بدائل البحث:
process optimization » model optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
library based » laboratory based (توسيع البحث)
based process » based processes (توسيع البحث), based probes (توسيع البحث), based proteins (توسيع البحث)
binary risk » primary risk (توسيع البحث), dietary risk (توسيع البحث)
process optimization » model optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
library based » laboratory based (توسيع البحث)
based process » based processes (توسيع البحث), based probes (توسيع البحث), based proteins (توسيع البحث)
binary risk » primary risk (توسيع البحث), dietary risk (توسيع البحث)
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An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows
منشور في 2025"…Experimental Methodology Framework Local Processing Pipeline Architecture Data Flow: Storage I/O → Memory Buffer → CPU/GPU Processing → Cache Coherency → Storage I/O ├── Input Vector: mmap() system call for zero-copy file access ├── Processing Engine: OpenMP parallelization with NUMA-aware thread affinity ├── Memory Management: Custom allocator with hugepage backing └── Output Vector: Direct I/O bypassing kernel page cache Cloud Processing Pipeline Architecture Data Flow: Local Storage → Network Stack → TLS Tunnel → CDN Edge → Origin Server → Processing Grid → Response Pipeline ├── Upload Phase: TCP window scaling with congestion control algorithms ├── Network Layer: Application-layer protocol with adaptive bitrate streaming ├── Server-side Processing: Containerized microservices on Kubernetes orchestration ├── Load Balancing: Consistent hashing with geographic affinity routing └── Download Phase: HTTP/2 multiplexing with server push optimization Dataset Schema and Semantic Structure Primary Data Vectors Field Data Type Semantic Meaning Measurement Unit test_type Categorical Processing paradigm identifier {local_processing, cloud_processing} photo_count Integer Cardinality of input asset vector Count avg_file_size_mb Float64 Mean per-asset storage footprint Mebibytes (2^20 bytes) total_volume_gb Float64 Aggregate data corpus size Gigabytes (10^9 bytes) processing_time_sec Integer Wall-clock execution duration Seconds (SI base unit) cpu_usage_watts Float64 Thermal design power consumption Watts (Joules/second) ram_usage_mb Integer Peak resident set size Mebibytes network_upload_mb Float64 Egress bandwidth utilization Mebibytes energy_consumption_kwh Float64 Cumulative energy expenditure Kilowatt-hours co2_equivalent_g Float64 Carbon footprint estimation Grams CO₂e test_date ISO8601 Temporal execution marker RFC 3339 format hardware_config String Node topology identifier Alphanumeric encoding Statistical Distribution Characteristics The dataset exhibits non-parametric distribution patterns with significant heteroscedasticity across computational load vectors. …"
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63
Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds
منشور في 2025"…The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). …"
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64
Aluminum alloy industrial materials defect
منشور في 2024"…</p><h2>Description of the data and file structure</h2><p dir="ltr">This is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.…"
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65
Code
منشور في 2025"…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…"
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66
Core data
منشور في 2025"…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…"
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67
Table 1_Advances in the application of human-machine collaboration in healthcare: insights from China.docx
منشور في 2025"…“Human–machine collaboration” is based on an intelligent algorithmic system that utilizes the complementary strengths of humans and machines for data exchange, task allocation, decision making and collaborative work to provide more decision support. …"
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68
<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)
منشور في 2025"…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…"