AFM characterization of physical properties in coal adsorbed with different cations induced by electric pulse fracturing

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions.Peer reviewe

Quantitative Diagnosis and Prognosis Framework for Concrete Degradation Due to Alkali- Silica Reaction

This research is seeking to develop a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants that are subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification, and prognosis. The current work focuses on degradation caused by ASR (alkali-silica reaction). Controlled concrete specimens with reactive aggregate are prepared to develop accelerated ASR degradation. Different monitoring techniques—infrared thermography [1], digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy [2] (NIRAS), and vibro-acoustic modulation [3] (VAM)—are studied for ASR diagnosis of the specimens. Both DIC and mechanical measurements record the specimen deformation caused by ASR gel expansion. Thermography is used to compare the thermal response of pristine and damaged concrete specimens and generate a 2-D map of the damage (i.e., ASR gel and cracked area), thus facilitating localization and quantification of damage. NIRAS and VAM are two separate vibration-based techniques that detect nonlinear changes in dynamic properties caused by the damage. The diagnosis results from multiple techniques are then fused using a Bayesian network, which also helps to quantify the uncertainty in the diagnosis. Prognosis of ASR degradation is then performed based on the current state of degradation obtained from diagnosis, by using a coupled thermo-hydro-mechanical-chemical (THMC) model [4] for ASR degradation. This comprehensive approach of monitoring, data analytics, and uncertainty-quantified diagnosis and prognosis will facilitate the development of a quantitative, risk-informed framework that will support continuous assessment and risk management of structural health and performance

Hydrologic and Hydraulic Modeling for the Restoration of the Calumet Marshes: Assessment of Runoff Scenarios

Lake Calumet is located south of Lake Michigan. It is a site of former landfills and abandoned industrial facilities, yet a place of economical and ecological significance for the future development of the area. The marshes surrounding Lake Calumet are ecologically significant to the Black-crowned Night Heron but the hydrology in the area has been greatly impacted by the large amount of landfilling and the constantly changing land use and drainage of the surrounding uplands. In order to save threatened species, to prevent ecosystem degradation, and recreate a local economic base, the City of Chicago’s Department of Environment has been leading community groups and other agencies to develop plans to restore the region to a recreational area. Millions of dollars will be invested for the effort. To support the development plan for the Calumet Region to become an ecological park, hydrologic and hydraulic models have been developed for the region. These models serve as a basis for determining the best water management strategies for the Lake Calumet Cluster Sites and the adjacent open spaces, namely the Indian Ridge Marsh (IRM). An integrated hydrologic and hydraulic model was used to evaluate the hydrologic impacts of different remedial options proposed for the Cluster Sites and other upland properties in the marsh watersheds, and to assess the adequacy of the existing marsh outlets in terms of long-range ecological goals. This report evaluates six proposed management scenarios to cope with flooding and to establish a more suitable environment for Black-crowned Night Heron nests in the marsh areas by controlling water level fluctuations. For Black-crowned Night Heron nests, the maximum fluctuation is ten inches. Our study showed that diverting surface runoff from the Cluster Sites appeared to be the best option for limiting water level fluctuations to around six inches in the IRM.Illinois Sustainable Technology Center Sponsored Research Program ; HWR06-202Ope

Unraveling the Effects of Mobile Application Usage on Users’ Health Status: Insights from Conservation of Resources Theory

Numerous studies have documented adverse consequences arising from increased technology usage and advocated for a reduction in such usage as a plausible remedy. However, such recommendations are often infeasible and oversimplistic given mounting evidence attesting to users’ growing reliance on technology in both their personal and professional lives. Building on conservation of resources (COR) theory, we construct a research model to explain how mobile application usage, as delineated by its breadth and depth, affects users’ nomophobia and sleep deprivation, which can have negative impacts on users’ health status. We also consider the moderating influence of physical activity in mitigating the effects of mobile application usage on users’ health. We validated our hypotheses via data collected by surveying 5,842 respondents. Empirical findings reveal that (1) nomophobia is positively influenced by mobile application usage breadth but negatively influenced by mobile application usage depth, (2) sleep deprivation is negatively influenced by mobile application usage breadth but positively influenced by mobile application usage depth, and (3) sleep deprivation and nomophobia negatively impact users’ health status, whereas (4) physical activity attenuates the impact of mobile application usage on sleep deprivation but not nomophobia. The findings from this study not only enrich the extant literature on the health outcomes of mobile application usage by unveiling the impact of mobile application usage patterns and physical activity on users’ health but they also inform practitioners on how calibrating usage breadth and depth, along with encouraging physical activity, can promote healthy habits among users

The impact of ability-, motivation- and opportunity-enhancing HR sub-bundles on employee wellbeing: An examination of nonlinearities and occupational differences in skill levels

Existing research examines the impact of human resource (HR) practices on employee wellbeing by considering each practice in isolation or multiple practices as a bundle, focusing on linear associations. Drawing on the too-much-of-a-good-thing (TMGT) meta-theory, we examine possible nonlinear effects of Ability Motivation-Opportunity (AMO) sub-bundles on job satisfaction and job stress. We, also, examine boundary conditions on whether and how the nature of the identified curvilinear associations varies across employees in high-, medium-, and low-skilled occupations. Using data from the Workplace Employment Relations Study (WERS2011), we uncover an inverse U-shaped association between motivation-enhancing (ME) practices and job satisfaction and a U-shaped association between opportunity-enhancing (OE) practices and job stress. No evidence of a curvilinear ability-enhancing (AE) practices-wellbeing association emerges. Additionally, occupational differences in skills levels moderate the curvilinear ME practices-stress association. Likewise, occupational skills differences moderate the associations between OE practices and job satisfaction, and work stress. There is no suggestion that occupational differences moderate the AE practices-wellbeing association. These findings underline the contingent nature of the TMGT effect and call for a more nuanced investigation of the HR-wellbeing association

The impact of ability-, motivation- and opportunity-enhancing HR sub-bundles on employee wellbeing: an examination of nonlinearities and occupational differences in skill levels

Existing research examines the impact of human resource (HR) practices on employee wellbeing by considering each practice in isolation or multiple practices as a bundle, focusing on linear associations. Drawing on the too-much-of-a-good-thing (TMGT) meta-theory, we examine possible nonlinear effects of Ability-Motivation-Opportunity (AMO) sub-bundles on job satisfaction and job stress. We, also, examine boundary conditions on whether and how the nature of the identified curvilinear associations varies across employees in high-, medium-, and low-skill occupations. Using data from the Workplace Employment Relations Study (WERS2011), we uncover an inverse U-shaped association between motivation-enhancing (ME) practices and job satisfaction and a U-shaped association between opportunity-enhancing (OE) practices and job stress. No evidence of a curvilinear ability-enhancing (AE) practices-wellbeing association emerges. Additionally, occupational differences in skills levels moderate the curvilinear ME practices-stress association. Likewise, occupational skills differences moderate the associations between OE practices and job satisfaction, and work stress. There is no suggestion that occupational differences moderate the AE practices-wellbeing association. These findings underline the contingent nature of the TMGT effect and call for a more nuanced investigation of the HR-wellbeing association