zLanqing

Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Debugging MATLAB signal-processing scripts
• Analyzing sensor time series in Python
• Checking units and assumptions in optical simulations

Turn papers and thesis materials into Chinese-first Word reports and PowerPoint presentations. Create editable files, preserve source evidence, match supplied templates, add speaker notes, and check layouts for overflow, overlap, and readability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a paper into a Chinese reading report
• Prepare slides for a lab meeting or thesis defense
• Adapt research content to a supplied slide template

Create statistical charts from tabular data to explore distributions, relationships, and group differences. This skill guides plot selection, multi-panel layouts, and styling, including box plots, heatmaps, and charts with estimates and uncertainty bands.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring data distributions and unusual values
• Comparing measurements across groups
• Visualizing correlations between variables

Explore design options in Python when goals such as cost and performance compete. This skill guides optimization, constraint handling, and visualization of trade-offs, then helps compare candidate solutions using stated preferences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing designs with competing cost and performance goals
• Finding solutions that meet design constraints
• Comparing optimization algorithms on benchmark problems

Simulate quantum systems in Python with QuTiP, including open systems that interact with their surroundings. Calculate time evolution, energy loss, and entanglement, then visualize states and results for physics research or educational experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Simulate the decay of a harmonic oscillator
• Track changes in two-qubit entanglement
• Study interactions between an atom and a cavity

Prepare scientific figures for journal submission with consistent panel layouts, error bars showing uncertainty, statistical significance labels, and accessible colors. Use matplotlib, seaborn, or plotly to adjust figure dimensions and export publication formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing figures for journal submission
• Combining multiple plots with consistent styling
• Making figures readable in color and grayscale

Search ten academic databases for papers, preprints, author publications, and citation links. Look up specific papers by DOI, a paper identifier, and find abstracts, available full text, or openly accessible PDF links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find research papers on a specific topic
• Look up a paper by DOI or PMID
• Find openly accessible full text and PDFs

Search multiple academic databases, screen studies against documented criteria, and compare findings by theme. Produce literature reviews in Markdown and PDF with study quality assessments, verified citation details, and diagrams explaining the review process or findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Conduct a systematic literature review
• Prepare a literature review section for a thesis
• Compare findings and disagreements across studies

Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Interpret regression coefficients and confidence intervals
• Model binary outcomes or event counts
• Forecast time series with uncertainty intervals

Create and edit spreadsheet files, clean messy tables, and convert tabular formats. Add formulas, formatting, and charts while following existing template styles, then recalculate formulas and check for errors before delivering the spreadsheet.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean messy CSV data into a spreadsheet
• Add formulas and charts to an Excel workbook
• Update a workbook using its existing formatting

Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Analyzing experimental CSV data and saving plots
• Solving matrix equations and differential equations
• Analyzing signal frequencies and applying filters

Model queues, production lines, logistics, and other systems that share limited resources in Python. Use discrete-event simulation, which advances through individual events, to measure waiting times, resource use, and throughput when comparing staffing or equipment levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing queue times for different staffing levels
• Examining production line bottlenecks
• Testing inventory and logistics flows

Extract text, tables, and images from PDFs, combine or split documents, rotate pages, fill forms, and create new files. It also covers password protection and OCR, which recognizes text in scanned pages for searching and reuse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting text and tables from documents
• Combining PDFs or splitting out selected pages
• Recognizing text in scanned documents

Process astronomical data in Python with Astropy. This skill covers celestial coordinate, unit, and time conversions; reading and editing FITS astronomy files; matching object catalogs; and calculating cosmic distances using cosmological models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting celestial coordinates for an observing time and location
• Reading FITS images and inspecting their headers
• Matching objects across astronomical catalogs

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Use SymPy in Python to solve equations, simplify formulas, calculate derivatives and integrals, and work with matrices. Keep exact results such as fractions and square roots, then turn expressions into executable code or LaTeX.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Solve equations and verify the solutions
• Calculate derivatives, integrals, and limits
• Find matrix eigenvalues and inverse matrices

Create charts in Python with detailed control over colors, axes, labels, legends, and layouts. Use it to build scientific figures, arrange multiple plots, and export results as PNG, PDF, or SVG files for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating figures for papers and presentations
• Arranging multiple charts in one figure
• Adjusting colors, axes, labels, and legends

Draft and revise research papers, thesis sections, and responses to reviewers, primarily in Chinese or in English on request. Organize arguments around supplied evidence while preserving formulas, citations, terminology, and measured results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting abstracts and introductions from research notes
• Improving logic and wording in thesis manuscripts
• Preparing responses to peer reviewers

Forecast sales, sensor readings, or other time series without training a custom model. Using Google's TimesFM, this skill produces forecasts and uncertainty ranges from CSV files or arrays, supports batch processing, and checks local computing resources before use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting monthly sales from a CSV file
• Forecasting demand across multiple stores
• Plotting sensor forecasts with uncertainty ranges

Analyze relationships between people, objects, or papers as networks in Python. Use NetworkX to find shortest paths, identify important nodes and communities, visualize connections, exchange graph data, and generate sample networks for testing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Visualizing social relationships or paper citation networks
• Finding shortest paths through a network
• Identifying important nodes and closely connected groups

Work with crystal structures in Python: convert files, analyze symmetry, assess material stability, and examine electronic properties. Retrieve Materials Project data and prepare input files for materials calculations, with scripts for common analysis tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert crystal structure files between CIF and POSCAR
• Analyze crystal symmetry and neighboring atoms
• Build phase diagrams and assess material stability

Build Python machine learning workflows for classification, numerical prediction, and grouping similar records. Use scikit-learn guidance and examples to prepare data, compare algorithms, tune model settings, and evaluate results with repeatable processing steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building classification and numerical prediction models
• Grouping customers by similar characteristics
• Preparing data with missing values and categorical fields

Find academic papers through Google Scholar and PubMed, retrieve publication details from identifiers, and build BibTeX reference files. Check author and publication information, detect duplicates, and standardize formatting before using references in a manuscript.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding papers on a research topic
• Converting DOI lists into BibTeX references
• Checking reference details before manuscript submission

Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting an unfamiliar scientific data file
• Checking missing values and duplicates in experimental tables
• Reviewing sequence lengths and quality scores in FASTQ files