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The fault-tolerant control strategies always need to not only diagnose open-circuit fault phase but also reconfigure transformation matrix and control structure according to specific fault phase, thus increasing complexity of control algorithm. Furthermore, the parameters of five-phase permanent magnet synchronous motor (PMSM) are susceptible to perturbations under open-circuit fault condition, thus deteriorating operational performance. To address these issues, a natural fault-tolerant model-free predictive flux control (NFT-MFPFC) was proposed. The novelty of proposed strategy lies in the development of an ultra-local model with constant regulation coefficient, and a new extended state observer (ESO) which employed to estimate the stator inductance and the disturbances caused by any-phase open-circuit fault. Based on these, the faulty motor with model-free predictive control can not only operate smoothly under any-phase open-circuit fault condition without fault diagnosis and reconfiguration of control system and transformation matrix, but also have none adjustment of ultra-local model parameter and strong robustness even in the case of parameter mismatch. In addition, the virtual voltage vectors were adopted to restrain the third harmonic currents under both healthy and open-circuit fault conditions. The experimental results were presented to verify the feasibility of proposed strategy.

Abstract Objective To investigate the effects of depression on non-suicidal self-injury (NSSI) and related psychological conditions in adolescents with unipolar disorder (UD) and bipolar disorder (BD), and to provide a basis for accurate prevention and intervention of NSSI behaviors in adolescents. Methods This cross-sectional study collected data from adolescents aged 12–18 years with depressive episodes who exhibited NSSI behaviors and attended the psychiatric outpatient clinic of Huangshi City Psychiatric Specialized Hospital from 2018 to 2023. Depressive episodes were clinically diagnosed by two psychiatrists according to the ICD-10. Results In terms of NSSI behavioral patterns and severity, adolescents with UD displayed more frequent behaviour of intentionally burning themselves with cigarettes and had more instances of self-inflicted suicidal thoughts that were not carried out compared to those with BD, and the differences between the two groups were statistically significant (P

Finite-control-set model predictive control (FCS-MPC) has advantages of multi-objective optimization and easy implementation. To reduce the computational burden and switching frequency, this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor (DTP-PMSM). The novelty of this method is the decomposition of prediction function and the switching optimization algorithm. Based on the decomposition of prediction function, the current increment vector is obtained, which is employed to select the optimal voltage vector and calculate the duty cycle. Then, the computation burden can be reduced and the current tracking performance can be maintained. Additionally, the switching optimization algorithm was proposed to optimize the voltage vector action sequence, which results in lower switching frequency. Hence, this control strategy can not only reduce the computation burden and switching frequency, but also maintain the steady-state and dynamic performance. The simulation and experimental results are presented to verify the feasibility of the proposed strategy.

Precise value of scheduling decisions forms the cornerstone of water distribution network (WDN) scheduling optimization, which aims at conserving energy and enhancing network operational efficiency. This article proposes a computational methodology for evaluating the value chain of scheduling decisions in WDN. The scheduling process is modeled as a Markov decision process with immediate reward function, action and state space. Due to the periodicity of water supply and sequential nature of scheduling, the calculation quantifies cumulative value of scheduling decisions by incorporating state transition probability with expected value. The effectiveness and applicability of the proposed evaluation method are demonstrated using scheduling data from a real world WDN. The method provides rational values on scheduling period and strategies, offering practical feedback for scheduling decisions.

Although Li–S batteries have a high theoretical capacity, their application are significantly hindered by the complex shuttle effect of polysulfides. To address the issues, in this paper, Ni2V2O7 prepared by a simple method is proposed as an efficient catalyst for S cathodes. Ni2V2O7 composed of Ni–O octahedra and V–O tetrahedra has a specific electronic structure. The Ni and V atoms interacted, enabling a fast kinetics of polysulfide redox. Moreover, Ni2V2O7 contains metal sites (Ni and V) and oxygen vacancies, providing a high adsorption capacity and abundant active sites for polysulfide redox. Therefore, Li–S batteries consisting of Ni2V2O7-based cathodes exhibit an excellent cycling stability with a capacity of 1061 mAh g−1 and capacity retention of 87.6% after 150 cycles at 0.2 C. Moreover, at 1 C rate, the batteries exhibit a capacity of 706 mAh g−1 after 300 cycles with a minimal capacity decay of only 0.08% per cycle.

Abstract The levels of fasting-state serum bile acids (BAs) in individuals with polycystic ovary syndrome (PCOS) differ from those of control subjects. However, there is a lack of research on the BAs profile in lean women with PCOS and whether these changes are linked to the host metabolism. Therefore, our objective was to investigate the synthesis and metabolism of serum BAs in lean women with PCOS and assess the correlation between BAs and clinical characteristics. This study employed a cross-sectional design of lean women with PCOS (n = 240) in comparison to a control group (n = 80) consisting of healthy lean women. The findings revealed significant increases in the levels of non-12-OH BAs and chenodeoxycholic acid (CDCA)% (both P

PurposeInflammation is a frequent precursor to the development of chronic pain. Ponicidin, a compound derived from traditional Chinese medicine, possesses immunomodulatory and anti-inflammatory properties. However, whether ponicidin mitigates inflammatory pain through its anti-inflammatory effects and potential target molecules remains to be further explored. In this study, we investigated the analgesic effects of ponicidin in a mouse model of Complete Freund's Adjuvant (CFA)—induced inflammatory pain and employed network pharmacology to predict the potential therapeutic targets of ponicidin for pain treatment.MethodsInitially, we established a mouse model of inflammatory pain induced by Complete Freund's Adjuvant (CFA). Following the establishment of the model, the analgesic effects of ponicidin were assessed using behavioral tests, and further validation was conducted through hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), and immunofluorescence methods. Subsequently, we analyzed the potential analgesic targets of ponicidin using network pharmacology approaches and molecular docking.ResultsIn this study, we observed that ponicidin has a significant alleviating effect on CFA-induced inflammatory pain. Our results suggest that ponicidin may alleviate inflammatory pain by reducing inflammatory responses in the spinal cord and hind paw of CFA model mice. Furthermore, we found that ponicidin can mitigate the activation of macrophages in the subcutaneous tissue of the hind paw and microglia in the dorsal horn of the spinal cord. Network pharmacology analysis suggests that ponicidin may exert its analgesic effects through a multi-target, multi-pathway mechanism. Key transcription factors such as nuclear factor NF-κB p105 subunit (NFKB1), RELA, SP1, signal transducer and activator of transcription 3 (STAT3), and repressor element 1 silencing transcription factor (REST) may be involved in the underlying mechanisms of ponicidin's analgesic action. Through molecular docking and experimental validation, we have identified toll-like receptor 4 (TLR4) and hypoxia-inducible factor 1-alpha (HIF1A) as key targets of ponicidin's analgesic effects.ConclusionsPonicidin alleviates inflammatory pain by reducing inflammatory responses in the spinal cord and hind paw of the CFA model mice. TLR4 and HIF1A may as key targets for the analgesic effects of ponicidin.

BackgroundThis study aimed to explore the possible value of follicular helper T (Tfh) cells in hospital-acquired pneumonia (HAP).MethodsFlow cytometry was used to measure circulating Tfh and helper T cell (Th) cells in 62 HAP patients and 16 healthy individuals. HAP patients were further categorized into uncontrolled and controlled groups, in accordance with relevant guidelines. Subgroup analyses were additionally conducted based on the pathogen and the presence of bloodstream infections (BSIs) and the incidence of septic shock. Kaplan-Meier survival analysis and ROC analysis were performed to estimate the prognostic value of the combination of Tfh/Th ratios and PCT levels.ResultsThe Tfh/Th ratio was notably higher in uncontrolled HAP patients than in controls (P

IntroductionThe present study investigates how autonomy-supportive coaching style influences youth athlete development through psychological resilience and dispositional optimism. Despite growing interest in factors that contribute to athlete development, gaps remain in understanding how coaching approaches interact with psychological traits to foster youth athletes’ growth. This study addresses these gaps by proposing a serial mediation model in which autonomy-supportive coaching indirectly enhances athlete development through resilience and optimism.MethodsData were collected from 325 youth athletes and their coaches across training facilities and schools in China, and analyzed using structural equation modeling in SmartPLS.ResultsResults indicate that autonomy-supportive coaching style significantly increases psychological resilience, which in turn boosts dispositional optimism, positively impacting athlete development. Both resilience and optimism serially mediate the link between coaching style and athlete growth.DiscussionThese findings emphasize the importance of autonomy-supportive coaching in creating psychologically supportive environments that foster resilience, optimism, and developmental pathways in youth sports.

Abstract Emerging evidence has shown that ferroptosis and antitumor immunity response of T lymphocytes play critical roles in multiple malignancies, including gastric cancer (GC). Here, the present research aims to reveal the function of novel N6-methyladenosine (m6A) methyltransferase METTL5 on GC immune microenvironment. Clinically, elevated METTL5 was negatively correlated to the prognosis of GC patients. METTL5 high-expression repressed the Fe2+ accumulation and ferroptosis to promote the GC immune evasion escaping from activated PBMCs’ killing effect. Mechanistically, upregulation of METTL5 promoted NRF2 mRNA stability, thereby inactivating the ferroptosis and repressing PBMCs’ cells antitumor immunity. One valuable finding is that ferroptosis inhibitor (Ferrostatin-1, Fer-1) could reduce the antitumor immunity of cocultured PBMCs. In other words, the increase of ferroptosis might contribute to the anti-tumor efficacy of immunotherapy. Further study revealed that m6A reader IGF2BP1 mediated the stability of NRF2 mRNA via METTL5/m6A/NRF2 axis. Collectively, these results demonstrate that METTL5 functions as an oncogene in GC immune microenvironment, and highlights a critical role in T lymphocytes’ antitumor immunity.

Abstract Background R2R3-MYB transcription factors belong to one of the largest gene subfamilies in plants, and they are involved in diverse biological processes. However, the role of R2R3-MYB transcription factor subfamily genes in the response of rice (Oryza sativa L.) to salt stress has been rarely reported. Results In this study, we performed a genome-wide characterization and expression identification of rice R2R3-MYB transcription factor subfamily genes. We identified a total of 117 R2R3-MYB genes in rice and characterized their gene structure, chromosomal location, and cis-regulatory elements. According to the phylogenetic relationships and amino acid sequence homologies, the R2R3-MYB genes were divided into four groups. qRT-PCR of the R2R3-MYB genes showed that the expression levels of 10 genes significantly increased after 3 days of 0.8% NaCl treatment. We selected a high expression gene OsMYB2-115 for further analysis. OsMYB2-115 was highly expressed in the roots, stem, leaf, and leaf sheath. OsMYB2-115 was found to be localized in the nucleus, and the yeast hybrid assay showed that OsMYB2-115 has transcriptional activation activity. Conclusion This result provides important information for the functional analyses of rice R2R3-MYB transcription factor subfamily genes related to the salt stress response and reveals that OsMYB2-115 may be an important gene associated with salt tolerance in rice.

Abstract With the rapid growth of social media, fake news (rumors) are rampant online, seriously endangering the health of mainstream social consciousness. Fake news detection (FEND), as a machine learning solution for automatically identifying fake news on Internet, is increasingly gaining the attentions of academic community and researchers. Recently, the mainstream FEND approaches relying on deep learning primarily involves fully supervised fine-tuning paradigms based on pre-trained language models (PLMs), relying on large annotated datasets. In many real scenarios, obtaining high-quality annotated corpora are time-consuming, expertise-required, labor-intensive, and expensive, which presents challenges in obtaining a competitive automatic rumor detection system. Therefore, developing and enhancing FEND towards data-scarce scenarios is becoming increasingly essential. In this work, inspired by the superiority of semi-/self- supervised learning, we propose a novel few-shot rumor detection framework based on semi-supervised adversarial learning and self-supervised contrastive learning, named Detection Yet See Few (DetectYSF). DetectYSF synergizes contrastive self-supervised learning and adversarial semi-supervised learning to achieve accurate and efficient FEND capabilities with limited supervised data. DetectYSF uses Transformer-based PLMs (e.g., BERT, RoBERTa) as its backbone and employs a Masked LM-based pseudo prompt learning paradigm for model tuning (prompt-tuning). Specifically, during DetectYSF training, the enhancement measures for DetectYSF are as follows: (1) We design a simple but efficient self-supervised contrastive learning strategy to optimize sentence-level semantic embedding representations obtained from PLMs; (2) We construct a Generation Adversarial Network (GAN), utilizing random noises and negative fake news samples as inputs, and employing Multi-Layer Perceptrons (MLPs) and an extra independent PLM encoder to generate abundant adversarial embeddings. Then, incorporated with the adversarial embeddings, we utilize semi-supervised adversarial learning to further optimize the output embeddings of DetectYSF during its prompt-tuning procedure. From the news veracity dissemination perspective, we found that the authenticity of the news shared by these collectives tends to remain consistent, either mostly genuine or predominantly fake, a theory we refer to as “news veracity dissemination consistency”. By employing an adjacent sub-graph feature aggregation algorithm, we infuse the authenticity characteristics from neighboring news nodes of the constructed veracity dissemination network during DetectYSF inference. It integrates the external supervisory signals from “news veracity dissemination consistency” to further refine the news authenticity detection results of PLM prompt-tuning, thereby enhancing the accuracy of fake news detection. Furthermore, extensive baseline comparisons and ablated experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of DetectYSF for few-shot fake new detection under low-resource scenarios.

Abstract Aberrant RNA editing has emerged as a pivotal factor in the pathogenesis of hepatocellular carcinoma (HCC), but the impact of RNA co-editing within HCC remains underexplored. We used a multi-step algorithm to construct an RNA co-editing network in HCC, and found that HCC-related RNA editings are predominantly centralized within the network. Furthermore, five pairs of risk RNA co-editing events were significantly correlated with the overall survival in HCC. Based on presence of risk RNA co-editings resulted in the categorization of HCC patients into high-risk and low-risk groups. Disparities in immune cell infiltrations were observed between the two groups, with the high-risk group exhibiting a greater abundance of exhausted T cells. Additionally, seven genes associated with risk RNA co-editing pairs were identified, whose expression effectively differentiates HCC tumor samples from normal ones. Our research offers an innovative perspective on the etiology and potential therapeutics for HCC.

Abstract Background Knee osteoarthritis (KOA) has become a public health issue. Several systematic reviews (SRs) and meta-analyses (MAs) indicate that traditional Chinese exercise (TCE) may be an effective treatment for reducing pain and stiffness and improving physical function in people with knee osteoarthritis (KOA). Objectives To evaluate the literature quality and evidence for the systematic reviews of TCE for KOA and provide evidence to support the clinical application of TCE for KOA. Methods Eight databases were searched from their inception to January 3, 2023, to retrieve relevant literature, including China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Scientific Journal Database (VIP), China Biology Medical literature database (CBM), PubMed, Embase, Web of Science and Cochrane Library, without restrictions on publication date or language. AMSTAR-2 and PRISMA 2020 assessed the methodological and reporting quality of included SRs/MAs. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was utilized to evaluate the quality of evidence. Results A total of 18 SRs/MAs were included. The methodological quality was “very low” based on AMSTAR-2. The overall reporting quality was deficient based on PRISMA 2020. The quality of Chinese and English literature differed, with English literature being superior in methodological and reporting quality. Among 93 pieces of evidence obtained, 46 (49.46%) were of very low quality, 34 (36.56%) were of low quality, 13 (13.98%) were of moderate quality, and none were of high quality. TCE was supported by 76 pieces of evidence (81.72%). Conclusion TCE appears beneficial and safe for managing KOA. However, due to the relatively low methodological and evidentiary quality of included SRs/MAs, clinicians should interpret these findings cautiously.

Acute kidney injury (AKI) is a prevalent and serious condition in the intensive care unit (ICU), associated with significant morbidity and mortality. Septic acute kidney injury (SAKI) contributes substantially to AKI cases in the ICU. However, current diagnostic methods have limitations, necessitating the exploration of novel biomarkers. In this study, we investigated the potential of plasma and urine CCL2 levels as diagnostic markers for AKI and SAKI in 216 ICU patients. Our findings revealed significant differences in plasma (p

Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A status factor (α) has been introduced to seamlessly link open channel and pressurized flow dynamics, facilitating rapid adjustments in wave speed. On this basis, Fourier feature extraction and quadratic neural networks have been employed to capture complex dynamic processes featuring high-frequency. Validation through three classical cases using the Storm Water Management Model (SWMM) and comparisons with finite volume Harten-Lax-van Leer (HLL) solver reveal that the proposed model circumvents the constraints of spatiotemporal resolution, yielding accurate flow field predictions.

Abstract With an increasing demand for environmentally friendly refrigeration, the solid‐state refrigeration based on the electrocaloric effect (ECE) has been drawn extensive attention. It is a challenge to maintain a large adiabatic temperature change (∆T) over a broad temperature span. Herein, the authors designed and synthesised (0.74‐x) Na0.5Bi0.5TiO3‐0.06BaTiO3‐0.2SrTiO3‐xBi(Mg0.5Zr0.5)O3 (abbreviated as NBT‐xBMZ) (x = 0, 0.02, 0.04, 0.06 and 0.08) lead‐free relaxor ferroelectrics. Their microstructures, dielectric properties, ferroelectric properties, ECEs and the structure‐property relationships were investigated. Via doping with BMZ, an enhanced relaxor feature and a wider temperature range where multi‐phases coexist were achieved. The relaxor ferroelectric characteristics were illustrated using the Vogel‐Fulcher relation. The indirectly calculated ECE results showed that the optimal ΔT of 1.11 K was obtained for the x = 0.02 sample at 90°C and 70 kV/cm over a wide Tspan of 120°C, providing a potential ECE material. The direct ECE results procured using thermocouple indicated that the maximal ∆T of 2.14 K and ∆T/∆E of 0.31 K m/MV were achieved in the same sample at 70°C and 7 MV/m and the variation trend of ECE results was consistent with the indirect results. Moreover, the multi‐phases coexistent strategy can be extended to other materials system to generate a large ΔT over a wide temperature range.

Drainage modeling that accurately captures urban storm inundation serves as the foundation for flood warning and drainage scheduling. In this paper, we proposed a novel coupling ideology that, by integrating 2D-1D and 1D-2D unidirectional processes, overcomes the drawback of the conventional unidirectional coupling approach that fails to properly represent the rainfall surface catchment dynamics, and provides more coherent hydrological implications compared to the bidirectional coupling concept. This paper first referred to a laboratory experimental case from the literature, applied and analyzed the coupling scheme proposed in this paper and the bidirectional coupling scheme that has been widely studied in recent years, compared the two coupling solutions in terms of the resulting accuracy and applicability, and discussed their respective strengths and weaknesses to validate the reliability of the proposed method. The verified proposed coupling scheme was then applied to the modeling of a real drainage system in a region of Nanjing, China, and the results proved that the coupling mechanism proposed in this study is of practical application value. HIGHLIGHTS A novel coupling approach by combining two unidirectional processes.; Comparison with bidirectional tight coupling scheme in terms of simulation accuracy and applicability.; The proposed coupling idea provides reliable outcomes with enhanced applicability.;

In recent years, motor drive systems have garnered increasing attention due to their high efficiency and superior control performance. This is especially apparent in aerospace, marine propulsion, and electric vehicles, where high performance, efficiency, and reliability are crucial. The ability of the drive system to maintain long-term fault-tolerant control (FTC) operation after a failure is essential. The likelihood of inverter failures surpasses that of other components in the drive system, highlighting its critical importance. Long-term FTC operation ensures the system retains its fundamental functions until safe repairs or replacements can be made. The focus of developing a FTC strategy has shifted from basic FTC operations to enhancing the post-fault quality to accommodate the realities of prolonged operation post-failure. This paper primarily investigates FTC strategies for inverter failures in various motor drive systems over the past decade. These strategies are categorized into three types based on post-fault operational quality: rescue, remedy, and reestablishment. The paper discusses each typical control strategy and its research focus, the strengths and weaknesses of various algorithms, and recent advancements in FTC. Finally, this review summarizes effective FTC techniques for inverter failures in motor drive systems and suggests directions for future research.

Abstract Background Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. Methods A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). Results Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards.

In the field of water supply management, multi-steps water demand forecasting plays a crucial role. While there have been many studies related to multi-steps water demand forecasting based on deep learning, little attention has been paid to the interpretability of forecasting models. Aiming to improve both the forecasting accuracy and interpretability of the model, a novel urban water demand forecasting neural network (UWDFNet) was presented in this paper. Compared with traditional deep learning models, it innovatively considered domain-specific prior knowledge from water supply management and incorporated the correlation relationship between different input variables into the design of the neural network structure, and verified the consistency between the knowledge learned by the model and prior knowledge through interpretability analysis. Additionally, a systematic performance evaluation was conducted and proved that UWDFNet possesses better accuracy and stability compared to other baseline models(e.g., gated recurrent unit network (GRUN), GRUN with a corrected Network (GRUN+CORRNet), GRUN+PID, GRUN+Kmeans).

Abstract Accumulating evidence supports the notion that microglia play versatile roles in different chronic pain conditions. However, therapeutic strategies of chronic pain by targeting microglia remain largely overlooked. This study seeks to develop a miRNA-loaded nano-delivery system by targeting microglia, which could provide a decent and long-lasting analgesia for chronic pain. Surface aminated mesoporous silica nanoparticles were adopted to load miR-26a-5p, a potent analgesic miRNA, by electrostatic adsorption, which can avoid miR-26a-5p is rapidly released and degraded. Then, targeting peptide MG1 was modified on the surface of aminated mesoporous silica particles for microglia targeting. In peripheral nerve injury induced neuropathic pain model, a satisfactory anti-allodynia effect with about 6 weeks pain-relief duration were achieved through targeting microglia strategy, which decreased microglia activation and inflammation by Wnt5a, a non-canonical Wnt pathway. In inflammatory pain and chemotherapy induced peripheral neuropathic pain, microglia targeting strategy also exhibited more efficient analgesia and longer pain-relief duration than others. Overall, we developed a microglia-targeting nano-delivery system, which facilitates precisely miR-26a-5p delivery to enhance analgesic effect and duration for several chronic pain conditions. Graphical Abstract

According to the society of assisted reproductive technologies (SART), the top fertility clinics in North America have reported overall pregnancy rates of up to 50% in good prognosis patients. Although new techniques in advanced reproductive technologies have begun to emerge in the twenty-first century, including time-lapse monitoring of embryo development, analysis of metabolites in embryo culture media, and the use of artificial intelligence in morphometric embryo grading, currently chromosomal status obtained via pre-implantation genetic testing for aneuploidy (PGT-A) appears to be the most definitive in evaluating embryonic potential. Yet still, in most clinics, approximately 30% of euploid or chromosomally normal embryos fail to achieve a viable pregnancy resulting in stress and anxiety for patients, repeat cycles, and increased cost of treatment. Recent literature suggests that blastocysts with an increased mitochondrial DNA (mtDNA) ratio have greatly reduced implantation potential, however, there exists controversial studies which do not report a relationship. This review summarizes the current literature on utilizing mitochondrial DNA as a possible biomarker for embryo implantation, highlights the reasons for inconsistencies in some studies and suggests possible improvements to the study design of future mtDNA ratio research.

Abstract Platinum-based chemotherapy remains one of the major choices for treatment of ovarian cancer (OC). However, primary or acquired drug resistance severely impairs their efficiency, thereby causing chemotherapy failure and poor prognosis. SH3 domain containing ring finger 2 (SH3RF2) has been linked to the development of cancer. Here we find higher levels of SH3RF2 in the tumor tissues from cisplatin-resistant OC patients when compared to those from cisplatin-sensitive patients. Similarly, cisplatin-resistant OC cells also express higher levels of SH3RF2 than normal OC cells. Through in vitro and in vivo loss-of-function experiments, SH3RF2 is identified as a driver of cisplatin resistance, as evidenced by increases in cisplatin-induced cell apoptosis and DNA damage and decreases in cell proliferation induced by SH3RF2 depletion. Mechanistically, SH3RF2 can directly bind to the RNA-binding protein mRNA processing factor (RBPMS). RBPMS has been reported as an inhibitor of cisplatin resistance in OC. As a E3 ligase, SH3RF2 promotes the K48-linked ubiquitination of RBPMS to increase its proteasomal degradation and activator protein 1 (AP-1) transactivation. Impairments in RBPMS function reverse the inhibitory effect of SH3RF2 depletion on cisplatin resistance. Collectively, the SH3RF2-RBPMS-AP-1 axis is an important regulator in cisplatin resistance and inhibition of SH3RF2 may be a potential target in preventing cisplatin resistance.

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

Density variables based on nodal or Gaussian points are naturally incorporated in meshless topology optimization approaches, pursuing distinct topological layouts with solid and void solutions. However, engineering applications have been hampered by the fact that the authentic structure boundary cannot be identified without manual intervention. To alleviate this issue, the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) method is developed within the context of meshless approximation. In meshless analysis, the non-overlap cell variables instead of nodal or Gaussian-based variables are adopted to characterize the existence or absence of sub-regions. This work proposes a non-penalized SEMDOT where an interpolation-based heuristic sensitivity expression is utilized. The 2D and 3D compliance minimization problems serve to validate the efficiency and applicability of the proposed non-penalized SEMDOT approach based on the framework of the meshless method. The numerical results demonstrated that the proposed approach is capable of generating final designs with continuous and smooth edges or surfaces.

Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques.

Rapeseed is one of the primary oil crops; yet, it faces significant threats from weeds. The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds. To address the issues of dense weed identification, frequent occlusion, and varying weed sizes in rapeseed fields, this paper introduces a STBNA-YOLOv5 weed detection model and proposes three enhanced algorithms: incorporating a Swin Transformer encoder block to bolster feature extraction capabilities, utilizing a BiFPN structure coupled with a NAM attention mechanism module to efficiently harness feature information, and incorporating an adaptive spatial fusion module to enhance recognition sensitivity. Additionally, the random occlusion technique and weed category image data augmentation method are employed to diversify the dataset. Experimental results demonstrate that the STBNA-YOLOv5 model outperforms detection models such as SDD, Faster-RCNN, YOLOv3, DETR, and EfficientDet in terms of Precision, F1-score, and mAP@0.5, achieving scores of 0.644, 0.825, and 0.908, respectively. For multi-target weed detection, the study presents detection results under various field conditions, including sunny, cloudy, unobstructed, and obstructed. The results indicate that the weed detection model can accurately identify both rapeseed and weed species, demonstrating high stability.

Gravity data inversion is of critical importance in geophysics, encompassing a range of applications, such as the exploration of geological resources, the identification of geological structures, and the detection of groundwater resources. This study proposes a three-dimensional (3D) machine learning approach to enhance the efficiency of the aforementioned exploration tasks by leveraging gravity data. The mapping relationship between gravity data and subsurface density structures is modeled by the broad learning network, distinguished by its high training efficiency and robust modeling capability. Notably, the proposed inversion method obviates the constraints on the number of anomalies prior to the inversion process. This is achieved by setting one anomaly with varied locations for different training samples. Numerical and field data applications demonstrate the efficiency of the proposed 3D machine learning gravity data inversion method, especially in automatically determining the number of anomalies. In particular, the proposed method produced accurate density inversion results in the field application, aiding in the identification of potential oil and gas reservoirs in the target region and offering the potential for broader application in other resource exploration. The proposed inversion method can promote the construction of density structures of subsurfaces based on gravity data.

Background: Response–response (RR) binding, involving the integration of independently planned and executed responses, presents a novel perspective on action control. While behavioral evidence on RR binding has been extensively examined, corresponding electrophysiological evidence remains scarce. This study aims to contribute novel insights into RR binding by event-related potentials (ERP) techniques to provide new evidence for RR binding. Methods: An adapted prime-probe paradigm was employed, in which the required responses could either involve repetition or change conditions from the prime to the probe phase. EEG data were collected from thirty-six participants, and ERP analysis focused on the peak amplitude and latency of the P1 and P3 components. Results: Notably shorter RTs were observed in the response–repetition conditions compared to others, consistent with previous findings on RR binding. Furthermore, the response–repetition conditions exhibited larger P3 amplitudes and shorter P1 and P3 latencies relative to other conditions. Conclusions: In summary, this study strengthens the evidence base for RR binding by bridging both behavioral and electrophysiological perspectives.

Remote sensing image dehazing (RSID) aims to remove haze from remote sensing images to enhance their quality. Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven haze, which often leads to color or structural differences between the dehazed image and the original image. In order to overcome this difficulty, we propose the multi-scale cross-attention dehazing network (MCADNet), which offers a powerful solution for RSID. MCADNet integrates multi-kernel convolution and a multi-head attention mechanism into the U-Net architecture, enabling effective multi-scale information extraction. Additionally, we replace traditional skip connections with a cross-attention-based gating module, enhancing feature extraction and fusion across different scales. This synergy enables the network to maximize the overall similarity between the restored image and the real image while also restoring the details of the complex texture areas in the image. We evaluate MCADNet on two benchmark datasets, Haze1K and RICE, demonstrating its superior performance. Ablation experiments further verify the importance of our key design choices in enhancing dehazing effectiveness.

Social mindfulness entails the consideration of the interests of others while respecting their autonomy. Although long-term group identity generates in-group favoritism in social mindfulness decisions, the effect of temporary group identity on social mindfulness remains to be validated. This study examined this effect by recruiting a convenience sample of 74 adolescents from a youth basketball club, who were randomly assigned to either an in-group or out-group decision-making condition. A basketball game scenario was used to establish temporary group identities, referencing the Minimal Group paradigm for grouping while applying the SoMi paradigm in a real-world context. The results showed that adolescents exhibited higher social mindfulness (χ2 = 22.774, df = 1, p < 0.001) and preference-adjusted social mindfulness (χ2 = 6.861, df = 1, p < 0.01) toward in-group compared to out-group members. Conversely, they displayed higher levels of preference-adjusted social hostility toward out-group members (χ2 = 11.291, df = 1, p < 0.01). These findings suggest that even temporary group identities, formed in a sports setting, can lead to intergroup bias in adolescents’ social decision-making, promoting goodwill toward the in-group while restricting the autonomy of out-group members.

Abstract Objective Risk stratification plays a critical role in patients with asymptomatic carotid atherosclerotic stenosis. Heavy macrophage infiltration (HMC) is an important factor of plaque destabilization. However, in vivo imaging technologies and screening criteria for HMC remain limited. We aimed to (i) introduce algorithms for in vivo detection of macrophage infiltrations using optical coherence tomography (OCT) and (ii) to investigate the threshold of HMC and its association with plaque vulnerability. Methods Ex vivo OCT images were co‐registered with histopathology in 282 cross‐sectional pairs from 19 carotid endarterectomy specimens. Of these, 197 randomly selected pairs were employed to define the parameters, and the remaining 85 pairs were used to evaluate the accuracy of the OCT‐based algorithm in detecting macrophage infiltrations. Clinical analysis included 93 patients receiving carotid OCT evaluation. The prevalence and burden of macrophage infiltration were analyzed. Multivariable and subgroup analysis were performed to investigate the association between HMC and plaque rupture. Results The sensitivity and specificity of algorithm for detecting macrophage infiltration were 88.0% and 74.9%, respectively. Of 93 clinical patients, ruptured plaques exhibited higher prevalence of macrophage infiltration than nonruptured plaques (83.7% [36/43] vs 32.0% [16/50], p

Abstract Background Damage in the ischemic core and penumbra after stroke affects patient prognosis. Microglia immediately respond to ischemic insult and initiate immune inflammation, playing an important role in the cellular injury after stroke. However, the microglial heterogeneity and the mechanisms involved remain unclear. Methods We first performed single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) on middle cerebral artery occlusion (MCAO) mice from three time points to determine stroke-associated microglial subclusters and their spatial distributions. Furthermore, the expression of microglial subcluster-specific marker genes and the localization of different microglial subclusters were verified on MCAO mice through RNAscope and immunofluorescence. Gene set variation analysis (GSVA) was performed to reveal functional characteristics of microglia sub-clusters. Additionally, ingenuity pathway analysis (IPA) was used to explore upstream regulators of microglial subclusters, which was confirmed by immunofluorescence, RT-qPCR, shRNA-mediated knockdown, and targeted metabolomics. Finally, the infarct size, neurological deficits, and neuronal apoptosis were evaluated in MCAO mice after manipulation of specific microglial subcluster. Results We discovered stroke-associated microglial subclusters in the brains of MCAO mice. We also identified novel marker genes of these microglial subclusters and defined these cells as ischemic core-associated (ICAM) and ischemic penumbra-associated (IPAM) microglia, according to their spatial distribution. ICAM, induced by damage-associated molecular patterns, are probably fueled by glycolysis, and exhibit increased pro-inflammatory cytokines and chemokines production. BACH1 is a key transcription factor driving ICAM generation. In contrast, glucocorticoids, which are enriched in the penumbra, likely trigger IPAM formation, which are presumably powered by the citrate cycle and oxidative phosphorylation and are characterized by moderate pro-inflammatory responses, inflammation-alleviating metabolic features, and myelinotrophic properties. Conclusions ICAM could induce excessive neuroinflammation, aggravating brain injury, whereas IPAM probably exhibit neuroprotective features, which could be essential for the homeostasis and survival of cells in the penumbra. Our findings provide a biological basis for targeting specific microglial subclusters as a potential therapeutic strategy for ischemic stroke.

Inappropriate scheduling plans can result in additional economic losses and the safety of water distribution network (WDN). Optimizing manual experience based scheduling plans can help water utilities rationally allocate water plants and pump stations, ensuring the safety, stability, and economy of the water supply system. However, there is a lack of real-time, rational, and optimized scheduling methods. To address this, we proposed a novel intelligent scheduling framework based on deep learning. In this framework, two neural network models, multi-heads convolutional gated recurrent unit network (MH-CGRU) and multi-head gated recurrent unit network (MH-GRU), can effectively extract key features from the WDNs. Operating data were used as decision variables to predict and generate scheduling orders for water plants and pump stations, respectively. The rationality of the orders is verified by combining a high precision online hydraulic model and the evaluation of the operational status of the WDNs. This system has been deployed in a real WDN and put into practical application. From June to November of 2022, the total adoption rate of all orders reached 96.29%, with the average deviation between predicted and actual control targets being less than 5%, and energy consumption decreased by 3.05% compared to the previous year. HIGHLIGHTS Proposed an optimized, real-time, and secure intelligent control method for water supply networks based on deep learning algorithms.; Presented a data evaluation approach for selecting high-quality samples from monitoring data in the water supply system.; Developed an intelligent verification mechanism that combines a high-precision hydraulic model with scheduling orders for improved control reliability.;

Abstract Background Contrast-enhanced ultrasound (CEUS) has been recently used for the assessment of cervical lymph node metastasis (LNM) to guide surgical operation in patients with papillary thyroid carcinoma (PTC). However, the specificity and sensitivity of CEUS reported from previous studies are not consistent. The objective of this study was to evaluate the diagnostic value of CEUS for the metastasis of cervical lymph nodes in PTC patients based on data from one regional central hospital. Methods The diagnostic value of CEUS in preoperative LNM of PTC patients was concluded by comparing the results of CEUS on lymph node status with postoperative pathology examination. In addition, this study conducted hierarchical analysis of PTC patients to explore whether tumor size, different lymph node regions, and Hashimoto’s thyroiditis influence the assessment of CEUS. Results This research study ultimately enrolled 965 PTC patients, including 266 males and 699 females with a mean age of 42.27 ± 11.34 years. A total of 527 patients were considered clinical-node negative, and 438 were clinical-node positive before surgery. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of CEUS in the assessment of LNM in PTC patients were 56.00%, 71.00%, 57.06%, 69.76% and 62.59%, respectively. For central and lateral lymph nodes, the accuracy of CEUS in PTC patients was 49.43% and 54.30%, respectively. In addition, it was shown that the accuracy of CEUS in PTC patients with Hashimoto’s thyroiditis (HT) slightly decreased to 58.44%, and the accuracy of CEUS in PTC patients with non-HT in turn increased to 64.17%. The accuracy of CEUS in non-papillary thyroid microcarcinoma (PTMC) and PTMC patients was 65.68% and 61.24%, respectively. The accuracy of CEUS in predicting central LNM was significantly different between PTC patients with or without HT (P

This study aimed to determine the toxic effects of vascular CCM3 gene deficiency and lead (Pb) exposure on the nervous system. Lentiviral transfection was performed to generate a stable strain of brain microvascular endothelial cells with low CCM3 expression. MTT assay assessed the survival rate of cells exposed to Pb, determining the dose and duration of Pb exposure in vitro. Proteomic analysis was performed on the differentially expressed proteins in bEnd3 and HT22 cells and flow cytometry was used to detect cell apoptosis. Finally, urine samples from pregnant and postpartum women were subjected to ICP-MS to detect Pb levels and HPLC to detect neurotransmitter metabolites. Based on the proteomic analysis of bEnd3 (CCM3-/-) cells co-cultured with HT22 cells, it was determined that HT22 cells and CCM3 genes interfered with bEnd3 cell differential proteins,22 Abbreviations: SNP, FGF2, EGF, AD including apoptosis and ferroptosis pathways. Electron microscopy observation, ICP-MS iron ion loading detection, and WB determination of protein GPX4 expression confirmed that HT22 cells undergo apoptosis, while bEnd3 cells undergo multiple pathways of iron death and apoptosis regulation. Furthermore, a linear regression model showed the interaction between maternal urine Pb levels, the rs9818496 site of the CCM3 SNP in peripheral blood DNA, and the concentration of the neurotransmitter metabolite 5-HIAA in maternal urine (F=4.198, P < 0.05). bEnd3 cells with CCM3 gene deficiency can induce HT22 cell apoptosis through iron death and apoptosis pathways under Pb exposure in a combined cell culture Pb exposure model, and CCM3 gene deficiency in endothelial cells and Pb exposure interacts with neural cell HT22. Epidemiological studies on maternal and newborn infants further confirmed the interaction between urine Pb levels in mothers and the SNP rs9818496 site of the CCM3 gene in peripheral blood DNA.

Background: Graphene materials have the capacity to influence the tumor microenvironment and intracellular signaling responsiveness. However, the process of graphene-assisted liver cancer treatment still lacks specific biomarkers for assessing its efficacy. Methods: We identified graphene therapy-related lncRNAs (GTLncRNAs) through gene analysis and correlation tests. Multivariate COX and LASSO regression analyses yielded significant lncRNAs for a risk score model. We evaluated clinicopathological factors and tumor microenvironment using ssGSEA. We scrutinized the pathways of immune function, the evasion of tumor immunity, and the potential for immunotherapy. GTLncRNAs with differential expression were subjected to GO/KEGG analysis, and prospective chemotherapy drugs were discerned utilizing the pRRophetic algorithm. The prognostic model was authenticated through the examination of the Imvigor210 cohort, and an analysis of mRNA stemness was executed. Results: The researchers constructed a prognostic model based on 22 graphene therapy-related lncRNAs. Protective lncRNAs (AC010280.2, AL365361.1, and LINC01549) and negative lncRNAs (AC026412.3, AL031985.3, ELFN1-AS1, SNHG4, and EB2-AS1) were identified. Higher risk scores correlated with shorter survival. Low-risk immune pathways included Type_II_IFN_Reponse and cytolytic_activity. Subgroups differed significantly in TMB, TIDE, MDSC, exclusion, and dysfunction. Low TMB values correlated with longer survival. The high-risk subgroup showed increased sensitivity to screened compounds, and mRNAsi was higher in cancer tissue. Conclusions: Our GTLncRNAs-based model accurately predicted survival of HCC patients and underscored the influence of graphene therapy-related genes on the tumor microenvironment. Potential treatment compounds were identified, and the mRNAsi index demonstrated prognostic value.

With the development of global social economy and the deepening of the aging population, diseases related to aging have received increasing attention. The pathogenesis of many respiratory diseases remains unclear, and lung aging is an independent risk factor for respiratory diseases. The aging mechanism of the lung may be involved in the occurrence and development of respiratory diseases. Aging-induced immune, oxidative stress, inflammation, and telomere changes can directly induce and promote the occurrence and development of lung aging. Meanwhile, the occurrence of lung aging also further aggravates the immune stress and inflammatory response of respiratory diseases; the two mutually affect each other and promote the development of respiratory diseases. Explaining the mechanism and treatment direction of these respiratory diseases from the perspective of lung aging will be a new idea and research field. This review summarizes the changes in pulmonary microenvironment, metabolic mechanisms, and the progression of respiratory diseases associated with aging.

Pure water splitting by charged semiconductors and solar photons to concurrently generate H2 and O2 molecules has gained notable attention due to worldwide clean energy generation and storage. Despite significant advancements, challenges facing severe carrier recombination and sluggish electron transport continue to hinder the intrinsic efficiency of water splitting. Here, we fabricate two-dimensional graphitic carbon nitride-hybridized sulfur thionic polymorphs (CN/S100/S010 junctions) by incorporating photoactive semiconductors within lateral nanosheets. As a result, the charge rectification effect within the coplanar graphitic carbon nitride/S100/S010 junctions is induced by well-designed driving forces: favorable band offsets and cascade polarized surface work functions. During the water splitting process, the photogenerated electrons are sequentially transferred from graphitic carbon nitride to element semiconductor sulfur {100} and subsequently oriented to sulfur {010} facets. This unique behavior of charge migration within CN/S100/S010 photocatalysts contributes to impressive rates of H2 and O2 production, reaching 740 and 363 µmol g−1·h−1, respectively, nearly 13-fold higher than that of the parent carbon nitride. Comprehensive spectroscopic and theoretical analyses confirm the formation of CN/S100/S010 hierarchies with long-lived charge carriers during hydrogen energy production. This work introduces novel avenues for automatically orienting photogenerated carriers and holds promising prospects for clean energy production.

Collective Thomson scattering (TS) is a powerful technique to accurately diagnose the parameters of laser-produced plasmas. However, when the scattering parameter α, which is inverse to the product of electron Debye length and wavenumber of plasma fluctuations, is significantly larger than 1, which of the conditions is easily satisfied in the experiment, the accuracy of electron density measurement is usually poor just with the single-wavenumber ion-acoustic wave feature of the TS spectrum. This situation can be greatly improved in the imaging TS experiment because the signal intensity is proportional to electron density. A novel algorithm is developed and validated for the data analysis of imaging TS through the combination of signal intensity and spectral profile. The results show that we can obtain the electron density with high confidence, as well as other plasma parameters like electron and ion temperatures.

The recommendation system is one of the important methods to realize the intelligent recommendation of massive Internet benefit products.In order to improve the accuracy of personalized benefits recommendation, a deep learning recommendation system based on graph computing method was proposed.Considering the heterogeneity of multi-source data, a graph representation technology based on deep learning was carried out to construct the multiple relationship graph between users and benefit products.The multiple relationship graph extracted the information of graph structure, and model the heterogeneous graphs for the multi-dimensional features of users and the multiple interaction modes between rights and interests products, which effectively aggregated various interactive information and the multiple feature.A heterogeneous graph convolutional neural network was built to learn the high-dimensional feature vectors for various nodes, and excavate users＇ latent preferences to provide a recommendation link with strong interpretability, which greatly improved the recommendation success rate and generating economic value.

The current cultivated land quality (CLQ) evaluation method fails to consider the determination of soil nutrients, resulting in the low efficiency of soil nutrient message extraction. In an effort to effectively solve the above problems, combined with the spatial variation of soil nutrients (SVSN) at the field scale, a CLQ evaluation method is proposed. The soil nutrients were determined according to the soil spatial variation analysis, and the soil color was standardized. The characteristic bands were determined by soil fertility and nutrients such as nitrogen, phosphorus, and potassium, and the soil nutrient message was preprocessed. On this basis, the soil nutrient message extraction model was constructed. According to the damage principle of subsoiling shovel, the limit value of the membership function of the CLQ exponent is determined, and the weighted sum method is used to calculate the CLQ exponent, so as to realize the SVSN and the evaluation of CLQ at the field scale. The experimental results show that the dimensional autocorrelation of soil bulk density and soil water content in different soil layers is high and that of capillary porosity, non-capillary porosity, and total porosity in different soil layers is strong.

Abstract Background The phagocytosis and homeostasis of microglia play an important role in promoting blood clearance and improving prognosis after subarachnoid hemorrhage (SAH). LC3-assocaited phagocytosis (LAP) contributes to the microglial phagocytosis and homeostasis via autophagy-related components. With RNA-seq sequencing, we found potential signal pathways and genes which were important for the LAP of microglia. Methods We used an in vitro model of oxyhemoglobin exposure as SAH model in the study. RNA-seq sequencing was performed to seek critical signal pathways and genes in regulating LAP. Bioparticles were used to access the phagocytic ability of microglia. Western blot (WB), immunoprecipitation, quantitative polymerase chain reaction (qPCR) and immunofluorescence were performed to detect the expression change of LAP-related components and investigate the potential mechanisms. Results In vitro SAH model, there were increased inflammation and decreased phagocytosis in microglia. At the same time, we found that the LAP of microglia was inhibited in all stages. RNA-seq sequencing revealed the importance of P38 MAPK signal pathway and DAPK1 in regulating microglial LAP. P38 was found to regulate the expression of DAPK1, and P38-DAPK1 axis was identified to regulate the LAP and homeostasis of microglia after SAH. Finally, we found that P38-DAPK1 axis regulated expression of BECN1, which indicated the potential mechanism of P38-DAPK1 axis regulating microglial LAP. Conclusion P38-DAPK1 axis regulated the LAP of microglia via BECN1, affecting the phagocytosis and homeostasis of microglia in vitro SAH model. Video Abstract

The dynamical modeling and power optimization of floating wind–wave platforms, especially in regard to configurations based on constrained floating multi-body systems, lack in-depth systematic investigation. In this study, a floating wind-flap platform consisting of a flap-type wave energy converter and a floating offshore wind turbine is solved in the frequency domain considering the mechanical and hydrodynamic couplings of floating multi-body geometries and a model that suits the constraints of the hinge connection, which can accurately calculate the frequency domain dynamic response of the flap-type WEC. The results are compared with bottom-fixed flap-type wave energy converters in the absence of coupling with a floating wind platform. Moreover, combined with traditional optimization methods of power take-off systems for wave energy conversion, an optimization method is developed to suit the requirements of floating wind-flap platform configurations. The results are drawn for a specific operation site in the South China Sea, whereas a sensitivity analysis of the parameters is performed. It is found that the floating wind-flap platform has better wave energy absorption performance in the low-frequency range than the bottom-fixed flap-type wave energy converter; the average power generation in the low-frequency range can increase by up to 150 kW, mainly due to constructive hydrodynamic interactions, though it significantly fluctuates from the sea waves’ frequency range to the high-frequency range. Based on spectral analysis, operational results are drawn for irregular sea states, and the expected power for both types of flap-type WECs is around 30 kW, which points to a similar wave energy absorption performance when comparing the bottom-fixed flap with the flap within the hybrid configuration.

To address the harmonic issues associated with the pulse width modulation (PWM) method, this article introduces variable delayed time (VDT) space vector pulse width modulation (SVPWM) with double central symmetry for dual three-phase permanent magnet synchronous machines (DTP-PMSMs). Firstly, the switching sequence of four traditional vectors undergoes double central symmetry, resulting in the doubling of the frequency of the phase voltage. This alteration eliminates harmonics occurring at odd multiples of carrier frequency. Additionally, the adopting of a single null vector minimizes the additional switching times introduced by the double central symmetry. Subsequently, VDT-SVPWM is employed to further suppress the harmonics at the even multiples of carrier frequency. The implementation of the proposed double central symmetry method involves directly utilizing the calculated duty cycle of the traditional four vectors. Moreover, integrating VDT-SVPWM with the double central symmetry method is straightforward. Simulation and experimental results validate the efficacy of the proposed method in suppressing harmonics and mitigating vibrations in the DTP-PMSM.

To enhance the anti-inertia disturbance ability of permanent magnet synchronous motor (PMSM) speed system, an adaptive sliding mode control with inertia identification is proposed. A novel sliding mode control (NSMC) based on a new reaching law coupled with model reference adaptive system (MRAS) inertia identification is realized the adaptive control, named MRAS+NSMC. In the NSMC construct process, an integral sliding mode surface and a variable speed reaching law are introduced to avoid speed differentiation and improve dynamics, respectively. And the new reaching law imported a successive sigmoid( s ) to replace the traditional sign( s ) to suppress chattering phenomena. For the problem that the performance deteriorated by rotational inertia variation caused by load changes, the inertia is estimated in real time according to the MRAS theory, and the identification value is updated to the NSMC controller to realize adaptive MRAS+NSMC speed control. Experimental results show that the proposed adaptive MRAS+NSMC control has a faster speed response, and the speed response time is reduced from 85 to 49 ms compared with conventional SMC control. In addition, it has strong robustness to inertia disturbances and high speed tracking accuracy. Compared with conventional SMC, the speed tracking accuracy of proposed MRAS+NSMC is increased from 12% to 4%. This makes the proposed MRAS+NSMC control has great potential practical significance for speed control of PMSM.

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.