An empirical study on coordinated development of energy consumption structure and green total factor productivity under spatial interaction

Existing studies have found a non-linear relationship between the energy consumption structure (ECS) and the green total factor productivity (GTFP), but their influencing factors are not yet clear. This study examines the spatial impact of existing green development measures on coordinating the ECS and the GTFP using the coupling and spatial econometric models. The research findings are as follows: (1) The coordination between the ECS and the GTFP has increased over time, and the coordination is significantly higher in economically developed cities. (2) The spatial analysis results show a significant spatial auto-correlation between the ECS and the GTFP coordination. Green development approaches such as environmental regulations, technological innovations, and industrial structure significantly contribute to the degree of coordination. Decomposition of the spatial effects shows that technological innovations significantly affect local and neighbouring cities. These conclusions hold after endogeneity and robustness tests. The results suggest that local governments in city clusters should promote environmental regulations, industrial structure, and technological innovations to promote the coordinated development of the ECS and the GTFP of urban agglomeration

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

The Altered Reconfiguration Pattern of Brain Modular Architecture Regulates Cognitive Function in Cerebral Small Vessel Disease

Background: Cerebral small vessel disease (SVD) is a common cause of cognitive dysfunction. However, little is known whether the altered reconfiguration pattern of brain modular architecture regulates cognitive dysfunction in SVD.Methods: We recruited 25 cases of SVD without cognitive impairment (SVD-NCI) and 24 cases of SVD with mild cognitive impairment (SVD-MCI). According to the Framingham Stroke Risk Profile, healthy controls (HC) were divided into 17 subjects (HC-low risk) and 19 subjects (HC-high risk). All individuals underwent resting-state functional magnetic resonance imaging and cognitive assessments. Graph-theoretical analysis was used to explore alterations in the modular organization of functional brain networks. Multiple regression and mediation analyses were performed to investigate the relationship between MRI markers, network metrics and cognitive performance.Results: We identified four modules corresponding to the default mode network (DMN), executive control network (ECN), sensorimotor network and visual network. With increasing vascular risk factors, the inter- and intranetwork compensation of the ECN and a relatively reserved DMN itself were observed in individuals at high risk for SVD. With declining cognitive ability, SVD-MCI showed a disrupted ECN intranetwork and increased DMN connection. Furthermore, the intermodule connectivity of the right inferior frontal gyrus of the ECN mediated the relationship between periventricular white matter hyperintensities and visuospatial processing in SVD-MCI.Conclusions: The reconfiguration pattern of the modular architecture within/between the DMN and ECN advances our understanding of the neural underpinning in response to vascular risk and SVD burden. These observations may provide novel insight into the underlying neural mechanism of SVD-related cognitive impairment and may serve as a potential non-invasive biomarker to predict and monitor disease progression

TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA

A novel top-down fabrication process for Ge2Sb2Te5 phase change material nanowires

A novel e-beam free, top-down spacer etch process was used to fabricate sub-hundred nanometer Ge2Sb2Te5 phase change nanowires. Naowires with a cross-section dimension of 50 nm × 100 nm (width × height) were obtained and phase change functionality demonstrated

Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation

The electron beam is an important way to effectively simulate the thermodynamic effects of an intense pulsed X-ray and the materials for electron beam irradiation are of great significance to study the effects of energy deposition and thermal shock waves. Based on the input conditions like the actual current, voltage, and energy spectrum of an electron beam device (REB), the analytic method and the Monte Carlo method were used to calculate the energy deposition of a multi-energy-spectrum electron beam in materials of hard aluminum and carbon phenolic, the differences of the two methods were analyzed, the energy deposition profiles of different incident angles and energies were obtained, and the energy deposition of electron beam irradiation of a multilayer target was calculated as well. Through the numerical simulation and experimental study of thermal shock waves of electron beam irradiation materials, the calculation error was less than 10% by comparing the stress changes of thermal shock waves with different energies. This is helpful for studying the protective structure of spacecraft