Simulation of diffusion-derived platelet activation. by Ezhena S. Starodubtseva (19797385)

Table_2_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Table_1_Visuospatial, oculomotor, and executive reading skills evolve in elementary school, and errors are significant: a topological RAN study.pdf by Mario Lecce (18777553)

Visualization of spheroid bulk based features extracted from simulation data for four different phenotypes. by Julian Herold (14886464)

MiR-21-3p validated targets viewed in a network. by Arsalan Amirfallah (7305221)

S1 Graphical abstract - by 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>. …”

Presentation of the DySCo framework. by 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. …”

Fig 1 - by Vinita Periwal (719082)

Comparison against the commercial scanner. by Swathi Lakshmi Balasubramanian (12078093)

my-home-is-my-secret.zip by Anna Brauer (11266881)

“…</p> <p><br></p> <p>The material contains two folders; each contains an implementation of the algorithm, one in Java and one in Python.<br> </p> <p><br></p> <p>The Python script includes a function (example()) demonstrating how the mechanism class ('STT') may be used. …”

RSeqFlow-OlivePollen2: RSeqFlow1_results_2023-02-09_16.03.33.zip by M. Gonzalo Claros (8543760)

“…</p> <p>•  <strong>CTFnormalisedCPMs-{DATETIME}.tsv</strong>: Normalised CPMs for each gene (rows) in each sample replicate (columns) using the CTF algorithm.</p> <p>•  <strong>DEGs_{CONTRAST}_TREAT_P-0.1_FC-1.2_{DATETIME}.tsv</strong>: LogFC, average expression, <em>t</em> statistic, <em>P</em> value, and adjusted <em>P</em> value, for all DEGs (rows) in the contrast indicated in {CONTRAST} using the <em>treat</em> method and the <em>P</em>and <em>FC</em> indicated in the filename.…”

BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks by Eric Yanchenko (3266514)

“…<p>The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose <math><mrow>BOPIM</mrow></math>, a Bayesian Optimization (BO) algorithm for IM on temporal networks. …”

Surgical treatment of macrodactyly of the foot in children by Lu Chen (75170)

“…The goal of the treatment of pedal macrodactyly is to obtain a functional and cosmetically acceptable foot. This treatment algorithm and multi-technique procedure could fully satisfy this goal.…”

Unmanned Ground Vehicle for the Identification of Trees Infested by Bark Beetles by Jonathan Flores (11414891)

“…The dynamic model of the vehicle is presented and a control algorithm is proposed to control the path following and the stability proof is provided by a Lyapunov function. …”

NanoDB: Research Activity Data Management System by 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. …”

PREDICTION OF DEM PARAMETERS OF COATED FERTILIZER PARTICLES BASED ON GA-BP NEURAL NETWORK by Xin Du (208780)

“…The predicted values matched the expected output values, indicating that the GA-BP neural network can accurately predict the nonlinear function output, and the network predicted output can be approximated as the actual output of the function. …”

Code and Data for 'Fabrication and testing of lensed fiber optic probes for distance sensing using common path low coherence interferometry' by 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 by 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.…”

A dynamically changing bistable switch enhances robustness and accuracy of transitions in time and space. by Jan Rombouts (5007665)

“…J) Coefficient of variation as function of Δ<i>a</i> for the stochastic simulation algorithm (compare to Panel F). …”

Table_1_Stigmatic Microscopy Enables Low-Cost, 3D, Microscale Particle Imaging Velocimetry in Rehydrating Aqueous Two-Phase Systems.DOCX by Cameron Yamanishi (3350813)

“…Stigmatic microscopy is inexpensive and advantageous because it modifies the point-spread function of fluorescent particles to enable measurement of their 3D positions from single 2D images, without needing to take slices. …”