Reliability and failure probability. by Jianqing Jia (21001506)

“…Consequently, tunnel reliability decreases. Specifically, with temperature increases of 2.6 °C and 4 °C, tunnel reliability decreases by approximately 4.5% and 6.3%, respectively, when considering insulation layer deterioration.…”

MOCR strength could predict the size of hearing deterioration caused by a violin practice. by Sho Otsuka (844383)

“…<p>Correlation of ipsilateral MOCR strength with the elevation of hearing threshold at 4 kHz (A) and with CEOAE decrease at 4 kHz in the left ear (B). …”

Profiling of intestinal microbiota in patients infected with respiratory influenza a and b viruses by Al Khatib, Hebah A.

“…Overall, infected patients experienced an increased abundance of Bacteroidetes and a corresponding decrease in Firmicutes. Differential abundance testing illustrated that differences in gut microbiota composition were influenza type-dependent, identifying ten differentially abun-dant operational taxonomic units (OTUs) between influenza A-and influenza B-infected patients. …”
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Life Cycle Environmental Impacts of Sewage Sludge Pyrolysis and Their Dynamic Evolution by Jan Matuštík (22500291)

Adaptation for Protein Synthesis Efficiency in a Naturally Occurring Self-Regulating Operon by Dorota Herman (118796)

“…We find that the strongest impact of all elements of the regulatory architecture is on improving the efficiency of protein synthesis by reduction in the number of mRNA molecules needed to be produced, leading to a greater than ten-fold reduction in host energy required to express these plasmid proteins. …”

A Novel Chimeric Avidin with Increased Thermal Stability Using DNA Shuffling by Barbara Taskinen (476111)

“…Here, we report the crystal structure of one of the previously selected and characterized chimeric avidin forms, A/A2-1. The structure was solved at 1.8 Å resolution and revealed that the protein fold was not affected by the shuffled sequences. …”

S1 Fig - Rescue of Retinal Function by BDNF in a Mouse Model of Glaucoma by Luciano Domenici (268643)

“…</b> Retinal ganglion cells (RGCs) labeled by an antibody recognizing γ-Synuclein (A), a member of small unfolded proteins (Synucleins), used to assess RGCs viability appear unaffected in 7 month DBA/2J (bottom panels); top panels report RGCs labeled in C57BL/6J mice. …”

Ball valve pipeline geometric modeling. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Hydrogen volume fraction change in the pipeline. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Mesh independence verification. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Role of ALIX and TSG101 in the entry of PEDV or PEAV VLP into Vero cells. by Xiongnan Chen (13307178)

Valve parameters and simulation results. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

PV1 treatment ameliorates EAU. by Pedro Henrique Papotto (791904)

Undulation pipeline geometric modeling. by Weiqing Xu (1279959)

“…<div><p>Hydrogen is a clean energy source, and blending it with natural gas in existing pipeline networks is a key transition solution for transportation cost reduction. …”

Time pressure: a controlled experiment of test case development and requirements review by Mika Mäntylä (406301)

“…<div>REF: Mäntylä M. V., Petersen K., Lehtinen, T. O. A., Lassenius, C. …”

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. Additionally, a secondary validation using experimental data from other researchers confirms the model’s good agreement with previous results, demonstrating its robust generalization ability. …”