Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation

Production cost minimization (PCM) simulation is commonly employed for assessing the operational efficiency, economic viability, and reliability, providing valuable insights for power system planning and operations. However, solving a PCM problem is time-consuming, consisting of numerous binary variables for simulation horizon extending over months and years. This hinders rapid assessment of modern energy systems with diverse planning requirements. Existing methods for accelerating PCM tend to sacrifice accuracy for speed. In this paper, we propose a stable relay learning optimization (s-RLO) approach within the Branch and Bound (B&B) algorithm. The proposed approach offers rapid and stable performance, and ensures optimal solutions. The two-stage s-RLO involves an imitation learning (IL) phase for accurate policy initialization and a reinforcement learning (RL) phase for time-efficient fine-tuning. When implemented on the popular SCIP solver, s-RLO returns the optimal solution up to 2 times faster than the default relpscost rule and 1.4 times faster than IL, or exhibits a smaller gap at the predefined time limit. The proposed approach shows stable performance, reducing fluctuations by approximately 50% compared with IL. The efficacy of the proposed s-RLO approach is supported by numerical results.Comment: Submitted to IEEE Transactions on Power Systems on December 15, 202

Effect of vascular endothelial growth factor rs35569394 in esophageal cancer and response to chemotherapy

The objective of this study was to investigate the possible association between the single nucleotide polymorphism (SNP), rs35569394, of the vascular endothelial growth factor gene (VEGF) and the risk of esophageal cancer (EC) in the Han Chinese population. A total of 290 EC subjects and 322 ethnically matched unrelated healthy controls free from the esophageal disease were studied. Genomic DNA was isolated from peripheral blood by salting out. Genotyping of VEGF rs35569394 polymorphism was carried out via polymerase chain reaction followed by agarose gel electrophoresis. The results showed that the distribution of genotypes was significantly different across the gender groups (p=0.032) and clinical stages (p=0.034). VEGF rs35569394 was associated with EC risk (p= 0.012, OR=1.34). A gender analysis break-down showed that rs35569394-D allele frequency was significantly higher in females than in the controls (p=0.0004, OR=1.81). Moreover, significant associations were also found in females under the dominant model (II versus ID+DD: χ2=8.18, p=0.003, OR=2.12) and the recessive model (II+ID versus DD: χ2=8.25, p=0.004, OR=2.39). Additionally, we found that the genotype, rs35569394-DD, was associated with a complete response + partial response to chemotherapy when compared with rs35569394-II (χ2=4.67, p=0.030, OR=0.47). In conclusion, our case-control study showed that the VEGF rs35569394 was significantly associated with the clinical stages and the increased risk of EC in Han Chinese females. In addition, the genotype rs35569394-DD showed a better response to chemotherapy

Large Language Model Alignment: A Survey

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various concerns. The potential of these models is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. Consequently, it becomes paramount to employ alignment techniques to ensure these models to exhibit behaviors consistent with human values. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs, in conjunction with the extant capability research in this domain. Adopting the lens of AI alignment, we categorize the prevailing methods and emergent proposals for the alignment of LLMs into outer and inner alignment. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks. To assess LLM alignment, we present a wide variety of benchmarks and evaluation methodologies. After discussing the state of alignment research for LLMs, we finally cast a vision toward the future, contemplating the promising avenues of research that lie ahead. Our aspiration for this survey extends beyond merely spurring research interests in this realm. We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.Comment: 76 page

High-performance electrochemical CO2 reduction cells based on non-noble metal catalysts

The promise and challenge of electrochemical mitigation of CO2 calls for innovations on both catalyst and reactor levels. In this work, enabled by our high-performance and earth-abundant CO2 electroreduction catalyst materials, we developed alkaline microflow electrolytic cells for energy-efficient, selective, fast, and durable CO2 conversion to CO and HCOO-. With a cobalt phthalocyanine-based cathode catalyst, the CO-selective cell starts to operate at a 0.26 V overpotential and reaches a Faradaic efficiency of 94% and a partial current density of 31 mA/cm2 at a 0.56 V overpotential. With a SnO2-based cathode catalyst, the HCOO--selective cell starts to operate at a 0.76 V overpotential and reaches a Faradaic efficiency of 82% and a partial current density of 113 mA/cm2 at a 1.36 V overpotential. In contrast to previous studies, we found that the overpotential reduction from using the alkaline electrolyte is mostly contributed by a pH gradient near the cathode surface

The Measurement of rho‐independent Transcription Terminator Efficiency

The purpose of this RFC is to provide standard methodology for the measurement of the absolute strength of terminators in bacteria. Because we have characterized the performance of terminator in E. coli and used a simple equation model, it can be expressed in PoPS

Dynamic Changes in the Nigrostriatal Pathway in the MPTP Mouse Model of Parkinson’s Disease

The characteristic brain pathology and motor and nonmotor symptoms of Parkinson’s disease (PD) are well established. However, the details regarding the causes of the disease and its course are much less clear. Animal models have significantly enriched our current understanding of the progression of this disease. Among various neurotoxin-based models of PD, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model is the most commonly studied model. Here, we provide an overview of the dynamic changes in the nigrostriatal pathway in the MPTP mouse model of PD. Pathophysiological events, such as reductions in the striatal dopamine (DA) concentrations and levels of the tyrosine hydroxylase (TH) protein, depletion of TH-positive nerve fibers, a decrease in the number of TH-positive neurons in the substantia nigra pars compacta (SNpc), and glial activation, are addressed. This article will assist with the development of interventions or therapeutic strategies for PD

Nanotechnology in peripheral nerve repair and reconstruction

The recent progress in biomaterials science and development of tubular conduits (TCs) still fails in solving the current challenges in the treatment of peripheral nerve injuries (PNIs), in particular when disease-related and long-gap defects need to be addressed. Nanotechnology-based therapies that seemed unreachable in the past are now being considered for the repair and reconstruction of PNIs, having the power to deliver bioactive molecules in a controlled manner, to tune cellular behavior, and ultimately guide tissue regeneration in an effective manner. It also offers opportunities in the imaging field, with a degree of precision never achieved before, which is useful for diagnosis, surgery and in the patientâ s follow-up. Nanotechnology approaches applied in PNI regeneration and theranostics, emphasizing the ones that are moving from the lab bench to the clinics, are herein overviewed.The authors acknowledge the Portuguese Foundation for Science and Technology (FCT) for the financial support provided to Joaquim M. Oliveira (IF/01285/2015) and Joana Silva-Correia (IF/00115/2015) under the program “Investigador FCT”.info:eu-repo/semantics/publishedVersio

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe