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.
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.
Objective: a hygienic assessment of working conditions and an analysis of the morbidity of aircraft workers. Materials and methods : the study was carried out on the basis of data from the Office of ...the Federal Service for Supervision of Consumer Rights Protection and Human Well-Being in the Republic of Tatarstan (Tatarstan) and the Center for Occupational Pathology Scientific and Clinical Center for Preventive Medicine of the Institute of Fundamental Medicine and Biology of the Kazan (Volga Region) Federal University. Results: hygienic monitoring of the working conditions of employees of the aviation plant showed a combined effect of harmful factors of the production environment and the labor process, corresponding to classes 3.1–3.2 in a number of professions. Among the diseases identified in employees of the enterprise, diseases of the eye and its accessory apparatus, circulatory organs and hearing predominate. According to the results of the medical examination, 6.4% of the subjects were found to have a suspicion of occupational chronic bilateral sensorineural hearing loss. Conclusions : research results indicate that there is a risk of developing occupational pathology as a result of exposure to physical factors. The employer was given recommendations to improve working conditions and preserve the health of workers.
The transfer learning method represented by domain adaptation (DA) can effectively improve the prediction accuracy of rolling bearings' remaining useful life (RUL) under different working conditions. ...However, the difference in the bearing degradation process under the same working conditions limits the reliability and generalization of the transfer RUL prediction model. Owing to the aforementioned problems, this study proposed an RUL transfer predicting method for rolling bearings based on working conditions' common benchmark. An attention mechanism autoencoder is proposed to extract the common benchmark under each working condition and improve the commonness between deep features. The dynamic benchmark constraint under the same working conditions was proposed to ensure the reliability of a common benchmark and improve the prediction accuracy in the process of benchmark transfer under different working conditions. Verified by XJTU-SY bearing datasets, the proposed method can effectively obtain a common benchmark that can be used for DA under various working conditions. In the experimental design of six sets of RUL prediction tests under different working conditions, more than 50% of the experimental tasks have better prediction results using the proposed method. The proposed approach increases the overall prediction accuracy to 11.74% compared with the method without DA. Experimental results show that the proposed method can better meet the needs of intelligent operation and maintenance in practical engineering.
Background: High rates of maltreatment and low caregiving quality in institutional care settings have been identified in various low-and middle-income countries. Many studies have focused on children ...living in institutions. Objective: This study investigates the prevalence of maltreatment, caregiver-specific, orphanage-context factors, and their relation to maltreatment of orphans from the caregivers’ perspective. Participants and setting: In this cross-sectional study, 227 caregivers (67% female) with a mean age of 36.84 years (SD
age
= 12.89) of 24 childcare centres in Dar es Salaam, Tanzania participated. Methods: The primary outcome was the maltreatment of children assessed through structured interviews of caregivers. Further information on individual (like work-related stress) and structural variables (working conditions) was gathered. Results: Caregivers’ work under extreme and exhausting conditions, with very high caregiver-child ratios, low salaries, and almost no possibility to recover. Results also showed significant differences in the maltreatment level and structural and individual factors (e.g., payment and days of entitlement) between the orphanages. Caregiver’s positive attitudes towards violence ( ƒ2attitudes = 0.19) and difficulties in the relationship with children ( ƒ2relationship = 0.05) significantly predicted maltreatment with moderate and small effects. Conclusions: The overall poor working conditions highlight the urgent need to reduce the caregivers’ burden. In addition to this, addressing caregivers’ positive attitudes and improving their interaction competencies with children may be a starting point to prevent maltreatment of children. Further investigation of structural factors contributing to maltreatment is essential to develop recommendations for the improvement and re-organization of childcare institutions.
In order to evaluate the beneficiation plant environment in a more scientific and reasonable way, this paper took the workshop environment of the beneficiation plant as the research object. This ...paper divided the beneficiation plant into 7 evaluation units according to its functions. The evaluation indices are dust, noise, light environment, microclimate, benzene, toluene and xylene. This paper combines the G1 method and the entropy weight method to evaluate the weight of each evaluation index, the element extension model of the concentrator working environment is established by the element analysis method, and the matter element analysis method is used to establish an evaluation index system of a beneficiation plant in East China. The results show that the evaluation level of the breaking workshop and the auxiliary facilities are unqualified, the auxiliary facility is qualified, the culling workshop, culled yard and accessory building are medium, the screening workshop and grinding workshop are good.
•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.
•One in two PhD students experiences psychological distress; one in three is at risk of a common psychiatric disorder.•The prevalence of mental health problems is higher in PhD students than in the ...highly educated general population, highly educated employees and higher education students.•Work and organizational context are significant predictors of PhD students’ mental health.
Research policy observers are increasingly concerned about the potential impact of current academic working conditions on mental health, particularly in PhD students. The aim of the current study is threefold. First, we assess the prevalence of mental health problems in a representative sample of PhD students in Flanders, Belgium (N=3659). Second, we compare PhD students to three other samples: (1) highly educated in the general population (N=769); (2) highly educated employees (N=592); and (3) higher education students (N=333). Third, we assess those organizational factors relating to the role of PhD students that predict mental health status. Results based on 12 mental health symptoms (GHQ-12) showed that 32% of PhD students are at risk of having or developing a common psychiatric disorder, especially depression. This estimate was significantly higher than those obtained in the comparison groups. Organizational policies were significantly associated with the prevalence of mental health problems. Especially work-family interface, job demands and job control, the supervisor’s leadership style, team decision-making culture, and perception of a career outside academia are linked to mental health problems.
•A novel method called DRHRML is proposed for bearing fault diagnosis with small samples under different working conditions.•Improved sparse denoising autoencoder (ISDAE) is proposed to preprocess ...the raw vibration data.•Two novel task datasets are constructed for verifying the proposed method.
Recently, intelligent fault diagnosis has made great achievements, which has aroused growing interests in the field of bearing fault diagnosis due to its strong feature learning ability. Sufficient bearing fault samples are taken for granted in existing intelligent fault diagnosis methods generally. In practice, however, the lack of fault samples has been a knotty problem. Therefore, in this paper, a novel method called data reconstruction hierarchical recurrent meta-learning (DRHRML) is proposed for bearing fault diagnosis with small samples under different working conditions. This approach contains data reconstruction and meta-learning stages. In the data reconstruction stage, noise is reduced and the useful information hidden in the raw data is extracted. In the meta-learning stage, the proposed method is trained by a recurrent meta-learning strategy with one-shot learning way. This approach is demonstrated on the bearing fault database with 92 working conditions from Case Western Reserve University and with 56 working conditions from laboratory. Results show that the proposed method is effective for bearing intelligent fault diagnosis with small samples under different working conditions.
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