Stratification of Differentially Expressed genes (DEGs) in cases versus controls. by Peter Nambala (7245215)

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Mean proportion and p-values (t-test) of cell type for Stage 1 cases, Stage 2 cases and controls. by Peter Nambala (7245215)

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DEGs and network analysis in Stage 1 case blood. by Peter Nambala (7245215)

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DEGs and network analysis in blood from Stage 2 case versus healthy controls. by Peter Nambala (7245215)

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Immune system Blood cells activated in r-HAT. by Peter Nambala (7245215)

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Summary of blood samples from r-HAT cases and healthy controls used for RNA sequencing. by Peter Nambala (7245215)

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Demographic details of recruited study participants. by Peter Nambala (7245215)

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List of significant (padj <0.05) differentially expressed genes in Stage 2 r-HAT with their corresponding log2 fold change. by Peter Nambala (7245215)

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List of significant (padj <0.05) differentially expressed genes in Stage 1 r-HAT with their corresponding log2 fold change. by Peter Nambala (7245215)

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Stratification of single blood cells in cases versus controls. by Peter Nambala (7245215)

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Differential gene expression in r-HAT cases vs controls. by Peter Nambala (7245215)

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Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm by Abolfazl Gharaei (21803416)

“…The performance evaluation and validation results revealed that the proposed ML-based BOZOA provides very good performance in solving TRSPTWs at a variety of scales with respect to the optimality criteria, including, number of taken iterations, infeasibility, optimality error and complementarity compared with both an exact solver and two inspired algorithms from ZOA.</p> <p><b>Highlights</b></p><p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p><p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p><p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p><p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p><p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p><p>Experiments using real data to show the performance of the model and solution method</p><p></p> <p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p> <p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p> <p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p> <p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p> <p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p> <p>Experiments using real data to show the performance of the model and solution method</p>…”

Scatter plots showing the relationships between PCV (phase clustering value) and 3D inter-site distance for representative frequencies (rows) for the five behavioral conditions (co... by Melisa Menceloglu (7610213)

The SSP accuracy of combined machine learning algorithms obtained with training and testing datasets sharing decreasing levels of sequence homology. by Teng-Ruei Chen (9046518)

“…Using the iMLS system’s bootstrapping function, random forests of several different machine learning algorithms were constructed. …”

Data Sheet 2_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.zip by Rui Zhang (13940)

“…</p>Results<p>Multivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”

Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx by Rui Zhang (13940)

“…</p>Results<p>Multivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”

Fig 2 - by Melisa Menceloglu (7610213)

Distributions of |Δθ| for the stable low-|Δθ| (left columns), stable high-|Δθ| (middle columns), and shifting-|Δθ| (right columns) networks for different <i>q</i> ranges in 5-perce... by Melisa Menceloglu (7610213)

Results of simulating the ability of pBO, BO and random search algorithms on identifying the optima in the Hartmann 3-dimensional surface. by Nienke E. R. van Bueren (11414330)

“…<b>b)</b> Shows the Euclidean distance of the identified optima from the true optima of the Hartmann function (i.e., accuracy of the algorithm). …”

Spatial distributions of connectivity degree for the stable low-|Δθ| (left columns) and stable high-|Δθ| (middle columns) networks, and for the shifting-|Δθ| network that shifted i... by Melisa Menceloglu (7610213)