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With the increasing complexity of the business environment, corporate financial planning and management face many challenges of ambiguity and uncertainty. Traditional financial decision support systems have obvious shortcomings in dealing with such problems. This paper constructs a fuzzy decision support system for financial planning and management, which realizes efficient processing of financial data by quantifying fuzzy information, reasoning based on fuzzy rules, and defuzzifying output. The experiment uses real financial data sets from multiple industries to compare the system in this paper with classic models such as the autoregressive moving average model (ARIMA) and the support vector machine model (SVM). The results show that the system in this paper performs well in multiple dimensions such as prediction accuracy and decision risk control. For example, in the financial crisis warning scenario, the comprehensive warning accuracy rate reaches 88.2%, and the false alarm rate is only 5.6%, which is significantly better than the control model. This study not only enriches the theory of financial decision-making, but also provides an efficient and practical tool for corporate financial decision-making, helping enterprises make more scientific decisions in a complex economic environment.

Liver cancer accounts for 6% of all malignancies causing death worldwide, and hepatocellular carcinoma (HCC) is the most common histological type. HCC is a heterogeneous cancer, but how the tumour microenvironment (TME) of HCC contributes to the progression of HCC remains unclear. In this study, we investigated the immune microenvironment by multiomics analysis. The tumour immune infiltration characteristics of HCC were determined at the genomic, epigenetic, bulk transcriptome and single-cell levels by data from The Cancer Genome Atlas portal and the Gene Expression Omnibus (GEO). An epigenetic immune-related scoring system (EIRS) was developed to stratify patients with poor prognosis. SPP1, one gene in the EIRS system, was identified as an immune-related predictor of poor survival in HCC patients. Through receptor-ligand pair analysis in single-cell RNA-seq, SPP1 was indicated to mediate the crosstalk between HCC cells and macrophages via SPP1-CD44 and SPP1-PTGER4 association. In vitro experiments further validate SPP1 can trigger the polarization of macrophages to M2-phenotype tumour-associated macrophages (TAMs).

The gut microbiome has emerged as a pivotal area of research due to its significant influence on the immune system and cognitive functions. Cognitive disorders, including dementia and Parkinson’s disease, represent substantial global health challenges. This review explores the relationship between gut microbiota, immune modulation, and cognitive decline, with a particular focus on the gut-brain axis. Research indicates that gut bacteria produce metabolites, including short-chain fatty acids (SCFAs), which affect mucosal immunity, antigen presentation, and immune responses, thereby influencing cognitive functions. A noteworthy correlation has been identified between imbalances in the gut microbiome and cognitive impairments, suggesting novel pathways for the treatment of cognitive disorders. Additionally, factors such as diet, environment, and pharmaceuticals play a role in shaping the composition of the gut microbiome, subsequently impacting both immune and cognitive health. This article aims to clarify the complex interactions among gut microbiota, immune regulation, and cognitive disorders, evaluating their potential as therapeutic targets. The goal is to promote microbiome-based treatments and lay the groundwork for future research in this field.

Remote sensing technology has become an indispensable core means for Earth observation. As two of the most commonly used remote sensing modalities, the fusion of optical and synthetic aperture radar (SAR) (OPT-SAR fusion) can effectively overcome the limitations of a single data source, achieve information complementarity and synergistic enhancement, thereby significantly improving the interpretation capability of multi-source remote sensing data. This paper first discusses the necessity of OPT-SAR fusion, systematically reviews the historical development of fusion technologies, and summarizes open-source resources for various tasks, aiming to provide a reference for related research. Finally, building upon recent advances in OPT-SAR fusion research and cutting-edge developments in deep learning, this paper proposes that future fusion technologies should develop in the following directions: interpretable fusion models driven by both data and knowledge, general fusion perception driven by multimodal large models, and lightweight architectures with efficient deployment strategies.

Lanthanide metal–organic frameworks (Ln-MOFs) have attracted extensive attention because of their structural adjustability and wide optical function applications. However, MOFs with a wide linear pH response and stable framework structures in acidic or alkaline solutions are rare to date. Here, we used 4,4′,4″-s-triazine-2,4,6-triyltribenzoate (H3TATB) as an organic ligand, coordinated with lanthanide ions (Eu3+/Tb3+), and synthesized a new metal–organic framework material. The material has a porous three-dimensional square framework structure and emits bright red or green fluorescence under 365 nm UV light. The carboxyl group of the ligand is prone to protonation in an acidic environment, and negatively charged OH− and ligand (TATB3−) have a competitive effect in an alkaline environment, which could affect the coordination ability of ligand. The luminescence degree of the framework decreases with the increase in the degree of acid and base. In particular, such fluorescence changes have a wide linear response (pH = 0–14), which can be used as a potential fluorescence sensing material for pH detection.

The division of the territorial space functional area is the primary method to study the rational exploitation and use of land space. The research on the Production–Living–Ecological Space (PLES) change and its motivating factors has major implications for managing and optimizing spatial planning and may open up a new research direction for inquiries into environmental change on a global scale. In this study, the transfer matrix and landscape pattern index methods were used to analyze the temporal changes as well as the evolution features of the landscape pattern of the PLES in the Chaohu Lake Basin from 2000 to 2020. Using principal component analysis and grey correlation analysis, the primary driving indicators of the spatial changes of the PLES in the Chaohu Lake Basin and the degree of the influence of various driving factors on various spatial types were determined. The study concluded with a few findings. First, from the standpoint of landscape structure, the Chaohu Lake Basin’s agricultural production space (APS) makes up more than 60% of the total area, and it and urban living space (ULS) are the two most visible spatial categories. Second, the pattern of the landscape demonstrates that the area used for agricultural production holds a significant advantage within the overall structure of the landscape. Although there is less connectedness between different landscape types, less landscape dominance, and more landscape fragmentation, the structure of different landscape types tends to be more varied. Third, the findings of the driving analysis demonstrate that the natural climate, population structure of agricultural development, and industrial structure of economic development are the three driving indicators of the change of the PLES. Finally, in order to promote the formation of a territorial space development pattern with intensive and efficient production space, appropriate living space, and beautiful ecological space, it is proposed to carry out land regulation according to natural factors, economic development, national policies, and other actual conditions.

The accumulation patterns of bioactive compounds in Ormosia henryi Prain, a traditional Chinese medicinal herb, remain poorly understood, limiting its potential for development. In this study, LC–MS/MS-based untargeted metabolomics was used to profile metabolic patterns across six tissues: new leaves (NL), old leaves (OL), stem bark (SB), stem xylem (SX), root bark (RB), and root xylem (RX). Multivariate analyses (PCA, OPLS-DA) identified key differentially accumulated metabolites (DAMs), including 34 phenolic compounds: 29 flavonoids and 5 phenolic acids. The total flavonoid content (TFC) was highest in the old leaves, while the total phenolic content (TPC) peaked in the root bark. Tissue extracts demonstrated strong antioxidant and hypoglycemic activities, with the old leaves showing the most significant bioactivity. Integrated correlation analysis further revealed significant relationships between TFC/TPC, core metabolites, and biological activities, highlighting potential biomarkers for functional evaluation. This study reveals the tissue-specific accumulation of bioactive phenolics in O. henryi Prain, providing valuable insights for the development of natural antioxidants, hypoglycemic drugs, and functional foods.

Background Colorectal neuroendocrine carcinomas (CRNECs) are highly aggressive tumours with poor prognosis and low incidence. To date, the genomic landscape and molecular pathway alterations have not been elucidated. Methods Tissue sections and clinical information of CRNEC ( n  = 35) and CR neuroendocrine tumours (CRNETs) ( n  = 25) were collected as an in-house cohort (2010–2020). Comprehensive genomic and expression panels (AmoyDx® Master Panel) were applied to identify the genomic and genetic alterations of CRNEC. Through the depiction of the genomic landscape and transcriptome profile, we compared the difference between CRNEC and CRNET. Reverse transcription-polymerase chain reaction and immunofluorescence staining were performed to confirm the genetic alterations. Results High tumour mutation load was observed in CRNEC compared with CRNET. CRNECs showed a “cold” immune landscape and increased endothelial cell activity compared with NETs. Importantly, PAX5 was aberrantly expressed in CRNEC and predicted a poor prognosis of CRNECs. CCL5, a factor that is considered an immunosuppressive factor in several tumour types, was strongly expressed in CRNEC patients with long-term survival and correlated with high CD8 + T cell infiltration. Conclusion Through the depiction of the genomic landscape and transcriptome profile, we demonstrated alterations in molecular pathways and potential targets for immunotherapy in CRNEC.

Action recognition has already been a heated research topic recently, which attempts to classify different human actions in videos. The current main-stream methods generally utilize ImageNet-pretrained model as features extractor, however it's not the optimal choice to pretrain a model for classifying videos on a huge still image dataset. What's more, very few works notice that 3D convolution neural network(3D CNN) is better for low-level spatial-temporal features extraction while recurrent neural network(RNN) is better for modelling high-level temporal feature sequences. Consequently, a novel model is proposed in our work to address the two problems mentioned above. First, we pretrain 3D CNN model on huge video action recognition dataset Kinetics to improve generality of the model. And then long short term memory(LSTM) is introduced to model the high-level temporal features produced by the Kinetics-pretrained 3D CNN model. Our experiments results show that the Kinetics-pretrained model can generally outperform ImageNet-pretrained model. And our proposed network finally achieve leading performance on UCF-101 dataset.

The advancement of semiconductor materials has played a crucial role in the development of electronic and optical devices. However, scaling down semiconductor devices to the nanoscale has imposed limitations on device properties due to quantum effects. Hence, the search for successor materials has become a central focus in the fields of materials science and physics. Transition‐metal nitrides (TMNs) are extraordinary materials known for their outstanding stability, biocompatibility, and ability to integrate with semiconductors. Over the past few decades, TMNs have been extensively employed in various fields. However, the synthesis of single‐crystal TMNs has long been challenging, hindering the advancement of their high‐performance electronics and plasmonics. Fortunately, progress in film deposition techniques has enabled the successful epitaxial growth of high‐quality TMN films. In comparison to reported reviews, there is a scarcity of reviews on epitaxial TMN films from the perspective of materials physics and condensed matter physics, particularly at the atomic level. Therefore, this review aims to provide a brief summary of recent progress in epitaxial growth at atomic precision, emergent physical properties (superconductivity, magnetism, ferroelectricity, and plasmon), and advanced electronic and plasmonic devices associated with epitaxial TMN films. Transition‐metal nitrides (TMNs) are exceptional materials with high stability, biocompatibility, and semiconductor integration, which have been extensively employed in various fields. However, the epitaxial growth of TMN films remains a challenge. The absence of high‐quality TMNs limits the understanding of their condensed matter physics and hinders their application. This review summarizes their recent progress in epitaxial growth at atomic precision, emergent physical properties (superconductivity, magnetism, ferroelectricity, and plasmon), and advanced electronic and plasmonic devices associated.