Using the census data from 2000-2015 and a pseudo-event study design, we estimate the motherhood penalty in China and explore its association with declining fertility. We find that one-third of working women leave their jobs in the year when they give birth, and the penalty persists for over eight years. The motherhood penalty increases significantly across almost all provinces during this period, and provinces with larger increases in the penalty experience greater declines in fertility rates. Using a mover-based design, we find that the rising motherhood penalty has caused a significant decline in the total fertility rate.
Existing methods for moving sound source localization and tracking face significant challenges when dealing withan unknown number of sound sources, which substantially limits their practical applications. This paper proposes amoving sound source tracking method based on source signal envelopes that does not require prior knowledge ofthe number of sources. First, an encoder-decoder attractor (EDA) method is used to estimate the number of sourcesand obtain an attractor for each source, based on which the signal envelope of each source is estimated. This signalenvelope is then used as a clue for tracking the target source. The proposed method has been validated throughsimulation experiments. Experimental results demonstrate that the proposed method can accurately estimate thenumber of sources and precisely track each source.
Multi-focus image fusion (MFIF) is a critical technique for enhancing depth of field in photography, producing an all-in-focus image from multiple images captured at different focal lengths. While deep learning has shown promise in MFIF, most existing methods ignore the physical model of defocus blurring in their neural architecture design, limiting their interoperability and generalization. This paper presents a novel framework that integrates explicit defocus blur modeling into the MFIF process, leading to enhanced interpretability and performance. Leveraging an atom-based spatially-varying parameterized defocus blurring model, our approach first calculates pixel-wise defocus descriptors and initial focused images from multi-focus source images through a scale-recurrent fashion, based on which soft decision maps are estimated. Afterward, image fusion is performed using masks constructed from the decision maps, with a separate treatment on pixels that are probably defocused in all source images or near boundaries of defocused/focused regions. Model training is done with a fusion loss and a cross-scale defocus estimation loss. Extensive experiments on benchmark datasets have demonstrated the effectiveness of our approach.
This article, which is an extension of the previous work of the authors (Tanveer and Ahmad (2022)) on yaw dynamics, investigates the modeling and control of heave degree-of-freedom of a compact custom designed ROV. Wherein, nonlinear data-driven modeling strategy is adopted to develop a high-fidelity heave dynamic model. The proposed modeling approach uses open-loop real-time experimental data to derive a high-fidelity NARX model of the vehicle. The resulting model accommodates the dynamics of the system in addition to the tether dynamics. The advantage of this approach is its ability to eliminate the need for intricate controller tuning. The identified model consistently demonstrated fitness scores ranging from 82% to 92% in both self-validation and cross-validation tests conducted on distinct datasets. This relative advantage is exemplified in real-time through the testing of a Genetic Algorithm Proportional-Integral (GAPI) controller. The performance of GAPI is subsequently compared with the relatively recent Marine Predators Algorithm (MPA) and the more conventional root-locus tuned PI controllers. The experimental results demonstrate that GAPI provides the most favorable response, achieving a 35%, 76%, and 44% improvement in rise time, percent overshoot and peak time, respectively. Furthermore, the controller effort required by GAPI running on an ATmega328 chipset is 22% less than its counterparts.
Generally, facilitating a slick switch to cleaner cooking fuels for households in the countryside has been a challenge. Based on survey data from the China Family Panel Survey for 2022 and provincial statistics, this study examined the effects of relevant factors on household cooking fuel utilization in rural China at the province, household, and neighborhood levels using a multilevel spatial logit model. The findings clearly indicate that new quality productive forces, household cultural consumption, and neighborhood effects significantly support households to adopt cleaner cooking fuels in rural areas. Further studies show that policies on green financial reforms and innovation, straw-burning ban guidelines, and atmospheric priority control areas activate new productive forces to support rural households’ switch to cleaner cooking fuels. These findings contribute to knowledge and action programs for the green transformation of rural energy consumption.
The poor endurance of hafnium oxide (HfO2)-based ferroelectric field-effect transistors (FeFETs) limits their applications. From a novel perspective of ferroelectric domain engineering, we propose and fabricate a high endurance HfO2-based FeFET with monolayer graphene (GR) inserted in the gate oxide for the first time. The introduction of GR between the ferroelectric (FE) layer and the interfacial layer (IL) increases the number of domains in the ferroelectric (FE) layer and reduces the electric field of the IL. Meanwhile, the low density of states (DOS) of monolayer GR suppresses the charge injection to further optimize the endurance. Experimental results show that the endurance of the GR-intercalated FeFET (GR-FeFET) exceeds 108 cycles, which is more than 2 orders of magnitude higher than that of the conventional FeFET. The gate leakage is also effectively suppressed by the GR layer. This work opens a new avenue for improvement of the endurance of FeFETs and demonstrates GR-FeFETs as potential candidates for next-generation embedded memory applications.
