five universities across different regions of China. Structural equation modeling showed that Environmental support significantly contributes to group awareness effects in collaboration. Self-efficacy mediates cognitive awareness and behavioral awareness in environmental support for generative AI facilitated collaboration. Perceived usefulness mediates the effect of environmental support on cognitive awareness, behavioral awareness, and social awareness. In addition, subjects in the high environmental support and high expectancy-value belief groups showed better group awareness. This study demonstrates the pathways and factors influencing the role of group awareness in the process of generative AI, facilitating collaboration from the perspective of situated expectancy-value.

Resilient manufacturing plays a key role in Industry 5.0, focusing on personalised demands and emphasising the combination of production flexibility and smart technologies. It enables workshops to adjust production more flexible to meet dynamic production needs, while optimising manufacturing processes. Autonomous Mobile Robots (AMRs) have become crucial in enhancing resilient manufacturing by dynamically adapting to real-time material handling tasks. Based on multi-agent communication, this paper proposes a distributed collaborative scheduling scheme comprising the autonomous perception, collaborative interaction, and scheduling decision aimed at enhancing workshop material handling resilience. First, based on the ‘perceive-reason-decide’ process, an AI-Agent model for AMR is constructed, providing them with sensing, analyzing, and processing capabilities. Second, using the principles of single and bidirectional feedback modes, a collaborative interaction model is established to achieve their communication capabilities. Third, an AMR scheduling model is built based on Multi-agent Deep Deterministic Policy Gradient algorithm (MADDPG), which evaluates the scheduling result of transportation tasks by incorporating attention mechanisms and reward values. Finally, the collaborative scheduling scheme is validated through case studies. It can quickly respond to changes in material handling and make optimal allocation decisions, thus significantly improving the resilience of the workshop.

This study aims to analyze the integration of managerial supervision and clinical supervision in improving teacher performance within Islamic educational institutions through a Systematic Literature Review (SLR) approach. The review draws on scholarly articles indexed in Scopus, DOAJ, and Google Scholar, with a publication range limited to the last ten years to ensure the relevance and currency of findings. The selection process followed rigorous inclusion and exclusion criteria, focusing on empirical and conceptual studies that address supervision practices in educational contexts, particularly within Islamic institutions. The findings indicate that managerial supervision plays a critical role in establishing a strong organizational foundation through effective planning, coordination, leadership, and resource management. This form of supervision contributes to the creation of a conducive academic climate that supports teacher professionalism and institutional effectiveness. Meanwhile, clinical supervision emphasizes direct, reflective, and collaborative interactions between supervisors and teachers, fostering continuous pedagogical improvement and instructional quality. The integration of these two supervisory approaches demonstrates a complementary relationship, where managerial supervision ensures systemic support and institutional alignment, while clinical supervision enhances individual teacher competence and classroom practices. This synergy leads to more sustainable improvements in teacher performance, professional development, and overall educational quality. The study contributes to the advancement of educational supervision literature by providing a comprehensive conceptual framework that integrates managerial and clinical supervision within Islamic educational settings. Furthermore, it enriches the body of knowledge by highlighting the synergistic mechanisms through which both supervisory approaches can be implemented to strengthen teacher professionalism and institutional effectiveness, offering a foundation for future empirical research and policy development in educational management and supervision. The study concludes that a balanced and integrated supervision model is essential for strengthening teacher performance and achieving educational excellence in Islamic educational institutions.

This study aimed to develop and validate a tool for assessing communication as a leadership competency among graduate healthcare students. Drawing from the American College of Healthcare Executives 2023 Competencies Assessment Tool and the National Center for Healthcare Leadership Competency Model 3.0, a survey was designed to capture essential domains of communication within leadership contexts. The initial instrument included six domains: relationship management, written communication, facilitation and negotiation, listening skills, verbal communication and effective speaking, and communicating in groups. After initial pilot testing revealed issues with item clarity and scale consistency, the survey was revised to improve content alignment and measurement accuracy. Subsequent testing incorporated exploratory factor analysis (EFA), internal consistency measures, and test-retest reliability analysis. As a result, eight components emerged and ultimately categorized as the following seven domains: communicating in groups, verbal communication and effective speaking, relationship management, written communication, listening skills, ethical collaboration, and respectful interactions. Most domains demonstrated acceptable to excellent psychometric properties, with Cronbach's alpha values and intraclass correlation coefficients (ICC) supporting internal consistency and stability over time. This tool, the Communication Competency Assessment in Health Professions (CCAHP) survey, provides a validated framework for assessing communication competencies in graduate health professions education and offers practical applications for guiding curriculum design, fostering interprofessional development, and sup¬porting professional identity formation.

Teacher readiness for generative AI: A Theory of Planned Behaviour approach

Enabling human–robot teaming through Haptic feedback: From caged automation to collaborative interaction

Instructional communication plays a central role in shaping meaningful learning experiences, particularly within student-centered and dialogical educational paradigms. This study aims to explore the forms and implementation of instructional communication in English learning based on the Finnish educational approach at Mutiara Insani Junior High School, Purwakarta. Employing a qualitative case study design, the research focuses on English teachers and eighth-grade students as the main subjects. Data were collected through classroom observations, in-depth semi-structured interviews, and document analysis, and analyzed using the interactive model of Miles and Huberman, which includes data reduction, data display, and conclusion drawing with triangulation to ensure credibility. The findings reveal that instructional communication in English learning is implemented in multimodal and contextual forms, including verbal-bilingual communication, nonverbal communication, media-supported communication, hands-on activities, collaborative interaction, and affective communication. Teachers position themselves as facilitators who encourage dialogue, provide constructive feedback, and create a psychologically safe learning environment. The integration of Finnish-based learning principles emphasizes student-centeredness, joyful learning, contextual relevance, and minimal exam orientation. Supporting factors include positive teacher–student relationships, flexible teaching strategies, and the use of varied media, while inhibiting factors involve differences in student proficiency, emotional readiness, time limitations, and vocabulary constraints.

ABSTRACT This study examined preschool children’s science experiences using the Child Observation in Preschools (COP) measure across 60 Head Start classrooms with 307 children. Research Findings: Science instruction was infrequent, occurring in less than 20% of observed periods and significantly less than English Language Arts or mathematics activities. When science occurred, it predominantly happened during whole-group instruction. Children showed medium engagement levels (47% of science periods), primarily through parallel rather than collaborative interactions. Science experiences were largely passive, with 80% of children listening to teachers rather than actively engaging with materials or peers. Language interactions were overwhelmingly teacher-directed, with children listening rather than discussing concepts collaboratively. Individual children’s science experiences varied considerably, with some receiving multiple opportunities while others received few or none. Practice or Policy: These findings reveal significant missed opportunities for science learning in preschool settings. Teachers may benefit from professional development focused on recognizing and creating informal science learning opportunities throughout the day, particularly during center time and outdoor activities. Programs should emphasize hands-on, inquiry-based science experiences that promote higher child engagement and peer interaction rather than passive whole-group instruction.

Artificial intelligence is creating a new era in university education, impacting both teaching and learning, as well as new research and institutional administration. The current study is a narrative review of the existing literature on the application of artificial intelligence in the future of higher education. Studies published from 2018 to 2025 were identified from various academic search engines using keywords pertaining to the concepts of AI, higher education, adaptive learning, and educational technology. Selected literature was critically analyzed to obtain the major trends, opportunities, and challenges for the integration of artificial intelligence in the University. From the review results, it was revealed that AI technologies can be leveraged for personalized learning, increase student engagement, aid in academic research, streamline administrative tasks, and boost AI-driven decision-making within higher education institutions. The study also raises a number of concerns related to the use of artificial intelligence, such as privacy and bias issues with data, academic integrity, digital inequalities, and the positions of educators in AI-supported learning settings. The paper underscores the need for institutional policies and responsible implementation practices, as well as ethical governance in AI utilization in universities to guarantee transparency and accountability. The study concludes that the future of university education is likely to depend on the collaborative interaction between the educator and intelligent technologies that will create more flexible, inclusive and innovative learning environment.

During the process of vehicle–grid interaction, the charging load of electric vehicles shows significant uncertainty, which is driven by multiple user behavior variables: including the differentiated characteristics of users’ daily travel needs, as well as personalized charging habits, random charging periods, and dynamic changes in charging power demands. To address the scheduling challenges arising from the uncertainty of electric vehicle loads in the interaction between electric vehicles and the power grid, this paper proposes a multi-objective optimization scheduling method for the interaction between electric vehicles and the power grid, which takes into account the uncertainty of power sources and loads. This method can enhance the economic operation level of the power grid, increase the acceptance capacity of renewable energy, and improve the stability of the system. Firstly, this paper proposes an improved K-means clustering algorithm, which combines Monte Carlo sampling to achieve the generation and reduction of scenarios for electric vehicle load and photovoltaic output. Secondly, a scheduling framework based on the vehicle–grid collaborative interaction mode is constructed, and a random optimization scheduling model for photovoltaic storage electric vehicles is established. Finally, an example of a photovoltaic storage charging station in an industrial park is used for verification. The simulation results demonstrate the economic feasibility and effectiveness of this scheduling strategy.

Cooperative learning has long been recognized as an effective pedagogical strategy, yet the development of innovative techniques tailored to modern educational demands remains a challenge. This study introduces the Curriculum Concept Constellation Technique (CCCT), a novel cooperative learning technique developed with the support of artificial intelligence (AI). The CCCT employs a metaphorical and visual approach, wherein students collaboratively map key curriculum concepts into visual 'constellations,' (Throughout this manuscript, references to 'constellations' should be understood as a pedagogical metaphor for conceptual relationships, not as an astronomical or literal representation)-a metaphor representing how individual ideas (like stars) interconnect to form meaningful patterns. This approach fosters deeper conceptual understanding through creativity, role-based collaboration, and peer interaction. Using a mixed-methods explanatory sequential design, the study examined the effects of CCCT on 67 prospective teachers enrolled in a "Curriculum Development in Education" course. Participants were divided into experimental (CCCT) and control (conventional lecture-based) groups. Quantitative results revealed statistically significant improvements in the experimental group's academic achievement (g = 0.839), co-regulation situations (g = 0.512), and attitudes toward cooperativeness (g = 0.751), with moderate effect sizes. Qualitative feedback highlighted CCCT's strengths in enhancing communication, teamwork, and engagement, though challenges such as unequal participation and time constraints were noted. The study demonstrates that AI can serve as a valuable generative tool in pedagogical innovation, producing structured cooperative learning techniques based on human-authored prompts. The findings suggest that AI-generated techniques can be effectively implemented in higher education contexts, offering a scalable model for developing tailored instructional methods.

This study aimed to develop Discord-based learning media as a digital platform for communication, collaboration, and student interaction in the learning process. The study employed a Research and Development (R&D) method using the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The developed product was organized as a structured Discord server containing channels for announcements, discussion, quizzes, assignment submission, and learning materials. The product was evaluated through expert validation and a limited trial involving students of Class XII 2 at SMAN 3 Polewali. Data were collected through questionnaires, supported by observation and interviews, and analyzed using descriptive qualitative and quantitative techniques based on a five-point Likert scale. The results showed that the developed media were considered feasible for learning use. Media expert validation yielded an average score of 4.2 (84%), categorized as good, while material expert validation yielded 4.6 (92%), categorized as very good. Student responses also indicated positive acceptance, with an average score of 4.02 (80.8%), categorized as good. The highest student rating was found in interest in the media, whereas ease of use received the lowest score. These findings imply that Discord has potential as an alternative digital learning medium that can support communication, collaboration, and student interaction, although technical guidance and accessibility support are needed to optimize its implementation in educational settings.

This article analyzes the implementation of participatory leadership practices in educational institutions and their implications for improving pedagogical quality. The main objective is to investigate the structural and cultural obstacles that interfere with the adoption of more horizontal management models, examining how the decentralization of decision-making contributes to strengthening educational processes. The study adopts a qualitative approach based on a bibliographic review conducted in indexed academic databases. The results indicate that the sharing of responsibilities among managers, teachers, and the school community strengthens the organizational climate, reduces institutional conflicts, and increases the sense of belonging among the actors involved. However, the persistence of centralized administrative practices and the high bureaucratic burden in public education systems still represent significant barriers to the consolidation of participatory processes. The study also shows that the effective functioning of collegiate bodies and the collective elaboration of institutional plans contribute to the continuity of pedagogical actions and to the stability of educational goals. Furthermore, collaborative interaction between schools, families, and the community strengthens social co-responsibility for education and supports student retention. It is concluded that sustainable institutional development depends on the consolidation of practices based on transparency, dialogue, and collective participation, transforming the school environment into a democratic space that promotes citizenship.

Background. The rapid expansion of digital media has transformed educational practices, encouraging the integration of multimedia platforms into teaching and learning processes. Podcasts and vlogs have emerged as accessible and engaging digital narrative formats that enable educators and students to communicate knowledge through audio-visual storytelling. Despite their growing popularity, systematic empirical examination of their effectiveness as pedagogical tools remains limited. Purpose. This study aims to explore the effectiveness of podcasts and vlogs as educational tools in enhancing student engagement and learning outcomes. Method. A mixed-methods research design was employed involving 72 undergraduate students divided into experimental and comparison groups. The experimental group participated in a six-week intervention integrating podcast and vlog production into course activities, while the comparison group received conventional instruction. Data were collected through engagement surveys, academic performance assessments, classroom observations, and semi-structured interviews. Results. Findings indicate that students engaged in podcast and vlog production demonstrated significantly higher levels of cognitive and emotional engagement, along with improved academic performance. Qualitative evidence revealed increased motivation, collaborative interaction, and enhanced communication skills. Conclusion. The study concludes that podcasts and vlogs function as effective digital narrative tools for enriching teaching practices and promoting meaningful learning in contemporary educational contexts.

The rise of generative artificial intelligence (AIGC) is observed in many fields. The key benefit of using the technology lies in the increase in efficiency and creativity. The current research aims to explore the mechanism of human-computer collaboration creativity stimulation in generative AI used for the generation of art design. Developing a single-scale generative adversarial network (GAN) called S_OpenGAN intended for human-computer cooperation, the intelligent generation of art creation images is achieved, and the SD method is applied to evaluate the art design creation results. Based on the experimental data, it is concluded that the developed algorithm generates high-quality images with a significant diversity even when there are insufficient amounts of data. Besides, the generated results outperform the ones generated using alternative approaches such as SNI, DAT, TransEditor, and StyleGAN-2. What is more, the inference and memory consumption of S_OpenGAN is less than that of competitive approaches on all platforms, allowing generating images with the help of fewer resources. In addition, the S_OpenGAN algorithm possesses the ability to change the preferences of observers through specific changes in image elements.

Robot swarms are composed of many, typically simple, robots that accomplish complex tasks through local communication and decentralized coordination. Traditionally, robot controllers are designed before a mission using programming code. This process requires substantial development effort and limits the flexibility of the swarm. We discuss how onboard foundation models (FMs) could revolutionize this process through two complementary approaches. The first approach uses FMs as swarm designers to synthesize robot controllers and perform high-level planning. The second approach uses FMs as swarm operators to facilitate robot-robot collaboration and human-swarm interaction.