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In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness.

Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optimize drug usage, and mitigate postoperative complications. This study focuses on enhancing the classification performance of anesthesia-induced transitions between wakefulness and deep sleep into eight classes by leveraging advanced graph neural network (GNN). The research combines seven datasets into a single dataset comprising 290 samples and investigates key brain regions, to develop a robust classification framework. Initially, the dataset is augmented using the Synthetic Minority Over-sampling Technique (SMOTE) to expand the sample size to 1197. A graph-based approach is employed to get the intricate relationships between features, constructing a graph dataset with 1197 nodes and 714,610 edges, where nodes represent data samples and edges are the connections between the nodes. The connection (edge weight) is calculated using Spearman correlation coefficient matrix. An optimized GNN model is developed through an ablation study of eight hyperparameters, achieving an accuracy of 92.8%. The model’s performance is further evaluated against one-dimensional (1D) CNN, and six machine learning models, demonstrating superior classification capabilities for small and imbalanced datasets. Additionally, we evaluated the proposed model on six different anesthesia datasets, observing no decline in performance. This work advances the understanding and classification of anesthesia states, providing a valuable tool for improved anesthesia management.

The possible existence of a research-practice gap is the topic of a long-standing debate in the field of public administration. In this Viewpoint essay, the authors examine the agendas of scholars and practitioners using the topic modeling technique of computational social science. Topic modeling a content analysis of 35 topics identified in Public Administration Review and PA Times (3,796 articles) showed that just over 50 percent of topics were common to both groups, indicating shared interests. There were, however, topics that the two groups distinctly focused on. Moreover, scholars and practitioners attached significant differences to the weights allocated to the prominent topics in their writing. Taken together, these findings indicate that topic modeling can shed new light on the research-practice gap in public administration.

Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulnerability to infections and cancers. HIV spreads primarily via sharing needles, from mother to child during childbirth or breastfeeding, or unprotected sexual intercourse. Therefore, early diagnosis and treatment are crucial to prevent the disease progression of HIV to AIDS, which is associated with higher mortality. This study introduces a machine learning-based framework for the classification of HIV infections crucial for preventing the disease’s progression and transmission risk to improve long-term health outcomes. Firstly, the challenges posed by an imbalanced dataset is addressed, using the Synthetic Minority Over-sampling Technique (SMOTE) oversampling technique, which was chosen over two alternative methods based on its superior performance. Additionally, we enhance dataset quality by removing outliers using the interquartile range (IQR) method. A comprehensive two-step feature selection process is employed, resulting in a reduction from 22 original features to 12 critical variables. We evaluate five machine learning models, identifying the Random Forest Classifier (RFC) and Decision Tree Classifier (DTC) as the most effective, as they demonstrate higher classification performance compared to the other models. By integrating these models into a voting classifier, we achieve an overall accuracy of 89%, a precision of 90.84%, a recall of 87.63%, and a F1-score of 98.21%. The model undergoes validation on multiple external datasets with varying instance counts, reinforcing its robustness. Furthermore, an analysis focusing solely on CD4 and CD8 cell counts which are essential lab test data for HIV monitoring, demonstrates an accuracy of 87%, emphasizing the significance of these clinical features for the classification task. Moreover, these outcomes underscore the potential of combining machine learning techniques with critical clinical data to enhance the accuracy of HIV infection classification, ultimately contributing to improved patient management and treatment strategies. These findings also highlight the scalability of the approach, showing that it can be efficiently adapted for large-scale use across various healthcare environments, including those with limited resources, making it suitable for widespread deployment in both high- and low-resource settings.

Aberrant aggregation of the RNA-binding protein TDP-43 in neurons is a hallmark of frontotemporal lobar degeneration caused by haploinsufficiency in the gene encoding progranulin 1 , 2 . However, the mechanism leading to TDP-43 proteinopathy remains unclear. Here we use single-nucleus RNA sequencing to show that progranulin deficiency promotes microglial transition from a homeostatic to a disease-specific state that causes endolysosomal dysfunction and neurodegeneration in mice. These defects persist even when Grn −/− microglia are cultured ex vivo. In addition, single-nucleus RNA sequencing reveals selective loss of excitatory neurons at disease end-stage, which is characterized by prominent nuclear and cytoplasmic TDP-43 granules and nuclear pore defects. Remarkably, conditioned media from Grn −/− microglia are sufficient to promote TDP-43 granule formation, nuclear pore defects and cell death in excitatory neurons via the complement activation pathway. Consistent with these results, deletion of the genes encoding C1qa and C3 mitigates microglial toxicity and rescues TDP-43 proteinopathy and neurodegeneration. These results uncover previously unappreciated contributions of chronic microglial toxicity to TDP-43 proteinopathy during neurodegeneration. In the absence of progranulin, microglia enter a disease-specific state that causes endolysosomal dysfunction and neurodegeneration, and these microglia promote TDP-43 granule formation, nuclear pore defects and cell death specifically in excitatory neurons via the complement activation pathway.

Blood vessels are continually exposed to circulating lipids, and elevation of ApoB-containing lipoproteins causes atherosclerosis. Lipoprotein metabolism is highly regulated by lipolysis, largely at the level of the capillary endothelium lining metabolically active tissues. How large blood vessels, the site of atherosclerotic vascular disease, regulate the flux of fatty acids (FAs) into triglyceride-rich (TG-rich) lipid droplets (LDs) is not known. In this study, we showed that deletion of the enzyme adipose TG lipase (ATGL) in the endothelium led to neutral lipid accumulation in vessels and impaired endothelial-dependent vascular tone and nitric oxide synthesis to promote endothelial dysfunction. Mechanistically, the loss of ATGL led to endoplasmic reticulum stress-induced inflammation in the endothelium. Consistent with this mechanism, deletion of endothelial ATGL markedly increased lesion size in a model of atherosclerosis. Together, these data demonstrate that the dynamics of FA flux through LD affects endothelial cell homeostasis and consequently large vessel function during normal physiology and in a chronic disease state.

The characteristics of railway subgrade mud pumping is that the subgrade soil forms mud and migrates upward along the pores of the track bed under the train load. The migration of fine particles will not only cause ballast fouling and reduce the elasticity of the ballast, but also lead to the reduction of the subgrade strength and the uneven settlement of the track, which seriously threatens driving safety. In this paper, the self-developed fine particle migration test device was used to conduct tests on saturated silty clay, and the effects of different cyclic loading amplitudes and initial dry densities of subgrade soil on the characteristics of fine particle migration were analyzed. Based on Computed Tomography (CT) scanning technology, the migration characteristics of fine particles in saturated silty clay under cyclic loading were studied from a mesoscopic perspective. The results show that with the increase in loading time, the mass of migrated fine particles increases nonlinearly, and the increase rate gradually reduces. The maximum mass of migrated fine particles increases with the increase of the cyclic loading amplitude, and decreases with the increase of the initial dry density of the subgrade soil. Finally, based on the test results, an evolution equation of fine particle migration mass under cyclic loading is established, and the accuracy and applicability of the evolution equation were verified by several groups of fine particle migration test data.

Plasmons in two-dimensional (2D) materials beyond graphene have recently gained much attention. However, the experimental investigation is limited due to the lack of suitable materials. Here, we experimentally demonstrate localized plasmons in a correlated 2D charge-density-wave (CDW) material: 2H-TaSe 2 . The plasmon resonance can cover a broad spectral range from the terahertz (40 μm) to the telecom (1.55 μm) region, which is further tunable by changing thickness and dielectric environments. The plasmon dispersion flattens at large wave vectors, resulted from the universal screening effect of interband transitions. More interestingly, anomalous temperature dependence of plasmon resonances associated with CDW excitations is observed. In the CDW phase, the plasmon peak close to the CDW excitation frequency becomes wider and asymmetric, mimicking two coupled oscillators. Our study not only reveals the universal role of the intrinsic screening on 2D plasmons, but also opens an avenue for tunable plasmons in 2D correlated materials. Here, arrays of 2H-TaSe2 nanoribbons are shown to exhibit a broadband plasmonic response that can be tuned from THz to telecom range. These localized plasmons can also couple to charge density wave excitations, offering insights on the physics of plasmonic excitations in 2D correlated materials.

In-plane anisotropic van der Waals materials have emerged as a natural platform for anisotropic polaritons. Extreme anisotropic polaritons with in-situ broadband tunability are of great significance for on-chip photonics, yet their application remains challenging. In this work, we experimentally characterize through Fourier transform infrared spectroscopy measurements a van der Waals plasmonic material, 2M-WS 2 , capable of supporting intrinsic room-temperature in-plane anisotropic plasmons in the far and mid-infrared regimes. In contrast to the recently revealed natural hyperbolic plasmons in other anisotropic materials, 2M-WS 2 supports canalized plasmons with flat isofrequency contours in the frequency range of ~ 3000-5000 cm −1 . Furthermore, the anisotropic plasmons and the corresponding isofrequency contours can be reversibly tuned via in-situ ion-intercalation. The tunable anisotropic and canalization plasmons may open up further application perspectives in the field of uniaxial plasmonics, such as serving as active components in directional sensing, radiation manipulation, and polarization-dependent optical modulators. Anisotropic light-matter excitations in van der Waals materials are expected to have an impact on nanophotonics applications. Here, the authors report the observation of canalized in-plane mid-infrared plasmons in the semimetallic phase of WS 2 and demonstrate their electrical tunability via ion intercalation.

The proteoglycan Syndecan-2 (Sdc2) has been implicated in regulation of cytoskeleton organization, integrin signaling and developmental angiogenesis in zebrafish. Here we report that mice with global and inducible endothelial-specific deletion of Sdc2 display marked angiogenic and arteriogenic defects and impaired VEGFA 165 signaling. No such abnormalities are observed in mice with deletion of the closely related Syndecan-4 (Sdc4) gene. These differences are due to a significantly higher 6-O sulfation level in Sdc2 versus Sdc4 heparan sulfate (HS) chains, leading to an increase in VEGFA 165 binding sites and formation of a ternary Sdc2-VEGFA 165 -VEGFR2 complex which enhances VEGFR2 activation. The increased Sdc2 HS chains 6-O sulfation is driven by a specific N-terminal domain sequence; the insertion of this sequence in Sdc4 N-terminal domain increases 6-O sulfation of its HS chains and promotes Sdc2-VEGFA 165 -VEGFR2 complex formation. This demonstrates the existence of core protein-determined HS sulfation patterns that regulate specific biological activities. Proteoglycans are glycosylated proteins that play a number of structural and signalling functions. Here, Corti, Wang et al. show that the N-terminal sequence of proteoglycan Syndecan-2 selectively increases 6-O sulfation of its heparan sulfate chains, and that this promotes formation of a ternary Sdc2/VEGFA/VEGFR2 complex leading to increased angiogenesis.