Abstract
Hybrid quantum systems integrating semiconductor quantum dots (QDs) and atomic vapours become important building blocks for scalable quantum networks due to the complementary strengths of ...individual parts. QDs provide on-demand single-photon emission with near-unity indistinguishability comprising unprecedented brightness—while atomic vapour systems provide ultra-precise frequency standards and promise long coherence times for the storage of qubits. Spectral filtering is one of the key components for the successful link between QD photons and atoms. Here we present a tailored Faraday anomalous dispersion optical filter based on the caesium-D
1
transition for interfacing it with a resonantly pumped QD. The presented Faraday filter enables a narrow-bandwidth (Δ
ω
=2
π
× 1 GHz) simultaneous filtering of both Mollow triplet sidebands. This result opens the way to use QDs as sources of single as well as cascaded photons in photonic quantum networks aligned to the primary frequency standard of the caesium clock transition.
Objectives The aims of this position paper are to (i) summarize research on precarious employment (PE) in the context of occupational health; (ii) develop a theoretical framework that distinguishes ...PE from related concepts and delineates important contextual factors; and (iii) identify key methodological challenges and directions for future research on PE and health. Methods This position paper is the result of a working group consisting of researchers from the EU, Turkey and the USA, who have discussed the issue over the course of six months (October 2018-April 2019), meeting both online and face-to-face on several occasions. Results The lack of a common theoretical framework of PE hinders it from becoming an established part of occupational and public health research. There are also issues regarding operationalization in surveys and registers. Further, previous research on PE and health suffers from methodological limitations including inadequate study designs and biased assessments of exposure and outcomes. PE is highly dependent on contextual factors and cross-country comparison has proven very difficult. We also point to the uneven social distribution of PE, ie, higher prevalence among women, immigrants, young and low educated. We propose a theoretical framework for understanding precarious employment as a multidimensional construct. Conclusions A generally accepted multidimensional definition of PE should be the highest priority. Future studies would benefit from improved exposure assessment, temporal resolution, and accounting for confounders, as well as testing possible mechanisms, eg, by adopting multi-level and intersectional analytical approaches in order to understand the complexity of PE and its relation to health.
Titelbild: Bautechnik 8/20
Die Bautechnik,
August 2020, 20200801, Letnik:
97, Številka:
8
Journal Article
Zum Titelbild: Von der Industrie‐ in die Netzwerkökonomie – der derzeit massiv anziehende digitale Strukturwandel bringt neue Arbeitsorganisationen und Arbeitsstile mit sich. Attribute wie „agil, ...teamvernetzt” und „innovationssteigernd” stehen für das, was „New Work” heute meint. Dass diese auch „New Architecture” und „New Working Environment” in Gestalt flexibler Lösungen braucht, ist zwar logisch, hat aber mit der Realität der Arbeitswelt zumeist noch wenig zu tun. Nicht so im Modulbau. Wo ein Neubau als Massivgebäude wenig planerischen Spielraum lässt, hat die ALHO‐Gruppe als wirtschaftliche und maximal flexible Alternative das neue Modulbau‐Mietsystem FAGSI ProCOMFORT entwickelt. Standardisierte Modul‐Bausteine mit hochwertigen Gebäudedetails zum Mieten schließen die Lücke zwischen dauerhaften Modul‐ und temporären Containerbauten und bieten somit das Beste aus zwei Welten. (Foto: FAGSI)
•A correlation analysis algorithm for IMFs reconstruction is proposed.•The CorAA can extract main feature of fault modes and eliminate useless information.•The proposed method can diagnose accurately ...under different working conditions.•The fusion method is superior to peer methods under identical working conditions.
The effective and accurate diagnosis of the fault of a gearbox is crucial. However, differences in working condition significantly affect the energy of the original vibration signals of a gearbox, which makes it difficult to distinguish the faulty signals from normal signals. To solve this problem, this paper proposes an integrated method based on complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SampEn) and the correlation analysis algorithm (CorAA) for the fault diagnosis of a gearbox under different working conditions. In this method, CEEMD is used to decompose the raw vibration signals into sets of finite intrinsic mode functions (IMFs). Then, the correlation coefficients between the raw signal and each IMF are calculated using the CorAA. Subsequently, the IMFs with large correlation coefficients are selected for a probabilistic neural network (PNN) to classify the fault patterns. Finally, two cases are studied based on experimental gearbox fault diagnosis data, and the integrated method achieves classification rates of 97.50% and 95.16%. The proposed approach outperforms all other existing methods considered, thus validating its effectiveness and superiority.
•A metric adversarial domain adaptation approach is proposed to successfully achieve cross-domain RUL prediction.•A feature extraction scheme with a dual self-attention module is developed to learn ...features with multi-scale semantics.•A supervised positive contrastive module is designed to maximize the target-specific mutual information.
Many existing domain adaptation-based methods try to derive domain invariant features to address domain shifts and obtain satisfactory remaining useful life (RUL) of bearings under multiple working conditions. However, most methods may not consider local semantics about degradation features and mutual information from target-specific data when aligning distribution discrepancies, thus resulting in limitations. Additionally, the use of contrastive learning to maintain mutual information may introduce unstable negative samples. To overcome these issues, a metric adversarial domain adaptation approach (MADA) is proposed to evaluate the bearing RULs under multiple working conditions. More specifically, an adversarial domain adaptation architecture with a supervised positive contrastive module is developed to consider mutual information without a negative sample, further learning domain invariant features. Also, the dual self-attention module is designed to extract multi-scale contextual semantics between degradation features. Meanwhile, extensive experiments are conducted in twelve cross-domain scenarios for two bearing cases. The experimental results show that the proposed method is more competitive.
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.
IMPORTANCE: 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). OBJECTIVE: To evaluate academic medical culture, faculty mental health, and their relationship. DESIGN, SETTING, AND PARTICIPANTS: 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. EXPOSURES: Minoritized identity based on gender, race and ethnicity, and LGBTQ+ status. MAIN OUTCOMES AND MEASURES: 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. RESULTS: 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. CONCLUSIONS AND RELEVANCE: 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.
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