The inherent characteristics of Generation Z with digital devices trigger moral degradation and ethical decline due to unfiltered digital information flows. This condition requires educational institutions to design science learning strategies that integrate technological skills with noble character cultivation. This qualitative case study aims to describe the planning of digital-based chemistry learning in strengthening the morals of Generation Z students at SMA Muhammadiyah 13 Jakarta. Data collection was conducted through in-depth interviews with the principal, vice principal for curriculum, chemistry teachers, and students, reinforced by classroom observations and instructional module documentation. Data analysis proceeded interactively through data reduction, data display, and conclusion drawing. The findings revealed that digital chemistry learning planning utilized Google Classroom, PhET simulations, and Islamic value-based educational videos, yet remained confined to individual teacher initiatives without formal institutional curriculum standard operating procedures. Moral reinforcement was executed by internalizing digital etiquette, scientific integrity, and virtual discipline. Integrated policy synchronization and teacher competency development are essential for effectively solidifying student character. ABSTRAK Karakteristik Generasi Z yang lekat dengan gawai memicu krisis degradasi moral dan penurunan etika akibat masifnya arus informasi digital tanpa penyaring nilai. Kondisi tersebut menuntut institusi pendidikan merancang strategi pembelajaran sains yang mengintegrasikan kecakapan teknologi dengan penanaman karakter luhur. Penelitian kualitatif studi kasus ini bertujuan mendeskripsikan perencanaan pembelajaran kimia berbasis digital dalam memperkuat moral peserta didik Generasi Z di SMA Muhammadiyah 13 Jakarta. Pengumpulan data dilakukan melalui wawancara mendalam terhadap kepala sekolah, wakil kurikulum, guru kimia, dan siswa, diperkuat observasi pembelajaran serta dokumentasi modul ajar. Analisis data berlangsung secara interaktif melalui reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian mengungkap bahwa perencanaan pembelajaran kimia digital telah memanfaatkan Google Classroom, simulasi PhET, dan video edukasi berbasis nilai Islami, namun perencanaannya masih terbatas pada inisiatif mandiri guru serta belum dibakukan dalam dokumen kurikulum operasional sekolah. Penguatan moral dilakukan lewat internalisasi adab digital, kejujuran ilmiah, dan kedisiplinan daring. Sinkronisasi kebijakan terpadu dan pembinaan kompetensi guru mutlak diperlukan agar digitalisasi pembelajaran sains efektif mengokohkan karakter peserta didik.
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Minimum viable product (MVP) is the main focus of both business and product development activities in software startups. We empirically explored five early stage software startups to understand how MVP are used in early stages. Data was collected from interviews, observation and documents. We looked at the MVP usage from two angles, software prototyping and boundary spanning theory. We found that roles of MVPs in startups were not fully aware by entrepreneurs. Besides supporting validated learning, MVPs are used to facilitate product design, to bridge communication gaps and to facilitate cost-effective product development activities. Entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP). The work also implies several research directions about prototyping practices and patterns in software startups.
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Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations
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