RBE for the selected volumetric dose distribution of CIBs with a field size of 60 × 60 mm² and a range of 150 mm as a function of SOBP size from 30 to 120 mm. by Masashi Yagi (3186909)

Absorption Intensities of Organic Molecules from Electronic Structure Calculations versus Experiments: the Effect of Solvation, Method, Basis Set, and Transition Moment Gauge by Jorge C. Garcia-Alvarez (11934119)

“…Here, we focus on a subset of transitions with the highest reliability to further benchmark the OSs from several wave function methods and density functionals. …”

Architecture of the neural network. by Ankit Gupta (211785)

Comparison chart of computation time. by Manoj Kumar V. (18196645)

“…In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. …”

Demand side management system. by Manoj Kumar V. (18196645)

“…In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. …”

DSM optimization method implementation flowchart. by Manoj Kumar V. (18196645)

“…In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. …”

Assessment of T2w signal intensity (SI). by Stephan Ellmann (314565)

Data Sheet 1_A modified A* algorithm combining remote sensing technique to collect representative samples from unmanned surface vehicles.docx by Lei Wang (6656)

Table_2_Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data.DOCX by Lianhong Ding (9753227)

“…However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. …”

Table_1_Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data.XLSX by Lianhong Ding (9753227)

“…However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. …”

Data Sheet 3_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.docx by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Data Sheet 2_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.zip by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Data Sheet 1_Pain - related methylation driver genes affect the prognosis of pancreatic cancer patients by altering immune function and perineural infiltration.zip by Chencheng Zhang (6877691)

“…</p>Methods<p>Integrating multi-omics data from TCGA-PAAD (Pancreatic adenocarcinoma), we identified methylation driver genes (MDGs) using the MethylMix algorithm. …”

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

“…<p>(<b>A</b>) Genetic algorithm schematic diagram. Initially a set of organisms is generated, each of which is determined by a random vector of scaling factors for optimized model parameters (step 1). …”

Screening and diagnosis pipeline for the Challenge. by Matthew A. Reyna (16965038)

Particle swarm optimization (PSO) algorithm to identify parameter values of model C-fiber axons and meet performance criteria. by Brandon J. Thio (5351147)

Data_Sheet_1_Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence.pdf by Mónica López-Vicente (3980462)

“…We used a k-means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. …”

Image_1_Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool.JPEG by Carole Sala (8270814)

“…In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). …”

Table_1_Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool.DOCX by Carole Sala (8270814)

“…In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). …”

Table_3_Designing a Syndromic Bovine Mortality Surveillance System: Lessons Learned From the 1-Year Test of the French OMAR Alert Tool.DOCX by Carole Sala (8270814)

“…In our system, every Thursday, the number of deaths is grouped by ISO week and small surveillance areas and then analyzed using traditional time-series analysis steps (cleaning, prediction, signal detection). For each of the five detection algorithms implemented (i.e., the exponentially weighted moving average chart, cumulative sum chart, Shewhart chart, Holt-Winters, and historical limits algorithms), seven detection limits are applied, giving a signal score from 1 (low excess mortality) to 7 (high excess mortality). …”