Fully recharged evenings? The effect of evening cyber leisure on next-day vitality and performance through sleep quantity and quality, bedtime procrastination, and psychological detachment, and the moderating role of mindfulness

Aligning with the recovery perspective, we propose a dual-path model to illustrate the effects of employees' evening cyber leisure on next-day work outcomes, namely, psychological vitality and performance. We argue that evening cyber leisure has contradicting effects on next-day performance and vitality through its effects on bedtime procrastination and psychological detachment, and in turn, sleep quantity and sleep quality. We also propose that trait mindfulness acts as an important boundary condition of the indirect effects of evening cyber leisure. We used an experience sampling methodology to collect 3 surveys per day for 10 days from 155 R&D employees of a biotech company. Our findings suggest that cyber leisure has a negative indirect effect on sleep quantity and sleep quality via bedtime procrastination, and a positive indirect effect on sleep quantity and sleep quality via evening psychological detachment. Additionally, sleep quantity was positively associated with performance, and sleep quality was positively associated with psychological vitality. Lastly, as trait mindfulness increased, the negative impact of cyber leisure on bedtime procrastination was mitigated, and the positive impact of cyber leisure on psychological detachment was enhanced. Theoretical and practical implications specific to the use of cyber devices for workplace recovery are discussed

Weight bias 2.0: the effect of perceived weight change on performance evaluation and the moderating role of anti-fat bias

Overweight employees are viewed as lazy, slow, inactive, and even incapable. Even if such attributes are false, this perspective can seriously undermine others' evaluation of their work performance. The current study explores a broader phenomenon of weight bias that has an effect on weight change. In a longitudinal study with a time lag of 6 months, we surveyed 226 supervisor-employee dyads. We found supervisor perceptions of employee weight change notably altered their evaluation of the employee performance from Time 1, especially following low vs. high Time-1 performance evaluation. Meanwhile, the moderating effects among different levels of supervisor anti-fat bias functioned as boundary conditions for such performance evaluation alteration. In particular, the interaction between the Time-1 performance evaluation and the impact of supervisor perception of employee weight change on the Time-2 performance evaluation was significant only if supervisors held a stronger anti-fat bias

Explainability in Graph Neural Networks: A Taxonomic Survey

Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. Our unified and taxonomic treatments of this subject shed lights on the commonalities and differences of existing methods and set the stage for further methodological developments. To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability. We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations

Global Value Chain Embeddedness, Digital Economy and Green Innovation- Evidence From Provincial-Level Regions of China

With globalization and digitalization, participating in Global Value Chain (GVC) and developing digital economy have had a profound impact, which transforms China’s economy into a green and innovative one. This paper studies the intrinsic influential mechanism of GVC embeddedness and digital economy on green innovation and proposes some research hypotheses. Based on panel data of 30 Chinese provinces from 2002 to 2016, we constructed some core indicators such as GVC embeddedness, digital economy and green innovation. The ordinary panel model and spatial panel model are used to empirically test the impact of GVC embeddedness and digital economy on China’s green innovation at the provincial level. The research findings are: First, GVC embeddedness and digital economy have significant promotion effects on green innovation. Second, the development of digital economy will not only directly promote green innovation, but also indirectly promote green innovation by effectively promoting the integration of provincial economy into GVC. The results of mediating effect test show that GVC embeddedness has a partial mediating effect in the influential mechanism of digital economy to promote green innovation. Third, GVC embeddedness and green innovation have significant spatial spillover effects. It indicates that Chinese provinces (cities¹) have significantly promoted green innovation in neighboring provinces through many possible channels and mechanisms in the process of participating in GVC, and the robustness test shows the stability of the spatial spillover mechanism. The findings provide useful policy implications for China’s deeply participating in GVC, vigorously developing digital economy and promoting green innovation

Preparation and solution properties of a novel cationic hydrophobically modified polyacrylamide for enhanced oil recovery

Financial support from National Natural Science Foundation of China (51504050, 51774062), Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1601305) and Research Foundation of Chongqing University of Science & Technology (CK2016B07, CK2016Z20).Peer reviewedPostprin

Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis

Naturally fractured gas reservoirs have contributed significantly to global gas reserves and production. The classical gas-well decline analysis relies largely on Arps’ empirical decline models, or modern production decline analysis associating with pseudo-variables. The explicit original gas in place determination methodology is extended from homogeneous reservoir to naturally fractured reservoir under constant or variable bottom-hole pressure conditions in gas-well rate decline analysis. Then, the relationship between gas flow rate and average reservoir pseudo-pressure in the boundary-dominated flow period is re-derived. This formula is in the same format with the equation for homogeneous reservoir by due to the introduction of a new productivity index parameter that captures the inter-porosity flow between fracture and matrix in the natural fractured reservoir. The proposed step-by-step procedures are applied here, which enable the estimation of decline exponent and the explicit and straightforward determination of the original gas in place without any iterative calculations. Four simulated cases prove that our methodology can be successfully used in heterogeneous naturally fractured reservoirs with irregular boundary under constant or variable bottom-hole pressure conditions.Document Type: Original articleCited as: Wang, Y., Wang, J., Zhao, W., Ji, P., Cheng, S., Yu, H. Explicit original gas in place determination of naturally fractured reservoirs in gas well rate decline analysis. Advances in Geo-Energy Research, 2023, 9(2): 117-124. https://doi.org/10.46690/ager.2023.08.0

TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training

Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities. Due to noises in web-harvested text-image pairs, however, scaling up training data volume in SMCL presents considerable obstacles in terms of computational cost and data inefficiency. To improve data efficiency in VLP, we propose Text-aware Image Mixing (TiMix), which integrates mix-based data augmentation techniques into SMCL, yielding significant performance improvements without significantly increasing computational overhead. We provide a theoretical analysis of TiMixfrom a mutual information (MI) perspective, showing that mixed data samples for cross-modal contrastive learning implicitly serve as a regularizer for the contrastive loss. The experimental results demonstrate that TiMix exhibits a comparable performance on downstream tasks, even with a reduced amount of training data and shorter training time, when benchmarked against existing methods. This work empirically and theoretically demonstrates the potential of data mixing for data-efficient and computationally viable VLP, benefiting broader VLP model adoption in practical scenarios.Comment: Accepted on AAAI202

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark

Prevalence and genotyping of Norovirus in environment and food handlers of catering services and hotels

Objective To investigate the prevalence and genotyping of Norovirus in environment and food handlers in catering services and hotels. Methods A total of 40 catering services and 10 hotels were selected as the sampling sites in this study and 4 environment samples and 2 food-handler fecal samples were collected from each site. RNA was extracted and preliminary analyzed for Norovirus by real-time polymerase chain reaction (PCR). Partial opening reading frames 1 (ORF1) sequences were amplified by reverse transcription-polymerase chain reaction (RT-PCR), followed by sequence and phylogenetic analysis. Results One mop sink swab out of 200 environment samples (0.5%, 1/200) and 3 out of 100 food handlers fecal samples (3.0%, 3/100) were positive for Norovirus. The genotyping of Norovirus revealed that one belonged to GII. 17 genotype and two belonged to GI. 3 genotype. Conclusion The transmission risk of Norovirus in catering services and hotels should be paid more attention to and hygienic management should be strengthened. Health education of food handlers to prevent the transmission of Norovirus should be strengthened