Online Build-Order Optimization for Real-Time Strategy Agents Using Multi-Objective Evolutionary Algorithms

The investigation introduces a novel approach for online build-order optimization in real-time strategy (RTS) games. The goal of our research is to develop an artificial intelligence (AI) RTS planning agent for military critical decision- making education with the ability to perform at an expert human level, as well as to assess a players critical decision- making ability or skill-level. Build-order optimization is modeled as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA) that provides a set of good build-orders to a RTS planning agent. We de ne three research objectives: (1) Design, implement and validate a capability to determine the skill-level of a RTS player. (2) Design, implement and validate a strategic planning tool that produces near expert level build-orders which are an ordered sequence of actions a player can issue to achieve a goal, and (3) Integrate the strategic planning tool into our existing RTS agent framework and an RTS game engine. The skill-level metric we selected provides an original and needed method of evaluating a RTS players skill-level during game play. This metric is a high-level description of how quickly a player executes a strategy versus known players executing the same strategy. Our strategic planning tool combines a game simulator and an MOEA to produce a set of diverse and good build-orders for an RTS agent. Through the integration of case-base reasoning (CBR), planning goals are derived and expert build- orders are injected into a MOEA population. The MOEA then produces a diverse and approximate Pareto front that is integrated into our AI RTS agent framework. Thus, the planning tool provides an innovative online approach for strategic planning in RTS games. Experimentation via the Spring Engine Balanced Annihilation game reveals that the strategic planner is able to discover build-orders that are better than an expert scripted agent and thus achieve faster strategy execution times

Association of Variants in Candidate Genes with Lipid Profiles in Women with Early Breast Cancer on Adjuvant Aromatase Inhibitor Therapy

Purpose: Aromatase inhibitors can exert unfavorable effects on lipid profiles; however, previous studies have reported inconsistent results. We describe the association of single-nucleotide polymorphisms (SNP) in candidate genes with lipid profiles in women treated with adjuvant aromatase inhibitors. Experimental design: We conducted a prospective observational study to test the associations between SNPs in candidate genes in estrogen signaling and aromatase inhibitor metabolism pathways with fasting lipid profiles during the first 3 months of aromatase inhibitor therapy in postmenopausal women with early breast cancer randomized to adjuvant letrozole or exemestane. We performed genetic association analysis and multivariable linear regressions using dominant, recessive, and additive models. Results: A total of 303 women had complete genetic and lipid data and were evaluable for analysis. In letrozole-treated patients, SNPs in CYP19A1, including rs4646, rs10046, rs700518, rs749292, rs2289106, rs3759811, and rs4775936 were significantly associated with decreases in triglycerides by 20.2 mg/dL and 39.3 mg/dL (P < 0.00053), respectively, and with variable changes in high-density lipoprotein (HDL-C) from decreases by 4.2 mg/dL to increases by 9.8 mg/dL (P < 0.00053). Conclusions: Variants in CYP19A1 are associated with decreases in triglycerides and variable changes in HDL-C in postmenopausal women on adjuvant aromatase inhibitors. Future studies are needed to validate these findings, and to identify breast cancer survivors who are at higher risk for cardiovascular disease with aromatase inhibitor therapy

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Structural brain correlates of childhood inhibited temperament: an ENIGMA-Anxiety Mega-analysis

NWORubicon 019.201SG.022Pathways through Adolescenc

The Influence of fly ash morphology and phase distribution on collection in an electrostatic precipitator

Fly Ash is the unburnt portion of fuels which is carried away as solid particles in the hot gas stream of a furnace. About 99% of the fly ash produced in a typical coal-fired power station is removed by electrostatic precipitators or baghouse filters located at the base of the emission stack. Precipitator efficiency is dependent on the charging properties of the fly ash particles and the adhesive forces between them. These forces depend on the size, morphology, chemical constitution and phase distribution of the fly ash. Larger particles are usually found as aggregates held together by bridging material which may be small glassy particles, graphite sheets, or a mixture of amorphous material and small crystallites

The influence of fly ash morphology and phase distribution on collection in an electrostatic precipitator

Fly Ash is the unburnt portion of fuels which is carried away as solid particles in the hot gas stream of a furnace. About 99% of the fly ash produced in a typical coal-fired power station is removed by electrostatic precipitators or baghouse filters located at the base of the emission stack. Precipitator efficiency is dependent on the charging properties of the fly ash particles and the adhesive forces between them. These forces depend on the size, morphology, chemical constitution and phase distribution of the fly ash. Larger particles are usually found as aggregates held together by bridging material which may be small glassy particles, graphite sheets, or a mixture of amorphous material and small crystallites

Structural brain correlates of childhood inhibited temperament: An ENIGMA-anxiety mega-analysis

NWORubicon 019.201SG.022Pathways through Adolescenc

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10−8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10−14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10−10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10−8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10−8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene