Data Sheet 1_Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.pdf by Magdalena Fafrowicz (629724)

“…The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.…”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by 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. …”

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.…”

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' by Paul Greer (22619328)

“…Blazhko periods were calculated for 18 out of 20 highly modulated objects by phase-folding the amplitude modulation induced upper envelope function of their light curves.…”

Mechanomics Code - JVT by Carlo Vittorio Cannistraci (5854046)

“…The functions were tested respectively in: MATLAB 2018a or youger, Python 3.9.4, R 4.0.3.…”

Data Sheet 1_Urinary lipid metabolites and progression of kidney disease in individuals with type 2 diabetes.pdf by Yu Xiao (150812)

“…The subsequent validation phase utilized an independent cohort of 248 T2D patients, in which rapid kidney function decline was defined as the highest quartile of annual estimated glomerular filtration rate (eGFR) reduction. …”

Table 1_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 2_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.xlsx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 4_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Supplementary file 3_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Supplementary file 2_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Supplementary file 6_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Supplementary file 1_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Supplementary file 4_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Supplementary file 5_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…</p>Methods<p>The work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. …”

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. …”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”