Aqueous zinc-ion batteries are realistic candidates as stationary storage systems for power-grid applications. However, to accelerate their commercialization, some important challenges must be ...specifically tackled, and appropriate experimental practices need to be embraced to align the academic research efforts with the realistic industrial working conditions for stationary storage. Within this commentary article, both the open challenges and the good experimental practices are discussed in relation to their impact on the future development of the aqueous Zn-ion technology.Aqueous Zn-based batteries represent a viable and cost-effective technology for electricity grid storage. Here, the authors discuss the most challenging aspects to bridge academic and industrial research and accelerate the adoption of this class of devices on a large scale.
•We present a deep twin convolutional neural network (TDNN) with multi-domain inputs.•3 input layers is constructed to fuse multi-domain features and improve performance.•TDNN are presented for ...resisting the influence of noise and operating conditions.•It shows that its accuracy and anti-noise performance outperform existing methods.•Its accuracy on four datasets is 61%, 33%, 17% and 29% higher than other methods.
Although machine learning-based intelligent detection methods have made many achievements for diesel engine misfire diagnosis, they suffer from a certain degree of performance degradation due to the strong environmental noise and the change of working conditions in the actual application scenarios. To tackle this issue, this study presents a deep twin convolutional neural networks with multi-domain inputs (DTCNNMI) for diesel engine misfire diagnosis under strong environmental noise and different working conditions. Vibration signals of engine cylinder heads at different speeds are collected experimentally and fed into input layers of the proposed model. It constructs three input layers to combine automatically extracted time-domain, time-frequency-domain and hand-craft time-domain statistical features, improving the model performance. Twin convolutional neural networks with large first-layer kernels are presented for extracting multi-domain information of vibration signals and resist influence of environmental noise and the change of operating conditions on the final diagnosis results. The effectiveness of the proposed approach is validated on the datasets collected experimentally and by making comparison with the existing representative algorithms. The results demonstrate that the accuracy and anti-noise performance of the proposed DTCNNMI outperforms the existing algorithms. On the constructed four datasets under different working conditions, the proposed method achieved at least 97.019% accuracy even when signal-to-noise ratio was −4 dB, which is much higher than those of the other methods. Moreover, the accuracy of the proposed DTCNNMI on datasets A, B, C and D is at least 61.177%, 33.334%, 17.646% and 29.961% higher than other methods, respectively.
The promoted activity and enhanced selectivity of electrocatalysts is commonly ascribed to specific structural features such as surface facets, morphology, and atomic defects. However, unraveling the ...factors that really govern the direct electrochemical reduction of CO2 (CO2RR) is still very challenging since the surface state of electrocatalysts is dynamic and difficult to predict under working conditions. Moreover, theoretical predictions from the viewpoint of thermodynamics alone often fail to specify the actual configuration of a catalyst for the dynamic CO2RR process. Herein, we re‐survey recent studies with the emphasis on revealing the dynamic chemical state of Cu sites under CO2RR conditions extracted by in situ/operando characterizations, and further validate a critical link between the chemical state of Cu and the product profile of CO2RR. This point of view provides a generalizable concept of dynamic chemical‐state‐driven CO2RR selectivity that offers an inspiration in both fundamental understanding and efficient electrocatalysts design.
A critical link between the dynamic chemical state of Cu sites (mixed Cu+‐ and Cu0‐, Cu+‐, and Cu0‐dominated) and their unique selectivity toward the direct electrochemical reduction of CO2 (yielding C2H4/C2H5OH, CO/HCOO−, and CH4, respectively) is put forward. This may be a valuable tool for fine‐tuning the Cu surface state toward distinct CO2RR pathways.
The culture of academic medicine may foster mistreatment that disproportionately affects individuals who have been marginalized within a given society (minoritized groups) and compromises workforce ...vitality. Existing research has been limited by a lack of comprehensive, validated measures, low response rates, and narrow samples as well as comparisons limited to the binary gender categories of male or female assigned at birth (cisgender).
To evaluate academic medical culture, faculty mental health, and their relationship.
A total of 830 faculty members in the US received National Institutes of Health career development awards from 2006-2009, remained in academia, and responded to a 2021 survey that had a response rate of 64%. Experiences were compared by gender, race and ethnicity (using the categories of Asian, underrepresented in medicine defined as race and ethnicity other than Asian or non-Hispanic White, and White), and lesbian, gay, bisexual, transgender, queer (LGBTQ+) status. Multivariable models were used to explore associations between experiences of culture (climate, sexual harassment, and cyber incivility) with mental health.
Minoritized identity based on gender, race and ethnicity, and LGBTQ+ status.
Three aspects of culture were measured as the primary outcomes: organizational climate, sexual harassment, and cyber incivility using previously developed instruments. The 5-item Mental Health Inventory (scored from 0 to 100 points with higher values indicating better mental health) was used to evaluate the secondary outcome of mental health.
Of the 830 faculty members, there were 422 men, 385 women, 2 in nonbinary gender category, and 21 who did not identify gender; there were 169 Asian respondents, 66 respondents underrepresented in medicine, 572 White respondents, and 23 respondents who did not report their race and ethnicity; and there were 774 respondents who identified as cisgender and heterosexual, 31 as having LGBTQ+ status, and 25 who did not identify status. Women rated general climate (5-point scale) more negatively than men (mean, 3.68 95% CI, 3.59-3.77 vs 3.96 95% CI, 3.88-4.04, respectively, P < .001). Diversity climate ratings differed significantly by gender (mean, 3.72 95% CI, 3.64-3.80 for women vs 4.16 95% CI, 4.09-4.23 for men, P < .001) and by race and ethnicity (mean, 4.0 95% CI, 3.88-4.12 for Asian respondents, 3.71 95% CI, 3.50-3.92 for respondents underrepresented in medicine, and 3.96 95% CI, 3.90-4.02 for White respondents, P = .04). Women were more likely than men to report experiencing gender harassment (sexist remarks and crude behaviors) (71.9% 95% CI, 67.1%-76.4% vs 44.9% 95% CI, 40.1%-49.8%, respectively, P < .001). Respondents with LGBTQ+ status were more likely to report experiencing sexual harassment than cisgender and heterosexual respondents when using social media professionally (13.3% 95% CI, 1.7%-40.5% vs 2.5% 95% CI, 1.2%-4.6%, respectively, P = .01). Each of the 3 aspects of culture and gender were significantly associated with the secondary outcome of mental health in the multivariable analysis.
High rates of sexual harassment, cyber incivility, and negative organizational climate exist in academic medicine, disproportionately affecting minoritized groups and affecting mental health. Ongoing efforts to transform culture are necessary.
Organizational support theory (OST) proposes that employees form a generalized perception concerning the extent to which the organization values their contributions and cares about their well-being ...(perceived organizational support, or POS). Based on hypotheses involving social exchange, attribution, and self-enhancement, we carried out a meta-analytic assessment of OST using results from 558 studies. OST was generally successful in its predictions concerning both the antecedents of POS (leadership, employee–organization context, human resource practices, and working conditions) and its consequences (employee’s orientation toward the organization and work, employee performance, and well-being). Notably, OST successfully predicted the relative magnitudes of different relationships, influences of process variables, and mediational effects. General implications of the findings for OST and research on POS are discussed.
•The two-stage RUL prediction framework is investigated in this paper.•The two-level alarm mechanism is proposed to detect FPT of each entity adaptively.•DSCN-DTAM is built for cross-domain ...prognostic with incomplete target domain data.•Double transferable attention mechanism is designed for the fined-grained transfer.•Four transfer prognostic tasks verify the effectiveness of the proposed method.
The remaining useful life (RUL) prediction provides an essential basis for improving mechanical equipment reliability. In practical application, the variant of working conditions and incomplete degradation data seriously deteriorate the performance of the prognostic models. In order to conquer this problem, a two-stage RUL prediction method is proposed for the cross-domain prognostic task with insufficient degradation data. At first, the two-level alarm mechanism is employed to detect the first predicting time (FPT) of each mechanical entity adaptively. Then, the deep separable convolutional network with the double transferable attention mechanism (DSCN-DTAM) is proposed to construct the cross-domain prognostic model. In DSCN-DTAM, multiple regularization strategies can guide the model to extract domain-invariant features, and the double transferable attention mechanism is designed to select the degradation information with high transferability. Finally, the proposed method is verified by multiple transfer prognostic tasks designed by two bearing datasets. Compared with other methods, the proposed method shows superior performance.
The world of work is changing. Communications technologies and digital platforms have enabled some types of work to be delivered from anywhere in the world by anyone with a computer and an internet ...connection. This digitally-mediated work brings jobs to parts of the world traditionally characterized by low incomes and high unemployment rates. As such, it has been touted by governments, third-sector organizations, and the private sector as a novel strategy of economic development. Drawing on a four-year study with 65 workers in South Africa, Kenya, Nigeria, Ghana and Uganda, we examine the development implications of the gig economy on labour in Africa. We offer four analytical development dimensions through which platform-based remote work impacts the lives and livelihoods of African workers, i.e. freedom, flexibility, precarity and vulnerablity. We argue that these dimensions should be understood in a continuum to better explain the working conditions and lives of workers in the gig economy.
Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by ...multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.
•CNN is modified with stochastic pooling and Leaky rectified linear unit.•Multi-channel signals are used to pre-train a series of CNNs.•Transfer CNN is constructed with parameter transfer strategy.•A new decision fusion strategy is designed based on flexible weight assignment.
...conditions such as free accommodation, free meals, and maid service, which suggest valued and respected employees, are replaced with the grudging provision of limited mileage allowance for on-call ...shifts, which suggests that employees are viewed as commodities whose long term loyalty and morale are of no consequence.
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