Poly[[(1,10-phenanthroline-κ2 N,N′)zinc]-μ-2,5-bis­(all­yloxy)terephthalato-κ2 O 1:O 4]

The title compound, [Zn(C14H12O6)(C12H8N2)]n, is a coordination polymer forming one-dimensional infinite zigzag chains along [10] by inter­connection of ZnII atoms by 2,5-bis­(all­yl­oxy)­terephthalate anions via the carboxyl­ate groups. The ZnII atom is located on a twofold axis and is in a distorted tetra­hedral coordination formed by the two carboxyl­ate O atoms [Zn—O = 1.9647 (12) Å] and two phenanthroline N atoms [Zn—N = 2.0949 (14) Å]

Degradable composite aerogel with excellent water-absorption for trace water removal in oil and oil-in-water emulsion filtration

In this study, using chitosan (CS) and carboxymethyl cellulose (CMC) as backbone and introducing citric acid (CA)to enhance the electrostatic interaction of the system, citric acid/chitosan/carboxymethyl cellulose (CA/CS/CMC) aerogel is obtained by simple freeze-drying. CA/CS/CMC composite aerogel exhibits light weight, low density, high porosity, outstanding hydrophilic and water retention properties, and satisfactory underwater oleophobicity. The water adsorption capacity of the obtained aerogels can reach 43.87–80.28 g/g, which are far more than that of carboxymethyl cellulose and chitosan aerogels (14.27–20.08 g/g). In addition, with strong hydrophilicity, underwater oleophobicity and water retention endowed by the rough internal microstructure and the rich hydroxyl, amino, and carboxyl groups, the fabricated aerogel can also be used as a filter to achieve effective separation of oil-in-water emulsions and oil/water mixtures. The separation efficiency of aerogel for oil/water mixtures are higher than 90.7%. Because the developed preparation method is green, simple and mild and the raw materials are readily available and environmentally friendly, the obtained CA/CS/CMC aerogel with strong water absorption capacity and good separation efficiency displays a promising application in water-oil separation

A voice recognition-based digital cognitive screener for dementia detection in the community: Development and validation study

IntroductionTo facilitate community-based dementia screening, we developed a voice recognition-based digital cognitive screener (digital cognitive screener, DCS). This proof-of-concept study aimed to investigate the reliability, validity as well as the feasibility of the DCS among community-dwelling older adults in China.MethodsEligible participants completed demographic, clinical, and the DCS. Diagnosis of mild cognitive impairment (MCI) and dementia was made based on the Montreal Cognitive Assessment (MoCA) (MCI: MoCA < 23, dementia: MoCA < 14). Time and venue for test administration were recorded and reported. Internal consistency, test-retest reliability and inter-rater reliability were examined. Receiver operating characteristic (ROC) analyses were conducted to examine the discriminate validity of the DCS in detecting MCI and dementia.ResultsA total of 103 participants completed all investigations and were included in the analysis. Administration time of the DCS was between 5.1–7.3 min. No significant difference (p > 0.05) in test scores or administration time was found between 2 assessment settings (polyclinic or community center). The DCS showed good internal consistency (Cronbach’s alpha = 0.73), test-retest reliability (Pearson r = 0.69, p < 0.001) and inter-rater reliability (ICC = 0.84). Area under the curves (AUCs) of the DCS were 0.95 (0.90, 0.99) and 0.77 (0.67, 086) for dementia and MCI detection, respectively. At the optimal cut-off (7/8), the DCS showed excellent sensitivity (100%) and good specificity (80%) for dementia detection.ConclusionThe DCS is a feasible, reliable and valid digital dementia screening tool for older adults. The applicability of the DCS in a larger-scale community-based screening stratified by age and education levels warrants further investigation

Sports Participation and Anti-Epidemic: Empirical Evidence on the Influence of Regular Physical Activity on the COVID-19 Pandemic in Mainland China

This study aims to investigate the effects and influencing mechanisms of regular physical activity (RPA) on the COVID-19 pandemic. Daily data from 279 prefecture-level cities in mainland China were collected from 1 January to 17 March 2020. A two-way fixed-effects model was used to identify the causal relationship between physical activity and COVID-19, while also considering factors such as patterns of human behavior and socioeconomic conditions. The instrumental variable (IV) approach was applied to address potential endogeneity issues for a more accurate causal identification, and the mediating effect model was applied to examine the mechanisms of the influence of physical activity on the epidemic. We found that regular physical activity significantly improves individual immunity, which, in turn, leads to a reduction in the probability of being infected with COVID-19. Furthermore, we investigated the heterogeneity of the influence, finding that the negative impact of physical activity on the pandemic is more pronounced in the absence of adequate medical resources, strong awareness of prevention among residents, and fully implemented public health measures. Our results provide empirical evidence for the mechanisms of influence of physical activity on the pandemic. We would suggest that not only should physical activity be actively practiced during the pandemic, but also long-term regular exercise habits should be consciously cultivated to improve the ability of the individual immune system to better cope with sudden outbreaks of emerging infectious diseases

Expression and clinical significance of RBBP4 gene in lower-grade glioma: An integrative analysis

This study investigated the expression pattern of retinoblastoma binding protein 4 (RBBP4) gene in glioma and explored its associations with clinicopathologic characteristics and prognosis of patients. Data retrieved from the GEPIA, CGGA, HPA and TIMER databases were processed to analyze RBBP4 expression in glioma and investigate its relationship with clinicopathologic characteristics, tumor immune infiltration and prognosis in glioma patients. Immunohistochemistry was applied to determine the expression of RBBP4 protein in glioma tissue. Additionally, the Coexpedia database was visited to identify co-expressed genes for RBBP4 gene, while the Cytoscape software was run to visualize the enriched GO entries and KEGG pathways of these co-expressed genes. The expression levels of RBBP4 in lower-grade glioma (LGG) and glioblastoma (GBM) tissues were markedly elevated when compared to normal tissues (both p < 0.05). The up-regulation of RBBP4 expression was associated with an increase in WHO grade (II-IV), wild-type IDH, and 1p/19q non-codeletion (all p < 0.05). Multi-variate Cox regression analysis showed that both increased abundance of infiltrating macrophages and up-regulated RBBP4 expression independently predicted poor survival outcomes in LGG patients (both p < 0.05). Furthermore, RBBP4 expression exhibited significant positive correlations with the abundance of infiltrating B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell in LGG (all p < 0.05). Functional enrichment analyses indicated that the co-expressed genes associated with RBBP4 were highly involved in pathways such as the p53 signaling pathway, cell cycle, DNA replication, glutathione metabolism, as well as biological processes including cell cycle process, DNA replication, and DNA repair. High levels of RBBP4 are predictive for the poor survival outcome of LGG patients. RBBP4 gene, therefore, is expected to be a potential biomarker for prognosis of LGG and a target for immunotherapy

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 &lt; q &lt; 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping

Numerical Simulation and Field Test of a PDC Bit with Mixed Cutter Arrangement to Break Non-Homogeneous Granite

As the depth of petroleum drilling increases, the strata environment becomes more complex. The efficiency and lifespan of Polycrystalline Diamond Compact (PDC) drill bits fail to meet current drilling demands. However, the structure and arrangement of PDC cutters are valuable determinants of drilling efficiency, although related research still has gaps and deficiencies. This study focuses on PDC cutters in axe, triangular prism, and circular forms. It establishes an inhomogeneous granite model based on the actual measurements of granite and verifies the accuracy of this model through uniaxial compression simulation. Finite element models of three types of cutters in various combination schemes are constructed to examine rock-breaking effects, with the best scheme optimized using Box-Behnken response surface methodology. The rock-breaking process of the optimal PDC drill bit layout has been compared to that of a single cutter bit. Field drilling has demonstrated the effectiveness of a mixed cutter arrangement. The results show that the inhomogeneous granite model can be trusted. The optimal arrangement involves axe cutters in the front row and an alternate arrangement of triangular prism cutters and axe cutters in the back row. The optimal lateral and longitudinal distances for the triangular cutters from the front row of axe cutters are 10 mm and 7 mm, respectively, while those for the back row of axe cutters from the front row are 10.06 mm and 7 mm, respectively. The ROP standard deviation in the drilling process of mixed cutter bits decreases by 53.06% and 43.08% compared to axe and triangular prism cutter bits, respectively. The drilling efficiency increases by 16.8% and 16.6%, respectively, demonstrating higher efficiency and stability. Field drilling results indicate that a mixed cutter bit increases efficiency by 23.5% compared to a bit with only triangular prism cutters. This study posits that research on the combination schemes and parameters of PDC cutters can significantly enhance drilling efficiency, thereby reducing the drilling cycle and costs

A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains