Image 10_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Data Sheet 4_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.xlsx by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 1_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 5_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 15_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 4_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 2_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Image 3_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.pdf by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Data Sheet 1_Type VII secretion system gene mutations driving global mycobacterium tuberculosis transmission revealed by whole genomic sequence.xlsx by Jian-Jun Yang (6013610)

“…The intersection of these two independent methods was recognized as the optimal set of risk mutations for TB transmission. …”

Visual Poster | Machiavellian Marketing: The Necessary Evolution of Persuasion, Power, and Digital Propaganda by Hadrian Stone (22244185)

“…It provides a conceptual map of the mechanisms that govern digital influence and demonstrates why the future of marketing depends on the architecting of belief, not the optimization of messaging.</p><p><br></p><p dir="ltr">This poster contributes to ongoing research in:</p><ul><li>behavioral economics</li><li>digital persuasion</li><li>narrative theory</li><li>platform psychology</li><li>propaganda studies</li><li>marketing strategy</li></ul><p><br></p><p dir="ltr"><b>Machiavellian Marketing</b> is presented not as a metaphor or stylistic approach, but as an emergent field: a school of thought for understanding how perception, narrative, and psychological power determine outcomes in a digital market defined by algorithms, scarcity of attention, and narrative warfare.…”

Data Sheet 1_Decoding the association between health level and human settlements environment: a machine learning-driven provincial analysis in China.zip by Haidong Zhu (103585)

“…PDP plots further revealed that optimal health levels are typically associated with high UR, high MPPR, high RAPC, and moderate NMIB, underscoring the importance of structural synergy over isolated infrastructure expansion.…”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by 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. …”

<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)

“…Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. …”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><p dir="ltr"><b>Quality Assurance</b>: Comprehensive technical validation demonstrates the dataset's integrity, sensitivity to rainfall impacts, and capability to reveal complex traffic-weather interaction patterns.</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”

Scalable software infrastructures for knowledge graphs by Ariful Azad (21773021)

“…The second layer implements highly optimized parallel algorithms for these operations. …”

Higher-Order Accurate Two-Sample Network Inference and Network Hashing by Meijia Shao (20543225)

“…Our method outperforms existing tools in speed and accuracy, and it is proved power-optimal. Our algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). …”

Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

“…These conventional systems are limited by their inability to capture multi-modal clinical inputs, susceptibility to diagnostic ambiguity, and lack of structured integration between exertional physiology and latent cardiovascular risk.…”

Table 1_Augmented intelligence with voice assistance and automated machine learning in Industry 5.0.docx by Alexandros Bousdekis (20821778)

“…<p>Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. …”

Research data (2013-2023) for bank study by Haibo Wang (17822606)

“…SLHCs from 2013 to 2023, constructs intercompany funding and exposure networks using intercompany assets and liabilities data. Two algorithms are utilized to extract core-periphery network features, assessing optimal linkages and disruptions from external shocks. …”

<b>FCG-MFD: Benchmark Function Call Graph-Based Dataset for Malware Family Detection</b> by Hassan jalil hadi (18243805)

“…Our dataset has two sub-datasets (FCG & Metadata) (1,00,000 samples) from VirusSamples, Virusshare, VirusSign, theZoo, Vx-underground, and MalwareBazaar curated using FCGs and metadata to optimize the efficacy of ML algorithms. We suggest a new malware analysis technique using FCGs and graph embedding networks, offering a solution to the complexity of feature engineering in ML-based malware analysis. …”