Who Is More Mobile in Response to Local Demand Shifts in China?

In this paper, we use two nationally representative datasets to examine the population adjustment of demographic groups in response to regional demand shifts between 2000 and 2005. Results from OLS regressions show that population changes of less educated groups are more associated with changes in total city working hours than population changes of educated groups. These findings explain increases in skill premia in coastal regions after China's entry into the WTO, but it does not mean that the former groups are more responsive to demand shocks, because changes in city working hours also reflect other forces such as supply shocks. Using an IV strategy, we find that educated workers are more responsive to demand shocks than those who are less educated. In addition, old subgroups are particularly inert in responding to demand shocks. Our results also suggest that China's household registration (Hukou) system prevents the mobility of urban residents more than it prevents the mobility of rural residents. We propose that Hukou reform should not only abolish the agricultural vs. non-agricultural division, but also change the decentralized (local vs. non-local) feature of the system

Astragaloside IV inhibits pathological functions of gastric cancer-associated fibroblasts through regulation of HOXA6/ZBTB12 axis

[email protected], [email protected] Cancer-associated fibroblasts (CAFs) play critical roles in the tumor microenvironment and exert tumor-promoting or tumor-retarding effects on cancer development. Astragaloside IV has been suggested to rescue the pathological impact of CAFs in gastric cancer. This study aimed to investigate the potential mechanism of astragaloside IV in the regulation of CAF pathological functions in gastric cancer development. Homeobox A6 (HOXA6), and Zinc Finger and BTB Domain Containing 12 (ZBTB12) are highly expressed in gastric CAFs compared with normal fibroblasts (NFs) based on the GSE62740 dataset. We found that astragaloside IV-stimulated CAFs suppressed cell growth, migration, and invasiveness of gastric cancer cells. HOXA6 and ZBTB12 were downregulated after astragaloside IV treatment in CAFs. Further analysis revealed that HOXA6 or ZBTB12 knockdown in CAFs also exerted inhibitory effects on the malignant phenotypes of gastric cells. Additionally, HOXA6 or ZBTB12 overexpression in CAFs enhanced gastric cancer cell malignancy, which was reversed after astragaloside IV treatment. Moreover, based on the hTFtarget database, ZBTB12 is a target gene that may be transcriptionally regulated by HOXA6. The binding between HOXA6 and ZBTB12 promoter in 293T cells and CAFs was further confirmed. HOXA6 silencing also induced the downregulation of ZBTB12 mRNA and protein in CAFs. Astragaloside IV was demonstrated to regulate the expression of ZBTB12 by mediating the transcriptional activity of HOXA6. Our findings shed light on the therapeutic value of astragaloside IV for gastric cancer

OmniVid: A Generative Framework for Universal Video Understanding

The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal, different tasks often rely on distinct model architectures and annotation formats. In contrast, natural language processing benefits from a unified output space, i.e., text sequences, which simplifies the training of powerful foundational language models, such as GPT-3, with extensive training corpora. Inspired by this, we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way, a variety of video tasks could be formulated as video-grounded token generation. This enables us to address various types of video tasks, including classification (such as action recognition), captioning (covering clip captioning, video question answering, and dense video captioning), and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture, following a generative framework. Through comprehensive experiments, we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or competitive results on seven video benchmarks, providing a novel perspective for more universal video understanding. Code is available at https://github.com/wangjk666/OmniVid.Comment: Accepted by CVPR 202

A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incor�porates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component’s prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. The simulation results indicate that the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF)

Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels

First fracture characteristics of main roof plate structure with goaf (coal pillar) on both sides and elastic-plastic foundation boundary

In order to study the fracture position and engineering significance of the main roof plate structure under the condition of goaf on both sides (coal pillars), the double plasticized foundation boundary mechanical model of the main roof plate structure considering the elastic-plastic deformation of coal and the width and support capacity weakening of coal pillar on both sides is constructed. Based on the finite difference algorithm and the principal moment breaking criterion, the shape characteristics, location attributes and overall position characteristics of the main roof fault line above the asymmetric coal pillars area and the long side solid coal area are systematically calculated, and the new conclusions and important engineering significance of the new model are clarified by comparing with the traditional models from seven levels and four pairs of areas in transverse and longitudinal directions. The conclusions are as follows: ① The asymmetric coal pillars parameters on both sides have little influence on the main roof principal bending moment and fracture position above the long side solid coal area, but significantly affect the principal bending moment, position and fracture shape of the main roof above the coal pillar areas respectively. There are three types of evolution patterns of the main roof fracture line above the coal pillar areas on both sides (strong/wide coal pillar area + weak/narrow coal pillar area). With the increase of main roof thickness and elastic modulus, while coal pillars width, coal pillar bearing capacity and working face span decrease, its evolution law is as follows: asymmetric “continuous single arc + continuous single arc”→ “continuous single arc + open discontinuous double short arc”→ asymmetric “open discontinuous double short arc + open discontinuous double short arc”. ② The fracture line of the main roof above the long side solid coal area mainly has three types of location attributes. With the increase of main roof thickness and elastic modulus, while the plastic zone width and plasticization degree of solid coal and working face span decrease, its evolution law is as follows: the fracture line is above the plastic coal area (C-S type) → elastoplastic coal boundary area (C-TS type) → elastic coal area (C-T type). ③ With the increase of coal pillars width, coal pillar bearing capacity and working face span decrease, and considering the location attribute of the fracture line, the fracture mode and evolution law of the whole area of the main roof are as follows: the mode of C-S ()→the mode of C-TS ()→the mode of C-T ()→ mode of C-T ()→the mode of C-T (). Aiming at the three kinds of mechanical models for studying the fracture of the main roof plate structure with goaf (coal pillar) on both sides, the important differences of the three kinds of models are compared from seven levels, and its important engineering role is expounded from four transverse areas (front and rear of the mining area, coal pillar areas on both sides) and four longitudinal areas (asymmetric left coal pillar underlying and underlying mining space output/input coal pillar/body)