122 research outputs found

    Study on Fatigue Characteristics of Concrete Sleepers with Porous Basalt as the Aggregate

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    Due to the shortage of local materials, porous basalt was used as the coarse aggregate in the prefabrication of sleepers for the Mombasa-Nairobi Railway in Kenya. To study their fatigue characteristics, the sleepers were measured under fatigue loading for their local strain, overall deformation and crack initiation. The methods used include the traditional strain measurement, the sleeper deflection measurement and the 3D optical strain measurement. To be more specific, the traditional strain measurement method was employed to compare the strain-load relation of the sleepers under different cyclic loading times. Deflection variations of the sleepers were taken into consideration to analyze sleeper local defects and the variation law of the constitutive relation for concrete. And the 3D optical non-contact strain measurement method was adopted to monitor the sleeper crack initiation and growth process under fatigue loading and analyze the crack growth law

    ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference

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    Aligning language models to human expectations, e.g., being helpful and harmless, has become a pressing challenge for large language models. A typical alignment procedure consists of supervised fine-tuning and preference learning. Most preference learning methods, such as RLHF and DPO, depend on pairwise preference data, which inadequately address scenarios where human feedback is point-wise, leading to potential information loss and suboptimal performance. Addressing this gap, we introduce Point-wise Direct Preference Optimization, a novel preference learning method designed to harness point-wise feedback effectively. Our work also uncovers a novel connection between supervised fine-tuning and point-wise preference learning, culminating in Unified Language Model Alignment, a single-step method that unifies the alignment with human demonstrations and point-wise preferences. Extensive experiments on point-wise preference datasets with binary or continuous labels validate the effectiveness of our methods. Our code and a new dataset with high-quality demonstration samples on harmlessness are released

    Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

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    We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.Comment: WSDM 23, Best Paper Candidat

    Identification of molecular subtypes, risk signature, and immune landscape mediated by necroptosis-related genes in non-small cell lung cancer

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    BackgroundNon-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.MethodsIn The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.ResultsWith the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.ConclusionThis study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy

    Detection and imaging of the free radical DNA in cells—Site-specific radical formation induced by Fenton chemistry and its repair in cellular DNA as seen by electron spin resonance, immuno-spin trapping and confocal microscopy

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    Oxidative stress-related damage to the DNA macromolecule produces lesions that are implicated in various diseases. To understand damage to DNA, it is important to study the free radical reactions causing the damage. Measurement of DNA damage has been a matter of debate as most of the available methods measure the end product of a sequence of events and provide limited information on the initial free radical formation. We report a measurement of free radical damage in DNA induced by a Cu(II)-H2O2 oxidizing system using immuno-spin trapping supplemented with electron paramagnetic resonance. In this investigation, the short-lived radical generated is trapped by the spin trap 5,5-dimethyl-1-pyrroline N-oxide (DMPO) immediately upon formation. The DMPO adduct formed is initially electron paramagnetic resonance active, but is subsequently oxidized to the stable nitrone adduct, which can be detected and visualized by immuno-spin trapping and has the potential to be further characterized by other analytical techniques. The radical was found to be located on the 20-deoxyadenosine (dAdo) moiety of DNA. The nitrone adduct was repaired on a time scale consistent with DNA repair. In vivo experiments for the purpose of detecting DMPO–DNA nitrone adducts should be conducted over a range of time in order to avoid missing adducts due to the repair processes

    STUDY ON THE OPERATING CHARACTERISTICS OF UNLOADER OF RECIPROCATING COMPRESSOR STEPLESS CAPACITY REGULATION SYSTEM

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    Reciprocating compressor stepless capacity control system in the petrochemical industry are widely used, unloading device as a key component of the system actuator, the action characteristics of the relationship between the quality of the system directly related to the efficiency of the air intake system and compressor valve life. In this paper, ANSYS/Ls-dyna explicit dynamics software is used to simulate the automatic opening process of the intake valve at the rated speed of the compressor. The displacement of the valve is measured by the eddy current displacement sensor. On the basis of this, the operation of the unloader under different oil supply pressure and different ejection time is simulated, the ideal hydraulic ejection force and the ejection phase are determined. It is concluded that the reasonable action of the unloader can effectively relieve the vibration of the valve plate and improve the force of the valve

    Diesel Engine Fault Diagnosis Based on Stack Autoencoder Optimized by Harmony Search

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