Hospital (A) accesses the B-UMCS. حسب Albatoul Almohana (20370514)

"…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …"

Hospital (A) sends the CR to Hospital (C). حسب Albatoul Almohana (20370514)

"…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …"

MOH reviews all hospital requests. حسب Albatoul Almohana (20370514)

"…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …"

Hospital (A) views the received messages. حسب Albatoul Almohana (20370514)

"…Most existing works utilized blockchain technology within a specific healthcare entity’s services but not among several healthcare organizations. …"

Development and characterization of a cell-type-specific gene expression reference panel from single-nucleus RNA sequencing (snRNAseq). حسب Brian Gural (21781982)

S1 Graphical abstract - حسب José M. Rivera-Arbeláez (12418512)

"…<div><p>Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart <i>in vitro</i>. …"

A Theory of Ultrafast Charge Transfer Relaxation with Non-Innocent Solvent Molecules حسب Vishikh Athavale (12623809)

"…Using a novel black-box electronic structure algorithm (time-dependent density functional theory with one double, TDDFT-1D) to efficiently run molecular dynamics that can treat charge recombination, we run ab initio surface hopping molecular dynamics and confirm that, for a polar solvent, charge recombination rates can be incredibly fast (indeed faster than the solvent relaxation time); for nonpolar solvents, the rate is much slower. …"

3D analysis of the perforated pebbles and the perforations. حسب Talia Yashuv (12883867)

Significantly enriched Gene Ontology (GO) terms among the genes showing significant results in the ancestry-specific TWAS. حسب Jenna C. Carlson (3950024)

"…P-values were adjusted to maintain an experiment-wide type 1 error rate of 0.05 within ancestry group using the g:SCS algorithm which accounts for the dependent structure of functionally annotated gene sets.…"

Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification حسب Vaibhavi Patel (19843282)

"…Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. …"

Intersection gene screening. حسب Qingqing Xue (5152622)

Presentation of the DySCo framework. حسب Giuseppe de Alteriis (20846230)

"…<p>A: What is dynamic Functional Connectivity: i) We can start from any set of brain recordings, where each signal is referred to a brain location (e.g. fMRI, EEG, intracranial recordings in rodents, and more). ii) “Static” Functional Connectivity (FC) is a matrix where each entry is a time aggregated functional measure of interaction between two regions, for example, the Pearson Correlation Coefficient. iii) Dynamic Functional Connectivity (dFC) is a FC matrix (that can be calculated in different ways, see below) that changes with time, under the assumption that patterns of brain interactions are non-stationary. …"

Daily expression of core clock genes in wild-type and <i>Clk</i>^<i>Δ</i>^<i>29</i> heads under 12:12 LD conditions. حسب Daniel J. Brady (20622465)

PLOS One data. حسب Ayşenur Eser (21713911)

"…<div><p>Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. …"

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making حسب Xiaofei Zhang (16483224)

"…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …"

Data Sheet 1_Identification of key biomarkers related to fibrocartilage chondrocytes for osteoarthritis based on bulk, single-cell transcriptomic data.docx حسب Bailin Pan (20300112)

"…We conducted functional-enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), and used weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm to select biomarkers. …"

Data Sheet 2_Identification of key biomarkers related to fibrocartilage chondrocytes for osteoarthritis based on bulk, single-cell transcriptomic data.csv حسب Bailin Pan (20300112)

"…We conducted functional-enrichment analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), and used weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm to select biomarkers. …"

NanoDB: Research Activity Data Management System حسب Lorenci Gjurgjaj (19702207)

"…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …"

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' حسب Radu Stancu (21165068)

"…Distance Sensing</p><p dir="ltr">Code and data to demonstrate extracting distance sensing data from A-scans and to generate Fig. 8 using the algorithm described in Fig. 7. Functions to generate distance measurements are in 'distance_sensing_utilities.py' and an example of how to use this on data in the 'data' folder is in 'distance_sensing_example.py', which generates Fig 8. …"

PSO-Optimized Electronic Load Controller with Intelligent Energy Recovery for Self-Excited Induction Generator Based Micro-Hydro Systems حسب MRINAL KANTI RAJAK (21838169)

"…The dataset includes: (1) <b>PSO configuration parameters</b> - complete algorithm setup with population size (N=20), adaptive inertia weights (0.9→0.4), time-varying cognitive/social coefficients (c1: 2.5→0.5, c2: 0.5→2.5), search space boundaries for all 10 optimization variables, and convergence criteria specifications; (2) <b>Multi-objective fitness function data</b> - detailed weight adaptation formulas, individual objective convergence statistics (voltage: 15.3 iter, frequency: 19.2 iter, THD: 12.8 iter, energy: 23.0 iter), and composite fitness evolution from 0.537 to 0.903 over 50 iterations; (3) <b>Particle dynamics tracking</b> - complete position and velocity trajectories for all 20 particles across optimization dimensions [Kpv, Kiv, Kdv, Kpf, Kif, Kdf, ma, θphase, fc, Ppump,ref], diversity evolution (100%→8%), and exploration/exploitation transition patterns; (4) <b>Real-time implementation metrics</b> - computational requirements (2.6 kB memory, 67% CPU utilization), execution timing (0.83 ms average, 1.2 ms worst-case), and synchronization protocols for 100 Hz optimization loops; and (5) <b>Validation datasets</b> - performance verification across six different load conditions, convergence statistics, and algorithm robustness testing results demonstrating consistent ±1.8% voltage regulation and ±0.9% frequency stability achievements, all provided in structured CSV/JSON formats with comprehensive documentation under CC-BY license.…"