With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%，31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Abstract: This study aims to set up a system to evaluate nursing competencies in disaster preparedness for undergraduates and influencing factors in China. The evaluation system was established by using the modified Delphi. There were 3 phases in this study: 1) an environmental scan; 2) setting framework; 3) adjusting indicators based on the modified Delphi. The questionnaire for these undergraduates lied in their basic information and assessment of nursing competencies in disaster preparedness. Based on the survey, the average score of nursing competencies in disaster preparedness for undergraduate nursing students surveyed was 3.10 (out of 5). In addition, gender, grade, inhabitation, and disaster drills of the students were four factors affecting nursing competencies of undergraduates. This study indicates that the present nursing competencies in disaster preparedness for undergraduates are weak, and it is essential to strengthen nursing education in disaster preparedness for undergraduates.

In this work, we report the synthesis of a new thiosemicarbazone-based drug of N'-(di(pyridin-2-yl)methylene)-4-(thiazol-2-yl)piperazine-1-carbothiohydrazide (HL) featuring a thiazole spectator for the efficient coordination with Cu(II) to give [CuCl(L)]2 (1) and [Cu(NO3)(L)]2 (2). Both 1 and 2 exhibit dimeric structures ascribed to the presence of di-2-pyridylketone moieties that demonstrate dual functions of chelation and intermolecular bridging. HL, 1 and 2 are highly toxic against hepatocellular carcinoma cell lines Hep-G2, PLC/PRF/5, and HuH-7, with half maximal inhibitory concentration (IC50) values as low as 3.26 nmol/mL (HL), 2.18 nmol/mL (1), and 2.54 × 10−5 nmol/mL (2) for PLC/PRF/5. While the free ligand HL may elicit its anticancer effect via sequestration of bio-relevant metal ions (i.e. Fe3+ and Cu2+), 1 and 2 are also capable of generating cytotoxic reactive oxygen species (ROS) to inhibit cancer cell proliferation. Our preliminary pharmacokinetic studies revealed that oral administration (per os, PO) of HL has a significantly longer half-life t1/2 of 21.61 ± 9.4 h, nearly doubled as compared to that of the intravenous (i.v.) administration of 11.88 ± 1.66 h, certifying HL as an effective chemotherapeutic drug via PO administration.