Table_1_“What About Automated Pain Recognition for Routine Clinical Use?” A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance.docx حسب Steffen Walter (814796)

PGAE-ICA_A simplified digital system for intellectual measurement-assessment in children and adolescents using cognitive testing and machine learning techniques حسب Runzhou Wang (5894849)

"…The present study aims to develop an intelligence measurement-assessment system based on primary cognitive ability tests across four cognitive domains and a machine learning model to distinguish between normal and abnormal intelligence. …"

Image 1_Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.jpeg حسب Pu Zhou (3957440)

"…</p>Conclusion<p>The GBM model based on ultrasound radiomics features and routine clinical date of BC patients had high performance in predicting pCR. …"

datasheet1_Evaluation of a Multidisciplinary Antimicrobial Stewardship Program in a Saudi Critical Care Unit: A Quasi-Experimental Study.docx حسب Abdul Haseeb (5830565)

"…</p><p>Conclusion: Overall, involvement of higher administration in multidisciplinary ASP committees, daily audit and feedback by clinical pharmacists and physicians with infectious disease training, continuous educational activities about antimicrobial use and resistance, use of local antimicrobial prescribing guidelines based on up-to-date antibiogram, and support from the intensive care team can optimize antibiotic use in Saudi healthcare institutions.…"

Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Table_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Image_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Image_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Image_6_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Table_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

Image_4_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF حسب Joshua A. Krachman (11660266)

"…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…"

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

"…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. …"