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All Journal Al-Hijr: Journal of Adulearn World Journal of Computer Science Advancements Research Psychologie, Orientation et Conseil Techno Agriculturae Studium of Research Journal of Biomedical and Techno Nanomaterials
Liu Yang
Shanghai Jiao Tong University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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BUILDING POSITIVE RELATIONSHIPS: THE ROLE OF EMPATHY AND COMPASSION IN PERSONAL GROWTH Liu Yang; Chen Mei; Ethan Tan
Research Psychologie, Orientation et Conseil Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v3i2.3740

Abstract

In today’s increasingly interconnected and diverse world, the ability to build positive relationships is essential for both personal and professional success. Empathy and compassion are two core interpersonal skills that contribute to stronger connections and greater well-being. These qualities not only enhance relationships but also promote personal growth by fostering understanding, emotional resilience, and a sense of shared humanity. However, the specific role of empathy and compassion in personal development remains underexplored. This study aims to examine the role of empathy and compassion in building positive relationships and promoting personal growth. The research investigates how these two traits influence emotional intelligence, self-awareness, and interpersonal communication in diverse contexts. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews. Data were collected from 250 participants across various age groups and professions. The findings revealed that higher levels of empathy and compassion were associated with improved interpersonal relationships and greater personal growth. Participants who reported stronger empathy and compassion exhibited better emotional regulation, higher self-awareness, and more positive social interactions. This study concludes that empathy and compassion are integral to building positive relationships and fostering personal growth.
REAL-TIME LEARNING ANALYTICS: THE ROLE OF AI IN MONITORING STUDENT PROGRESS Marten Mahadjani; Li Wei; Liu Yang
Al-Hijr: Journal of Adulearn World Vol. 5 No. 2 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i2.1269

Abstract

The integration of Artificial Intelligence (AI) in education has significantly transformed how student progress is monitored in real-time, offering valuable insights into individual learning trajectories. Real-time learning analytics powered by AI provide educators with the ability to track and assess students’ performance continuously, facilitating timely interventions and personalized learning experiences. Despite the potential of AI to enhance educational outcomes, its impact on the overall teacher-student dynamic and the challenges associated with its integration into traditional pedagogical frameworks remain underexplored. This study aims to investigate the role of AI in real-time learning analytics and its effect on monitoring student progress, exploring both its benefits and limitations. The research employs a mixed-methods approach, combining quantitative surveys, qualitative interviews, and classroom observations across 10 educational institutions utilizing AI-powered learning tools. The results indicate that AI tools significantly improve student engagement, performance, and the timeliness of feedback, but concerns about the depersonalization of interactions were also raised by both students and teachers. The study concludes that while AI can enhance the monitoring of student progress, it must be integrated in a way that preserves the human aspects of teaching. AI should complement, not replace, the teacher's role in providing emotional and social support in the learning process.
GREENHOUSE TECHNOLOGY INNOVATIONS FOR SUSTAINABLE AGRICULTURE IN THE UNITED KINGDOM Zhang Li; Yang Xiang; Liu Yang; Ardi Azhar Nampira
Techno Agriculturae Studium of Research Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i1.1993

Abstract

Greenhouse technology is an important innovation in facing the challenges of sustainable agriculture in the UK, especially in the face of climate change and increasing food needs. This research aims to explore the application of advanced technologies in greenhouses, such as automation sensors, hydroponics, aquaponics, and renewable energy, as well as their impact on agricultural productivity and sustainability. Descriptive-qualitative research methods are used to gain insights from farmers and experts in the field of agricultural technology, through interviews and direct observations. The results showed a significant improvement in resource use efficiency, with a reduction in water use of up to 50% and an increase in crop yields of up to 30%. The adoption of renewable energy in greenhouses also plays a role in reducing carbon emissions and operational costs. In conclusion, greenhouse technology innovation has the potential to be an important solution to achieving sustainable agriculture in the UK, but more research is needed to evaluate the long-term impact on the environment.
THE USE OF MULTISPECTRAL DRONE IMAGERY AND ARTIFICIAL INTELLIGENCE FOR THE EARLY DETECTION OF LEAF BLIGHT DISEASE IN INDONESIAN RICE PADDIES Sun Wei; Wang Jun; Liu Yang
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2961

Abstract

Leaf blight disease remains one of the major threats to rice production in Indonesia, causing significant yield losses and threatening national food security. Conventional detection methods rely heavily on manual field inspection, which is time-consuming, labor-intensive, and often ineffective for early-stage identification. Recent advances in multispectral drone imagery and artificial intelligence (AI) offer new opportunities for precision agriculture by enabling rapid, accurate, and large-scale crop health monitoring. However, the practical application of these technologies in Indonesian rice paddies is still limited and requires empirical validation. This study aims to examine the effectiveness of multispectral drone imagery integrated with AI-based classification models for the early detection of leaf blight disease in Indonesian rice fields. The research focuses on improving detection accuracy and supporting timely disease management decisions for farmers and agricultural stakeholders. The study employs an experimental research design using multispectral drone data collected from rice paddies in West Java during the growing season. Vegetation indices such as NDVI and GNDVI were extracted and analyzed using machine learning algorithms, including Random Forest and Convolutional Neural Networks (CNN). Ground truth data were obtained through field observations and laboratory confirmation to validate the model outputs. The results demonstrate that the AI-based model achieved high classification accuracy, exceeding 90% in detecting early-stage leaf blight symptoms. The integration of multispectral data significantly improved detection performance compared to visual RGB imagery alone. The study concludes that multispectral drone imagery combined with AI provides a reliable and efficient approach for early detection of leaf blight disease in rice paddies. This approach has strong potential to support precision agriculture, reduce crop losses, and enhance sustainable rice production in Indonesia.
QUANTUM ADVANTAGE HAS ARRIVED: TANGIBLE IMPACTS ON DRUG DISCOVERY AND NEW MATERIALS Li Wei; Zhou Hui; Liu Yang
Journal of Computer Science Advancements Vol. 3 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i5.3325

Abstract

The advancement of computational chemistry is currently stalled by the exponential memory scaling required to simulate strongly correlated electron systems on classical supercomputers. This fundamental barrier significantly impedes the rational design of complex pharmaceuticals and next-generation catalytic materials. This research aims to rigorously validate the immediate utility of Noisy Intermediate-Scale Quantum (NISQ) processors, demonstrating that “Quantum Advantage” has shifted from a theoretical milestone to a practical industrial reality. We employed a comparative research design utilizing the Variational Quantum Eigensolver (VQE) algorithm on the IBM Eagle quantum processor. The study targeted the electronic structure of iron-sulfur clusters and KRAS-G12C inhibitor binding sites, benchmarking quantum outputs against classical Density Functional Theory (DFT) and Full Configuration Interaction (FCI) standards, utilizing Zero-Noise Extrapolation for error mitigation. Results indicate that quantum simulations achieved chemical accuracy (within 1.6 kcal/mol) for these complex systems, whereas classical methods failed with deviations exceeding 8 kcal/mol. The data confirms that quantum hardware can now resolve electronic correlations invisible to classical approximation. We conclude that quantum computing offers a tangible, immediate pathway to accelerate discovery cycles in drug development and material science, necessitating the integration of hybrid quantum workflows into modern R&D pipelines.
Surface Modification of Gold Nanoparticles to Improve Cancer Cell Targeting Chen Mei; Wang Jing; Liu Yang
Journal of Biomedical and Techno Nanomaterials Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v1i4.1809

Abstract

Gold nanoparticles (AuNPs) are promising agents for cancer therapy due to their unique properties, but effective targeting remains a challenge. Surface modification with specific ligands can enhance targeting efficiency. To develop and optimize surface-modified AuNPs to improve targeting of cancer cells, enhancing therapeutic outcomes while minimizing side effects. The study employed theoretical modeling, laboratory experiments, and in vivo testing. Cancer cell lines (MCF-7, A549, PC-3) and mouse models with human tumors were used to evaluate targeting efficiency. Instruments included TEM, SEM, DLS, zeta potential analysis, and HPLC. Surface-modified AuNPs showed an 80% increase in cancer cell binding compared to unmodified AuNPs. In vivo studies demonstrated a 70% reduction in tumor volume in treated mice. Stability tests indicated consistent performance under various biological conditions. Surface modification of AuNPs with specific ligands significantly enhances their targeting ability and therapeutic efficacy against cancer cells. Further clinical trials are necessary to validate these findings for clinical application.  
Co-Authors Ardi Azhar Nampira Chen Mei Ethan Tan Li Wei Marten Mahadjani Sun Wei Wang Jing Wang Jun Yang Xiang Zhang Li Zhou Hui