بدائل البحث:
algorithm catenin » algorithm within (توسيع البحث)
catenin function » catenin functional (توسيع البحث), cohesin function (توسيع البحث), hardening function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm etc » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithm a (توسيع البحث)
etc function » spc function (توسيع البحث), fc function (توسيع البحث), npc function (توسيع البحث)
algorithm catenin » algorithm within (توسيع البحث)
catenin function » catenin functional (توسيع البحث), cohesin function (توسيع البحث), hardening function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm etc » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithm a (توسيع البحث)
etc function » spc function (توسيع البحث), fc function (توسيع البحث), npc function (توسيع البحث)
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161
Landscape17
منشور في 2025"…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10<sup>5 </sup>CPUh. …"
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162
MCCN Case Study 2 - Spatial projection via modelled data
منشور في 2025"…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…"
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163
Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf
منشور في 2025"…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …"
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164
Pressure control techniques in freeze-drying
منشور في 2025"…The most common Pressure control techniques would be listed as follows:</p><ul><li>PID method</li><li>Fuzzy logic</li><li>Max pressure algorithms</li><li>Reinforcement learning</li><li>Adaptive control</li><li>Setpoint profile tracking (Bang-bang control)</li></ul><p dir="ltr">Pressure control systems have to perform a particular task in the target process considering some key functionalities like: system dynamism, control performance, stability, adaptability, accuracy, etc. …"
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165
a. How various statistical models account for modulation classification performance across the entire dataset.
منشور في 2025"…Parameters are <i>Type:</i> neuron classification (primary-like, sustained chopper, etc.); <i>CV:</i> Coefficient of variation of the interspike intervals in response to a pure tone at CF. …"
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166
<b>Drug Release Nanoparticle Systems Design:</b><b>Dataset Compilation and Machine Learning Modeling</b>
منشور في 2024"…Herein 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …"
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167
Data Sheet 1_CDK1 may promote breast cancer progression through AKT activation and immune modulation.docx
منشور في 2025"…This study aimed to comprehensively evaluate CDK1 expression, prognostic value, and biological functions in breast cancer through integrated bioinformatics and experimental analyses.…"
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168
An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows
منشور في 2025"…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). …"