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Against the backdrop of the fourth technological revolution, industrial intelligence (INDI) represented by industrial robots has rapidly developed. This evolution provides favorable opportunities for precise decision-making in pollution control and achieving China's "dual carbon" goals. Previous studies have mainly discussed the economic effects of INDI from the perspective of the labor market. This study shifts its focus to examining the impact of INDI on the land green utilization efficiency (LGUE) in cities. Using the panel data of Chinese cities spanning 2009–2021, this study empirically tests the effect and transmission mechanism of INDI on LGUE. We find that urban INDI significantly enhances LGUE. In terms of its transmission mechanism, INDI drives improvements in urban LGUE through technological progress, energy structure optimization, and industrial structure upgrading. Urban infrastructure construction and financial agglomeration level can further strengthen the positive impact of INDI on LGUE. In addition, the improvement in LGUE due to INDI is more significant in non-resource-based and large-sized cities than resource-based and small and medium-sized cities. Therefore, each region should enhance the integration of intelligent technology with traditional industrial manufacturing. Doing so is essential to establish comprehensive assessment indicators that balance environmental protection and economic growth, strengthen regional information infrastructure construction, ensure steady financial flow, and support green development initiatives across regions. [ABSTRACT FROM AUTHOR]

Investigations and research on the giant African snail (GAS) mainly focus on Achatina fulica. However, in recent years, a more harmful GAS, Achatina immaculata, has been reported. In order to understand the invasive status of A. immaculata in China, we take Shenzhen, a coastal city in Southern China, as an example to carry out an ecological survey on the field populations of the two species of GAS in various districts. We explore the differences in the invasive characteristics of the two species of snails in terms of their dietary intake, cold adaptation and drought resistance. The results indicate that, based on the phylogenetic tree analysis, more than half of the sampled snails exhibit higher similarity to A. immaculata. The number of wild A. immaculata is significantly greater than that of A. fulica, and 70.64% of the 746 GAS are A. immaculata. At the same time, it is also found that the maximum shell length of A. immaculata is 135.83 mm, with an average shell length of 76.00 mm, which is significantly different from the average shell length of A. fulica (56.57 mm, p < 0.01). The food intake assay shows that there is no difference in the food preferences of the two species, but the food demand of A. immaculata is significantly greater than that of A. fulica (2.32 fold, p < 0.01). In the cold adaptation assay, A. immaculata recovers from the cold dormancy state significantly faster than A. fulica (1.92 fold, p < 0.05), and the speed with which A. immaculata enters the dormancy state in the drought environment is significantly slower than that of A. fulica (0.706 fold, p < 0.05). With the characteristics of a large body size, large food intake and strong resistance to cold and drought resistance, A. immaculata has the potential to be dominant in competition with A. fulica in the same ecological niche, and it has become the main invasive species of GAS in Shenzhen. [ABSTRACT FROM AUTHOR]

Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies. [ABSTRACT FROM AUTHOR]

Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling. [ABSTRACT FROM AUTHOR]

Aiming at the thermal corrosion issue of Cr-containing MCrAlY (M = Fe, Co, Ni) materials used in marine gas turbine hot-end components in a humid marine environment with highly corrosive chloride salts, Mo is anticipated to replace Cr, thereby enhancing the corrosion resistance and mechanical properties. However, excessive Mo facilitates oxidation and creates volatile metal oxides, ultimately jeopardizing the integrity of the oxide film. The alloy composition in the FCC_Ll2+BCC_B2+Ni5Y ternary phase region was designed according to the calculated phase diagrams of the Ni-Mo/Cr-Al-Y system. The influence and mechanism of replacing Cr with minor Mo on phase composition and high-temperature oxidation resistance were investigated. The high-temperature oxidation mass-gain experiments at 750 t indicate that a small amount of Mo (atomic fraction of 0. 5%) promotes the formation of BCC_B2 phase and facilitates the transformation of surface oxide films on the alloy from θ-Al2O3 to stable αα-Al2O3, which is conducive to the formation of dense α-Al2O3 oxide layer during long-term service, thus improving the oxidation resistance performance of the alloy. However, the addition of atomic fraction of 2. 5% Mo causes the transformation of ordered BCC_B2 phase to Mo-rich BCC_A2 phase, which is not conducive to the improvement of oxidation resistance performance. Thus, the present work indicate that the combination of theoretical calculations and experimental verification may provide a effective guidance for the composition design of novel materials with customized structures and properties. [ABSTRACT FROM AUTHOR]

Klebsiella michiganensis is a recently emerging human pathogen causing nosocomial infections. This study aimed to characterize the complete genome sequence of a clinical Klebsiella michiganensis strain KMIB106 which exhibited extensive drug-resistance. The whole genome of the strain was sequenced using PacBio RS III systems and Illumina Nextseq 500. Annotation, transposable elements and resistance gene identification were analyzed by RAST, prokka and Plasmid Finder, respectively. According to the results, KMIB106 was resistant to multiple antimicrobials, including carbapenems, but it remained susceptible to aztreonam. The genome of KMIB106 consisted of a single chromosome and three predicted plasmids. Importantly, a novel KPC plasmid pB106-1 was found to carry the array of resistance genes in a highly different order in its variable regions, including mphA, msrE, mphE, ARR-3, addA16, sul1, dfrA27, tetD and fosA3. Plasmid pB106-2 is a typical IncFII plasmid with no resistant gene. Plasmid pB106-IMP consists of the IncN and IncX3 backbones, and two resistance genes, blaIMP-4 and blaSHV-12, were identified. Our study for the first time reported an extensively drug-resistant Klebsiella michiganensis strain recovered from a child with a respiratory infection in Southern China, which carries three mega plasmids, with pB106-1 firstly identified to carry an array of resistance genes in a distinctive order, and pB106-IMP identified as a novel IncN-IncX3 cointegrate plasmid harboring two resistance genes blaIMP-4 and blaSHV-12. [ABSTRACT FROM AUTHOR]