Cross-linked CoMoO4/rGO nanosheets as oxygen reduction catalyst

Development of inexpensive and robust electrocatalysts towards oxygen reduction reaction (ORR) is crucial for the cost-affordable manufacturing of metal-air batteries and fuel cells. Here we show that cross-linked CoMoO4 nanosheets and reduced graphene oxide (CoMoO4/rGO) can be integrated in a hybrid material under one-pot hydrothermal conditions, yielding a composite material with promising catalytic activity for oxygen reduction reaction (ORR). Cyclic voltammetry (CV) and linear sweep voltammetry (LSV) were used to investigate the efficiency of the fabricated CoMoO4/rGO catalyst towards ORR in alkaline conditions. The CoMoO4/rGO composite revealed the main reduction peak and onset potential centered at 0.78 and 0.89 V (vs. RHE), respectively. This study shows that the CoMoO4/rGO composite is a highly promising catalyst for the ORR under alkaline conditions, and potential noble metal replacement cathode in fuel cells and metal-air batteries

OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning

Recently, there has been a surge of explainable AI (XAI) methods driven by the need for understanding machine learning model behaviors in high-stakes scenarios. However, properly evaluating the effectiveness of the XAI methods inevitably requires the involvement of human subjects, and conducting human-centered benchmarks is challenging in a number of ways: designing and implementing user studies is complex; numerous design choices in the design space of user study lead to problems of reproducibility; and running user studies can be challenging and even daunting for machine learning researchers. To address these challenges, this paper presents OpenHEXAI, an open-source framework for human-centered evaluation of XAI methods. OpenHEXAI features (1) a collection of diverse benchmark datasets, pre-trained models, and post hoc explanation methods; (2) an easy-to-use web application for user study; (3) comprehensive evaluation metrics for the effectiveness of post hoc explanation methods in the context of human-AI decision making tasks; (4) best practice recommendations of experiment documentation; and (5) convenient tools for power analysis and cost estimation. OpenHEAXI is the first large-scale infrastructural effort to facilitate human-centered benchmarks of XAI methods. It simplifies the design and implementation of user studies for XAI methods, thus allowing researchers and practitioners to focus on the scientific questions. Additionally, it enhances reproducibility through standardized designs. Based on OpenHEXAI, we further conduct a systematic benchmark of four state-of-the-art post hoc explanation methods and compare their impacts on human-AI decision making tasks in terms of accuracy, fairness, as well as users' trust and understanding of the machine learning model

SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition

The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios

Performance Regression Detection in DevOps

Performance is an important aspect of software quality. The goals of performance are typically defined by setting upper and lower bounds for response time and throughput of a system and physical level measurements such as CPU, memory, and I/O. To meet such performance goals, several performance-related activities are needed in development (Dev) and operations (Ops). Large software system failures are often due to performance issues rather than functional bugs. One of the most important performance issues is performance regression. Although performance regressions are not all bugs, they often have a direct impact on users’ experience of the system. The process of detection of performance regressions in development and operations is faced with challenges. First, the detection of performance regression is conducted after the fact, i.e., after the system is built and deployed in the field or dedicated performance testing environments. Large amounts of resources are required to detect, locate, understand, and fix performance regressions at such a late stage in the development cycle. Second, even we can detect a performance regression, it is extremely hard to fix it because other changes are applied to the system after the introduction of the regression. These challenges call for further in-depth analyses of the performance regression. In this thesis, to avoid performance regression slipping into operation, we first perform an exploratory study on the source code changes that introduce performance regressions in order to understand root-causes of performance regression in the source code level. Second, we propose an approach that automatically predicts whether a test would manifest performance regressions in a code commit. Most of the performance issues are related to configurations. Therefore, third, we propose an approach that predicts whether a configuration option manifests a performance variation issue. To assist practitioners to analyze system performance with operational data, we propose an approach to recovering field-representative workload that can be used to detect performance regression

Transparent system call based performance debugging for cloud computing

Abstract Problem diagnosis and debugging in distributed environments such as the cloud and popular distributed systems frameworks has been a hard problem. We explore an evaluation of a novel way of debugging distributed systems, such as the MapReduce framework, by using system calls. Performance problems in such systems can be hard to diagnose and to localize to a specific node or a set of nodes. Additionally, most debugging systems often rely on forms of instrumentation and signatures that sometimes cannot truthfully represent the state of the system (logs or application traces for example). We focus on evaluating the performance debugging of these frameworks using a low level of abstraction -system calls. By focusing on a small set of system calls, we try to extrapolate meaningful information on the control flow and state of the framework, providing accurate and meaningful automated debugging

How to Achieve Sufficient Endogenous Insulin Suppression in Euglycemic Clamps Assessing the Pharmacokinetics and Pharmacodynamics of Long-Acting Insulin Preparations Employing Healthy Volunteers

The therapeutic effect of basal insulin analogs will be sustained at a rather low insulin level. When employing healthy volunteers to assess the pharmacokinetics (PK) and pharmacodynamics (PD) of long-acting insulin preparations by euglycemic clamp techniques, endogenous insulin cannot be ignored and sufficient endogenous insulin inhibition is crucial for the PD and/or PK assessment. This study aimed to explore a way to sufficiently inhibit endogenous insulin secretion. Healthy Chinese male and female volunteers were enrolled. After a subcutaneous injection of insulin glargine (IGlar) (LY2963016 or Lantus) (0.5 IU/kg), they underwent a manual euglycemic clamp for up to 24 h where the target blood glucose (BG) was set as 0.28 mmol/L below the individual’s baseline. Blood samples were collected for analysis of PK/PD and C-peptide. The subjects fell into two groups according to the reduction extent of postdose C-peptide from baseline. After matching for the dosage proportion of Lantus, there were 52 subjects in group A (C-peptide reduction<50%) and 26 in group B (C-peptide reduction≥50%), respectively. No significant difference was detected in age, body mass index, the proportion of Latus treatment and female participants. A lower basal BG was observed in group B compared to group A (4.35 ± 0.26 vs. 4.59 ± 0.22 mmol/L, p < 0.05). The clamp studies were all conducted with high quality (where BG was consistently maintained around the target and exhibited a low variety). The binary logistic regression analysis indicated low basal BG as an independent factor for the success of sufficient endogenous insulin suppression. In conclusion, setting a lower sub-baseline target BG (e.g., 10% instead of 5% below baseline) might be an approach to help achieve sufficient endogenous insulin suppression in euglycemic clamps with higher basal BG levels (e.g., beyond 4.60 mmol/L)

Experimental investigation of kinetic instabilities driven by runaway electrons in the EXL-50 spherical torus

In this study, the first observation of high-frequency instabilities driven by runaway electrons has been reported in the EXL-50 spherical torus using a high-frequency magnetic pickup coil. The central frequency of these instabilities is found to be exponentially dependent on the plasma density, similar to the dispersion relation of the whistler wave. The instability frequency displays chirping characteristics consistent with the Berk-Breizman model of beam instability. Theoretically, the excitation threshold of the instability driven by runaway electrons is related to the ratio of the runaway electron density to the background plasma density, and such a relationship is first demonstrated experimentally in this study. The instability can be stabilized by increasing the plasma density, consistent with the wave-particle resonance mechanism. This investigation demonstrates the controlled excitation of chirping instabilities in a tokamak plasma and reveals new features of these instabilities, thereby advancing the understanding of the mechanisms for controlling and mitigating runaway electrons

Identification of Competing Endogenous RNA Regulatory Networks in Vitamin A Deficiency-Induced Congenital Scoliosis by Transcriptome Sequencing Analysis

Background/Aims: Congenital scoliosis (CS) is a result of anomalous development of vertebrae and is frequently associated with somitogenesis malformation. Although noncoding RNAs (ncRNAs) have been recently determined to be involved in the pathogenesis of CS, the competing endogenous RNA (ceRNA) regulatory networks in CS remain largely unknown. Methods: Sequencing was conducted to explore the ncRNA expression profiles in rat embryos (gestation day 9) following vitamin A deficiency (VAD) (n = 9 for the vitamin A deficiency-induced congenital scoliosis (VAD-CS) group and n = 4 for the control group). Real-time reverse transcriptase polymerase chain reaction (RT-PCR) was conducted to verify the expression levels of selected mRNAs, long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs). Bioinformatics analysis was used to discover the possible relationships and functions of the ceRNAs. Results: A total of 749 mRNAs, 56 miRNAs, 685 lncRNAs, and 70 circRNAs were identified to have significantly different expression levels in the two groups. Wnt, PI3K-ATK, FoxO, EGFR, and mTOR were found to be the most significant pathways involved in VAD-CS pathogenesis. The circRNA/miRNA/mRNA and lncRNA/miRNA/mRNA networks of CS were built, and the gene expression mechanisms regulated by ncRNAs were unveiled via the ceRNA regulatory networks. Conclusion: We comprehensively identified ceRNA regulatory networks of embryonic somite development in VAD-CS as well as revealed the contribution of different ncRNA expression profiles. Our data demonstrate the association between mRNAs and ncRNAs in the pathogenic mechanism of CS