๐Ÿ† Most Liked Article Award


 ๐Ÿ† Most Liked Article Award celebrates research articles that strongly resonate with readers, earning exceptional appreciation through likes, shares, and positive engagement across academic and professional platforms.

๐Ÿ“š This award reflects the power of clear ideas, impactful findings, and meaningful communication that connects research with a wide audience beyond traditional citations.

๐ŸŒ By recognizing reader-driven impact, the award highlights scholarly work that inspires discussion, visibility, and real-world relevance within the global research community.

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๐Ÿ“˜๐ŸขActivity Theory Framework for Workplace Learning with Generative AI ๐Ÿค–


 Activity Theory provides a strong analytical lens to study workplace learning technologies ๐Ÿข๐Ÿ“˜. It emphasizes the dynamic relationships between people, tools, tasks, and organizational rules, helping explain how learning naturally emerges through everyday work activities.

Generative AI conversational agents ๐Ÿค–๐Ÿ’ฌ are reshaping workplace learning by acting as intelligent assistants. They enable real-time problem-solving, personalized support, and continuous knowledge access, allowing employees to learn while performing tasks more efficiently ⚙️✨.

Applying Activity Theory to generative AI reveals both benefits and tensions ๐Ÿ”๐Ÿš€. It uncovers how AI influences collaboration, responsibility, and learning outcomes, guiding organizations to implement AI-driven learning tools that are ethical, effective, and human-centered ๐Ÿค๐Ÿ“š.

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๐Ÿง  Challenging Clinical Interactions & Coping Strategies in Health Students ๐Ÿฉบ

Clinical education is a vital yet challenging phase for health professions students ๐Ÿฉบ๐ŸŽ“. This systematic review explores how medical radiation undergraduates and other healthcare students experience difficult clinical interactions, including communication barriers, emotionally distressed patients, and high-pressure environments ๐Ÿ˜“๐Ÿค. These challenges often influence confidence, learning outcomes, and overall well-being during clinical placements.


The review highlights both shared and discipline-specific stressors across health programs ๐Ÿ“Š๐Ÿฅ. Medical radiation students frequently face technical responsibility alongside patient care, while nursing, medical, and allied health students encounter complex interpersonal and ethical situations ๐Ÿ’ฌ⚖️. Despite these differences, emotional stress, fear of making mistakes, and managing patient expectations remain common concerns.

To cope, students adopt a range of strategies focused on resilience and support ๐ŸŒฑ๐Ÿ’ช. Peer discussions, mentorship, reflective journaling, and institutional support systems play a crucial role ๐Ÿค—๐Ÿ“˜. The findings emphasize the importance of structured coping skill training and supportive clinical environments to better prepare future healthcare professionals for real-world practice ✨๐Ÿฉป.

Global Scholar Awards ๐ŸŒŸ Visit Our Website ๐ŸŒ: globalscholarawards.com Nominate Now๐Ÿ‘: https://globalscholarawards.com/doctor-awards-nobel-prize-scientists-award-nomination/?ecategory=Awards&rcategory=Awardee Contact us ✉️: contact@globalscholarawards.com

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๐Ÿ“ŠResearch Status and Bibliometric Analysis of High-Speed On/Off Valve Control Technology ⚙️

High-speed on/off valves play a vital role in modern hydraulic and pneumatic systems, offering fast response, high precision, and energy-efficient control ๐Ÿš€. Recent research focuses on improving switching speed, reducing flow ripple, enhancing durability, and integrating intelligent control algorithms for advanced automation ๐Ÿค–๐Ÿ”ง.


From a research status perspective, scholars are actively exploring novel valve structures, electromagnetic drive optimization, and digital control strategies to meet industrial demands ๐Ÿญ. High-speed on/off valves are now widely applied in robotics, aerospace, automotive systems, and smart manufacturing, driving innovation across multiple engineering domains ๐ŸŒ✨.

Bibliometric analysis reveals rapid growth in publications, strong interdisciplinary collaboration, and increasing citations in control engineering and fluid power research ๐Ÿ“ˆ. Emerging hotspots include AI-based control, energy-saving design, and fault diagnosis, indicating promising future directions for this evolving technology ๐Ÿ”⚡.


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๐Ÿงญ High-Sensitivity Planar Capacitive Sensors for Multidirectional Force Detection ⚡๐Ÿ“

High-sensitive planar capacitive sensors for multidirectional force detection are designed to accurately sense forces applied from different directions by monitoring tiny variations in capacitance ๐Ÿ”. Their planar architecture allows uniform stress distribution, resulting in enhanced sensitivity and stable signal output even under low-force conditions ⚙️๐Ÿ“.

These sensors offer fast response time, high resolution, and excellent repeatability, making them suitable for applications that require precise force discrimination ⚡๐Ÿ“Š. The compact and lightweight design enables easy integration into flexible electronics, MEMS devices, and smart systems without adding bulk ๐Ÿงฉ.

Widely used in robotics, wearable technology, biomedical devices, and human–machine interaction systems ๐Ÿค–๐Ÿ–️, planar capacitive sensors provide low power consumption and reliable multidirectional force feedback ๐Ÿ”‹. Their ability to detect subtle force changes improves control accuracy, safety, and overall system intelligence ๐ŸŒ✨.


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๐Ÿค– Machine Learning for Heavy Rare-Earth Cooling Materials ๐ŸงŠ

Machine learning is transforming the study of heavy rare earth–based low-temperature magnetocaloric materials by enabling intelligent analysis of complex magnetic and thermal behaviors ❄️๐Ÿงฒ๐Ÿค–. These materials are crucial for next-generation solid-state refrigeration, and ML helps uncover hidden patterns within large experimental datasets that traditional methods often miss ๐Ÿ“Š๐Ÿ”.


Advanced classification algorithms such as neural networks, support vector machines, and ensemble models efficiently categorize materials based on entropy change, magnetic transition, and crystal structure ๐Ÿง ⚙️. By learning from multidimensional features, ML improves accuracy in predicting performance and stability under low-temperature conditions ๐Ÿš€๐Ÿ”ฌ.

This data-driven approach accelerates material discovery, reduces experimental cost, and supports sustainable cooling technologies ๐ŸŒฑ✨. Integrating machine learning with magnetocaloric research opens new pathways for designing energy-efficient refrigeration systems and advancing smart materials science in the era of AI ๐ŸŒ๐Ÿค–❄️.

Global Scholar Awards ๐ŸŒŸ Visit Our Website ๐ŸŒ: globalscholarawards.com Nominate Now๐Ÿ‘: https://globalscholarawards.com/doctor-awards-nobel-prize-scientists-award-nomination/?ecategory=Awards&rcategory=Awardee Contact us ✉️: contact@globalscholarawards.com

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๐Ÿ”ฌ❄️ Physics-Informed ML Framework for Predicting Superconducting Tc ๐Ÿš€๐Ÿง 

๐Ÿ”ฌ❄️ Superconductivity remains one of the most fascinating phenomena in modern physics, promising lossless energy transmission, powerful quantum devices, and next-generation electronics. However, predicting superconducting transition temperatures (Tc) across diverse materials is challenging due to complex underlying physics. Physics-informed machine learning bridges this gap by embedding physical laws directly into data-driven models, ensuring predictions that are not only accurate but also physically meaningful ๐Ÿง ⚡



๐Ÿค–๐Ÿ“Š By unifying experimental data with theoretical constraints, this framework leverages material descriptors, electronic structures, and thermodynamic principles to learn universal patterns governing superconductivity. Unlike black-box AI models, physics-guided approaches improve interpretability, reduce overfitting, and enhance generalization across material classes. This synergy allows researchers to explore vast chemical spaces efficiently while maintaining trust in the predictions ๐Ÿ”✨

๐Ÿš€๐Ÿ”ฎ The impact of this approach extends beyond prediction, accelerating the discovery of high-Tc superconductors and guiding experimental design. With faster screening, reduced trial-and-error, and deeper physical insight, physics-informed machine learning marks a transformative step toward intelligent materials discovery, paving the way for future energy-efficient and quantum technologies ๐ŸŒ⚙️

International Research Awards on New Science Inventions๐ŸŒŸ

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๐Ÿ† Most Liked Article Award

 ๐Ÿ† Most Liked Article Award celebrates research articles that strongly resonate with readers, earning exceptional appreciation through lik...

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