An example Python input file for the CA4 binder design. by Jakub Lála (17136205)

<b>FluxZayn: </b>An Extension for Stable Diffusion WebUI Forge 300 word overview by Umran Ali (3853630)

“…<p dir="ltr"><b>FluxZayn: </b>An Extension for Stable Diffusion WebUI Forge 300 word overview<br></p><p dir="ltr">This practice-led, auto-ethnographic case study documents the creation of FluxZayn, a Python-based extension for the Stable Diffusion WebUI Forge platform that enables one-click transparent PNG generation. …”

Warm-Rain Precipitation by Xiaojun Guan (22098905)

“…<p dir="ltr">(1) The RAR file named “vertical_profiles_of_polarimetric_variables” contains 9 .npy files generated using Python’s NumPy library. …”

MSc Personalised Medicine at Ulster University by Steven Watterson (100045)

“…Both full-time and part-time programmes have two intakes and can be started in September or January.…”

Dataset – Student & Early-Career Survey on Data-Analytics Tool Adoption and Decision-Making (Uzbekistan, Apr–May 2025) by Lev Radman (21381269)

“…</i> Items operationalise seven UTAUT/TAM-based constructs: Performance Expectancy, Effort Expectancy, Behavioural Intention, Familiarity & Usage, Task–Technology Fit, Barriers to Adoption, plus Demographics (age, gender, study programme, prior stats courses, work experience). All Likert items use a five-point scale.…”

Variation of the low-mass end of the stellar initial mass function with redshift and metallicity (dataset) by Matthew R. Bate (21823244)

“…The P-files can be read using the Fortran programme included in the repository: ptmass_simple_MPI_2.f . …”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…This dataset was compiled and analyzed to:</p><p><br></p><ol><li>Characterize the air quality profile during the winter (Dec 2016–Mar 2017) immediately preceding and overlapping with the initial phase of the U/As exposure event.…”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…Betalain content increased sharply in non-reproductive ramets during extreme abiotic conditions in summer and during senescence in reproductive ramets. …”

Nucleotide analogue tolerant synthetic RdRp mutant construct for Surveillance and Therapeutic Resistance Monitoring in SARS-CoV-2 by Tahir Bhatti (20961974)

“…Biopython: Freely available Python tools for computational molecular biology and bioinformatics. …”

Landscape17 by Vlad Carare (22092515)

“…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10⁵CPUh. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Energy consumption metrics demonstrate polynomial scaling relationships (R² > 0.97) with respect to input cardinality, following power-law distributions characteristic of complex computational systems. Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”