بدائل البحث:
driven optimization » guided optimization (توسيع البحث), dose optimization (توسيع البحث), process optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
binary task » binary mask (توسيع البحث)
task driven » task derived (توسيع البحث), mapk driven (توسيع البحث), state driven (توسيع البحث)
primary co » primary pci (توسيع البحث), primary _ (توسيع البحث), primary pm (توسيع البحث)
co design » _ design (توسيع البحث)
driven optimization » guided optimization (توسيع البحث), dose optimization (توسيع البحث), process optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
binary task » binary mask (توسيع البحث)
task driven » task derived (توسيع البحث), mapk driven (توسيع البحث), state driven (توسيع البحث)
primary co » primary pci (توسيع البحث), primary _ (توسيع البحث), primary pm (توسيع البحث)
co design » _ design (توسيع البحث)
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Thesis-RAMIS-Figs_Slides
منشور في 2024"…In the context of facies recovery using simulations, the task of optimal sampling is formalized and addressed using a maximum information extraction criterion. …"
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2
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
منشور في 2024"…The DLCM is an advanced and comprehensive tool designed to simulate SOC dynamics for both the top 20 cm layer (SOC20) and the deeper 20–100 cm layer (SOC20–100). …"
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3
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. …"