The agricultural sector in Morocco heavily relies on female labor, playing a crucial role in production and providing specific expertise. However, despite their invaluable contributions, female ...agricultural workers face arduous working conditions and environmental challenges that threaten their health and well-being. The main objective of this research is to unveil the constraints they encounter, shedding light on various aspects such as long working hours, physically demanding environments, and health risks due to climate change and industrial fertilizers. It is also important to understand and analyze gender stereotypes that may influence these working conditions. To avoid considering this group of women as a homogeneous entity, this study adopts a microsociological approach based on direct observation of the workers in the fields and farms, as well as semi-structured interviews. The aim is to closely observe the female workers and listen to their narratives to better understand their experiences and the conditions in which they work. The study’s findings confirm that, in addition to gender differentiations in space and stigmatizing representations from their surroundings, female workers face access and exercise barriers in their work and suffer detrimental consequences to their health and well-being resulting from their working environment.
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)
In practical industrial applications, the working conditions of machinery are changing with long-term operation, and the health status is declining with the degradation of crucial components. When ...the working condition changes, prior diagnosis models cannot be generalized from one condition to another. To solve this challenging issue, in this article a robust weight-shared capsule network (WSCN) is introduced for intelligent fault diagnosis of machinery under varying working conditions. First, taking raw accelerometer signals as inputs, one-dimensional convolutional neural network is constructed to extract discriminative characteristics. Second, various capsule layers based on multistacked weight-shared capsules are developed to enhance the generalization performance for further fault classification. Finally, margin loss function as well as agreement-based dynamic routing algorithm are employed to optimize the WSCN. In this article, two diagnosis cases are carried out to demonstrate the generalization performance of the WSCN which obtains higher accuracy under varying working conditions than that of other state-of-the-art methods.
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.
•A hybrid cooling system is proposed for lithium-ion battery pack in EVs.•Cooling effect and energy consumption of the hybrid system are tested.•Hybrid system reduces Tmax to 29.6 °C and ΔT to 1.6 °C ...in dynamic working condition.•Desired thermal performance is achieved with a 62% reduction in energy consumption.
Lithium-ion batteries have been widely used to propel electric vehicles (EVs) owing to their high energy density, long lifespan, and high stability. However, the inevitable battery heat generation, particularly when there is a rapid increase in power under dynamic working conditions, threatens the safety and performance of EVs. In this study, we develop a hybrid battery thermal management system incorporating micro heat pipe arrays, convective air, and intermittent spray water. The heat pipes siphon the heat from the inside of the battery pack to the outside, and convective air dissipates heat during the normal operation of the EVs, while further cooling is achieved via intermittent spray water at high-power operations. For a 75 Ah lithium-ion battery pack under dynamic working conditions, the proposed hybrid system enables the maximum temperature to be reduced to 29.6°C and the temperature non-uniformity to be 1.6°C, which are 21% and 57% lower than those of thermal management systems without water spraying functions, respectively. Additionally, the energy consumption of the hybrid thermal management system is 4.9 Wh, only accounting for 1.8% of the total battery pack power capacity. Given these advantages, it is expected that the proposed thermal management system is a promising tool to address the practical thermal problems of lithium-ion battery packs used in EVs.
•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.
•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.
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