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In recent years, the intensification of global policy uncertainty has presented significant challenges to enterprise operations. Small and medium-sized enterprises (SMEs) in China which was characterized by limited financial capacity and weak risk resilience, are particularly vulnerable to such uncertainty. This paper systematically reviews existing literature and explores how uncertainty in monetary, fiscal, and trade policies affects SMEs by raising financing costs, delaying investment decisions, and disrupting operational strategies. It finds that traditional credit risk assessment models, which often rely solely on static financial indicators, fail to considerate the dynamic impact of external policy shocks— particularly in volatile economic environments. As a result, they tend to underestimate credit risk during periods of heightened uncertainty. Given the growing exposure of SMEs to macro-level policy changes, this paper advocates for the development of a more adaptive, policy-sensitive credit risk evaluation framework. Such a model should incorporate external policy indicators to improve early warning capabilities, better capture real-time risks, and support decision-making by financial institutions, regulators, and SMEs themselves. This research contributes to a more comprehensive understanding of credit risk in uncertain environments and offers a theoretical basis for enhancing financial resilience among SMEs.

This research paper examines the positive and negative impacts of social media short-form videos on adolescents’ psychological and psychosocial well-being. In most circumstances, parents and schoolteachers would equal the usage of social media with bad influences and results to their children and students. However, with the rapid development of social media, it is easily accessible to everyone, including adolescents as one of the major user groups. They choose to use social media to meet their certain needs and gratifications. So, there should be some positive impacts that go along with the usage of social media, particularly the short-form videos. Incorporating responses from interviews, this study demonstrates that the short-form videos on social media have both positive and negative impacts. Positive impacts play an important role on the usage of social media, which includes improving recognition level, enhancing social interactive skills, relieving stress level, etc. The adolescents’ social media dependency is not as heavy as most of the adults assume. This paper puts forward several suggestions to allow adolescents to use social media, especially short-form videos, benefiting their psychological and psychosocial well-being to the most and prevent short-form video addiction.

With global warming and the depletion of fossil energy sources, renewable energy is gradually replacing non-renewable energy as the main energy in the future. As one of the fastest growing renewable energy sources, the safety and reliability of wind energy have been paid more and more attention. The size of modern wind turbines is becoming larger and larger. As the main component of wind turbines to capture energy, the blade is often damaged by various complex environments and irregular loads. Therefore, the health monitoring and damage identification of wind turbine blades have become a main research focus. At present, in addition to the overview of various detection methods of wind turbine blades, there is a lack of comprehensive classifications and overviews of the main damage types, damage-generation mechanisms, and basic principles of the damage-detection technology of wind turbine blades. In this paper, firstly, the common fault types of wind turbine blades, such as trailing edge cracking, lightning strike, leading edge corrosion pollution, icing, and delamination, as well as their generation mechanism, are comprehensively analyzed. Then, the basic principles and the latest research progress of the current main detection technologies, such as vision, ultrasonic, thermal imaging, vibration, acoustic emission, and so on, are comprehensively reviewed. The advantages and limitations of the various detection technologies for practical application are summarized. Finally, through a comparative analysis of the various damage-detection technologies, we try to find potential future research directions, and draw conclusions. This paper will provide a reference for understanding the mechanism behind the main damage types and the damage-detection methods of wind turbine blades. It has important reference value for further promoting practical research of wind turbine blade damage-detection technology and grasping this research direction.

The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique physical properties and excellent integration properties, and they perform well as sensors or actuators in the aviation industry, providing a solid material foundation for various intelligent applications in the aviation industry. As a popular smart material, piezoelectric materials have a large number of application research in structural health monitoring, energy harvest, vibration and noise control, damage control, and other fields. As a unique material with deformation ability, shape memory materials have their own outstanding performance in the field of shape control, low-shock release, vibration control, and impact absorption. At the same time, as a material to assist other structures, it also has important applications in the fields of sealing connection and structural self-healing. Giant magnetostrictive material is a representative advanced material, which has unique application advantages in guided wave monitoring, vibration control, energy harvest, and other directions. In addition, giant magnetostrictive materials themselves have high-resolution output, and there are many studies in the direction of high-precision actuators. Some smart materials are summarized and discussed in the above application directions, aiming at providing a reference for the initial development of follow-up related research.

Among the many immunosuppressive cells in the tumor microenvironment, tumor-associated-macrophages (TAMs) are well known to contribute to tumor development. TAMs can be conditioned (polarized) to transition between classical M1-like macrophages, or alternatively to M2-like macrophages. Both are regulated by signaling molecules in the microenvironment. M1-like TAMs can secrete classic inflammatory cytokines that kill tumors by promoting tumor cell necrosis and immune cell infiltration into the tumor microenvironment. In contrast, M2-like TAMs exhibit powerful tumor-promoting functions, including degradation of tumor extracellular matrix, destruction of basement membrane, promotion of angiogenesis, and recruitment of immunosuppressor cells, all of which further promote tumor progression and distal metastasis. Therefore, remodeling the tumor microenvironment by reversing the TAM phenotype will be favorable for tumor therapy, especially immunotherapy. PLGA nanoparticles encapsulating baicalin and melanoma antigen Hgp peptide fragment 25-33 were fabricated using the ultrasonic double-emulsion technique. The nanoparticles were further loaded with CpG fragments and used conjugated M2pep and α-pep peptides on their surfaces to produce novel nano-complexes. The capability to target M2-like TAMs and anti-tumor immunotherapy effects of nano-complexes were evaluated by flow cytometry and confocal microscopy . We also investigated the survival and histopathology of murine melanoma models administrated with different nanocomplexes. Improvements in the tumor microenvironment for immune attack of melanoma-bearing mice were also assessed. The nano-complexes were effectively ingested by M2-like TAMs and , and the acidic lysosomal environment triggered the disintegration of polydopamine from the nanoparticle surface, which resulted in the release of the payloads. The released CpG played an important role in transforming the M2-like TAMs into the M1-like phenotype that further secreted inflammatory cytokines. The reversal of TAM released cytokines and gradually suppressed tumor angiogenesis, permitting the remodeling of the tumor microenvironment. Moreover, the activated TAMs also presented antigen to T cells, which further stimulated the antitumor immune response that inhibited tumor metastasis. Activated T cells released cytokines, which stimulated NK cell infiltration and directly resulted in killing tumor cells. The baicalin released by M1-like TAMs also killed tumor cells. The nano-complexes facilitated baicalin, antigen, and immunostimulant delivery to M2-like TAMs, which polarized and reversed the M2-like TAM phenotype and remodeled the tumor microenvironment to allow killing of tumor cells.

Background Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately. Methods This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation. Results While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods’ performance in terms of reproducibility, validity, and ability to predict different outcomes. Conclusion Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.

Plant mitochondrial genomes undergo frequent homologous recombination (HR). Ectopic HR activity is inhibited by the HR surveillance pathway, but the underlying regulatory mechanism is unclear. Here, we show that the mitochondrial RNase H1 AtRNH1B impairs the formation of RNA:DNA hybrids (R-loops) and participates in the HR surveillance pathway in Arabidopsis thaliana . AtRNH1B suppresses ectopic HR at intermediate-sized repeats (IRs) and thus maintains mitochondrial DNA (mtDNA) replication. The RNase H1 AtRNH1C is restricted to the chloroplast; however, when cells lack AtRNH1B, transport of chloroplast AtRNH1C into the mitochondria secures HR surveillance, thus ensuring the integrity of the mitochondrial genome and allowing embryogenesis to proceed. HR surveillance is further regulated by the single-stranded DNA-binding protein ORGANELLAR SINGLE-STRANDED DNA BINDING PROTEIN1 (OSB1), which decreases the formation of R-loops. This study uncovers a facultative dual targeting mechanism between organelles and sheds light on the roles of RNase H1 in organellar genome maintenance and embryogenesis.

Magnetic Barkhausen noise (MBN) is one of the most effective methods for determining the easy axis of ferromagnetic materials and for evaluating texture and residual stress in a nondestructive manner. MBN signals from multiple angles and different magnetization sections can be used to characterize magnetic anisotropy caused by various magnetization mechanisms. This paper reviews the development and application of magnetic anisotropy detection technology, and the MBN anisotropy models that take into account domain wall motion and magnetic domain rotation are analyzed thoroughly. Subsequently, the MBN anisotropy detection devices and detection methods are discussed, and the application of magnetic anisotropy detection technology in stress measurement and texture evaluation is reviewed. From the perspective of improving detection accuracy, the influence of composite mechanisms on magnetic anisotropy is analyzed. Finally, the opportunities and challenges faced by current magnetic anisotropy detection technology are summarized. The relevant conclusions obtained in this paper can be used to guide the MBN evaluation of magnetic anisotropy in ferromagnetic materials.

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Q d , 1.0Q d , and 1.2Q d. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Q d , 1.0Q d , and 1.2Q d , respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN).

Transcription-replication conflicts (TRCs), especially Head-On TRCs (HO-TRCs) can introduce R-loops and DNA damage, however, the underlying mechanisms are still largely unclear. We previously identified a chloroplast-localized RNase H1 protein AtRNH1C that can remove R-loops and relax HO-TRCs for genome integrity. Through the mutagenesis screen, we identify a mutation in chloroplast-localized primase ATH that weakens the binding affinity of DNA template and reduces the activities of RNA primer synthesis and delivery. This slows down DNA replication, and reduces competition of transcription-replication, thus rescuing the developmental defects of atrnh1c . Strand-specific DNA damage sequencing reveals that HO-TRCs cause DNA damage at the end of the transcription unit in the lagging strand and overexpression of ATH can boost HO-TRCs and exacerbates DNA damage. Furthermore, mutation of plastid DNA polymerase Pol1A can similarly rescue the defects in atrnh1c mutants. Taken together these results illustrate a potentially conserved mechanism among organisms, of which the primase activity can promote the occurrence of transcription-replication conflicts leading to HO-TRCs and genome instability. Resolving R-loops caused by transcription-replication conflicts (TRCs) is vital to genome stability in organisms. Here, the authors show that the chloroplast-localized primase ATH intensifies template strand competition and exacerbates the Head-On TRCs induced DNA damage.