Research on the economic effect of employment structure change in heterogeneous regions: evidence from resource-based cities in China

The Report on the Work of the Chinese Government in 2021 emphasised that stable employment is the foundation of national development. Therefore, adjustment of the employment structure is one of the main routes for sustainable development of resource-based cities. However, the impact of employment structure on sustained economic growth, particularly in heterogeneous regions, has not yet been determined. This study analyses China’s employment structure’s spatial evolution, using panel data from 2004 to 2018 of 115 prefecture-level resource-based cities. It explores the driving factors and spatial effects of employment structure changes on economic growth through an extended two-sector economic growth model and spatial econometric model, and proposes solutions for heterogeneous regions. The results show that the labour productivity of the employed population in the secondary industry is the most important factor affecting economic growth, but the spatial effects of employment structure adjustment on economic growth are different in heterogeneous regions. They further reveal that improving the productivity of the employed population in the secondary industry and building an industrial system according to regional advantages are the top priorities for developing the sustainable economy of resource-based cities

Analysis of microbial contamination status and influencing factors in pre-cooked food enterprises

Objective To understand the status of microbial contamination in the production of pre-cooked food, provide a basis for the sanitary control of pre-cooked food enterprises, and to make recommendations for the formulation of related product hygiene specifications and standards. Methods From the 70 pre-cooked food enterprises in Hunan Province, 5 samples were stratified to detect the settling microbe in ambient air, the total coliform and the total number of colonies in the contact surface, the adjacent contact surface, and pre-cooked food samples. Results The total coliform and the total number of colonies in the ambient air of medium-sized enterprises and the surfaces and pre-cooked vegetable samples was higher (P<0.05), and the total number of colonies on each surface of the pre-cooked vegetable production workshop is higher (P<0.05). It is easier to touch the hands of processing personnel contaminated by microorganisms (P<0.05); the total number of colonies in pre-cooked vegetable products is higher (P<0.05), and the total number of coliforms and colonies in process products are higher than those in finished products (P<0.05). Conclusion Pre-cooked food enterprises, especially medium-sized enterprises, should strengthen the hygienic requirements of production workshops and the hygienic control of processing. It is recommended that the relevant product standards and hygienic specifications should pay attention to these issues

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

Zero-Shot Point Cloud Registration

Learning-based point cloud registration approaches have significantly outperformed their traditional counterparts. However, they typically require extensive training on specific datasets. In this paper, we propose , the first zero-shot point cloud registration approach that eliminates the need for training on point cloud datasets. The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods. Specifically, we extract keypoints and features from 2D image pairs using a frozen pretrained 2D backbone. These features are then projected in 3D, and patches are constructed by searching for neighboring points. We integrate the geometric and visual features of each point using our novel parameter-free geometric decoder. Subsequently, the task of determining correspondences between point clouds is formulated as an optimal transport problem. Extensive evaluations of ZeroReg demonstrate its competitive performance against both traditional and learning-based methods. On benchmarks such as 3DMatch, 3DLoMatch, and ScanNet, ZeroReg achieves impressive Recall Ratios (RR) of over 84%, 46%, and 75%, respectively

Radiotherapy Exposure in Cancer Patients and Subsequent Risk of Stroke: A Systematic Review and Meta-Analysis

Background: Cancer patients who have undergone radiotherapy may have an increased risk of subsequent stroke. A clear and detailed understanding of this risk has not been established.Methods: A search for research articles published from January 1990 to November 2017 in the English language was conducted. Subsequent stroke risk in cancer survivors was compared using relative risk (RR) and 95% confidence intervals (CI) according to whether or not radiotherapy was given.Results: A total of 12 eligible studies were identified including 57,881 total patients. All studies were retrospective, as no prospective studies were identified. The meta-analysis revealed a higher overall risk of subsequent stroke in cancer survivors/patients given radiotherapy compared to those not given radiotherapy (RR: 2.09, 95% CI: 1.45, 3.16). In addition, compared to patients not given radiotherapy, there was an increased risk of subsequent stroke for radiotherapy treated patients with Hodgkin's lymphoma (RR: 2.81, 95% CI: 0.69, 4.93) or head/neck/brain/nasopharyngeal cancer (RR: 2.16, 95% CI: 1.16, 3.16), for patients younger than 40 years (RR: 3.53, 95% CI: 2.51, 4.97) or aged 40–49 years (RR: 1.23, 95% CI: 1.09, 1.45) and for patients treated in Asia (RR: 1.88, 95% CI: 1.48, 2.29), the United States (RR: 1.62, 95% CI: 1.01, 2.23), or in Europe (RR: 4.11, 95% CI 2.62, 6.45).Conclusions: The available literature indicates an approximate overall doubling of the subsequent stroke risk in cancer patients given radiotherapy. The elevated risk was generally statistically significant according to cancer type, baseline patient age and region or country where treatment was given. Caution is required in interpreting these findings due to the heterogeneity of populations represented and lack of standardization and completeness across published studies. Further, if real, we cannot conclude the extent to which patient, treatment and/or investigational factors are responsible for this apparent elevated risk. An objective and more detailed understanding of the risks of radiotherapy, and how to prevent them, is urgently required. It is the responsibility of all who provide cancer services to ensure that the experience of all their patients is documented and analyzed using quality registries

Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions

The nano-bridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a saw-tooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness {\Delta}{\theta}. Furthermore, the measured dependency of {\Delta}{\theta} on the critical current {I_0} from 108 NBJs turned out to be consistent with the calculated ones derived from the change in geometric dimensions. It suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of {\Delta}{\theta} versus {I_0} in three-dimensional space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.Comment: 20 pages, 10 figure

RNAi for Treating Hepatitis B Viral Infection

Chronic hepatitis B virus (HBV) infection is one of the leading causes of liver cirrhosis and hepatocellular carcinoma (HCC). Current treatment strategies of HBV infection including the use of interferon (IFN)-α and nucleotide analogues such as lamivudine and adefovir have met with only partial success. Therefore, it is necessary to develop more effective antiviral therapies that can clear HBV infection with fewer side effects. RNA interference (RNAi), by which a small interfering RNA (siRNA) induces the gene silence at a post-transcriptional level, has the potential of treating HBV infection. The successful use of chemically synthesized siRNA, endogenous expression of small hairpin RNA (shRNA) or microRNA (miRNA) to silence the target gene make this technology towards a potentially rational therapeutics for HBV infection. However, several challenges including poor siRNA stability, inefficient cellular uptake, widespread biodistribution and non-specific effects need to be overcome. In this review, we discuss several strategies for improving the anti-HBV therapeutic efficacy of siRNAs, while avoiding their off-target effects and immunostimulation. There is an in-depth discussion on the (1) mechanisms of RNAi, (2) methods for siRNA/shRNA production, (3) barriers to RNAi-based therapies, and (4) delivery strategies of siRNA for treating HBV infection

Articulatory-to-Acoustic Conversion Using BiLSTM-CNN Word-Attention-Based Method

In the recent years, along with the development of artificial intelligence (AI) and man-machine interaction technology, speech recognition and production have been asked to adapt to the rapid development of AI and man-machine technology, which need to improve recognition accuracy through adding novel features, fusing the feature, and improving recognition methods. Aiming at developing novel recognition feature and application to speech recognition, this paper presents a new method for articulatory-to-acoustic conversion. In the study, we have converted articulatory features (i.e., velocities of tongue and motion of lips) into acoustic features (i.e., the second formant and Mel-Cepstra). By considering the graphical representation of the articulators’ motion, this study combined Bidirectional Long Short-Term Memory (BiLSTM) with convolution neural network (CNN) and adopted the idea of word attention in Mandarin to extract semantic features. In this paper, we used the electromagnetic articulography (EMA) database designed by Taiyuan University of Technology, which contains ten speakers’ 299 disyllables and sentences of Mandarin, and extracted 8-dimensional articulatory features and 1-dimensional semantic feature relying on the word-attention layer; we then trained 200 samples and tested 99 samples for the articulatory-to-acoustic conversion. Finally, Root Mean Square Error (RMSE), Mean Mel-Cepstral Distortion (MMCD), and correlation coefficient have been used to evaluate the conversion effect and for comparison with Gaussian Mixture Model (GMM) and BiLSTM of recurrent neural network (BiLSTM-RNN). The results illustrated that the MMCD of Mel-Frequency Cepstrum Coefficient (MFCC) was 1.467 dB, and the RMSE of F2 was 22.10 Hz. The research results of this study can be used in the features fusion and speech recognition to improve the accuracy of recognition