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Abstract Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the $${{D}}_{\text{T}}$$ D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the $${{D}}_{\text{T}}$$ D T distance formula and shape based on the triangular cloud model (DDTSTCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DDTSTCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DDTSTCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DDTSTCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China’s island microgrid industry, and the evaluation results based on DDTSTCM are in line with human cognition and have good value for engineering applications. Graphical abstract

Introduction and Objectives: Type 2 Diabetes Mellitus (T2DM), a prevalent metabolic disorder, often coexists with a range of complications, with retinopathy being particularly common. Recent studies have shed light on a potential connection between diabetic retinopathy (DR) and hepatic fibrosis, indicating a possible shared pathophysiological foundation in T2DM. This study investigates the correlation between retinopathy and hepatic fibrosis among individuals with T2DM, as well as evaluates the diagnostic value of DR for significant hepatic fibrosis. Materials and Methods: Our cross-sectional analysis incorporated 5413 participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2008. The Fibrosis-4 score (FIB-4) classified hepatic fibrosis into different grades (F0-F4), with significant hepatic fibrosis marked as F2 or higher. Retinopathy severity was determined using retinal imaging and categorized into four levels. The analysis of variance or Chi-square tests facilitated group comparisons. Additionally, the receiver operating characteristic (ROC) analysis appraised the predictive accuracy of retinopathy for significant hepatic fibrosis in the T2DM population. Results: Among 5413 participants, the mean age was 59.56 ± 12.41, with 50.2% male. And 20.6% were diagnosed with T2DM. Hepatic fibrosis grading was positively associated with retinopathy severity (OR [odds ratio]: 1.521, 95%CI [confidence interval]: 1.152-2.008, P = 0.003) across the entire population. The association was amplified in the T2DM population according to Pearson's analysis results. The ROC curve demonstrated retinopathy's diagnostic capacity for significant hepatic fibrosis in the T2DM population (AUC [area under curve] = 0.72, 95%CI: 0.651-0.793, P < 0.001). Conclusions: Retinopathy could serve as an independent predictor of significant hepatic fibrosis in T2DM population. Ophthalmologists are advised to closely monitor T2DM patients with retinopathy.

As an important supplement to traditional airborne synthetic aperture radar (SAR), multirotor unmanned aerial vehicle (UAV) SAR has the advantages of low cost, high flexibility, and strong survival ability. However, due to the complex motion and flight characteristics of the multirotor UAV platform, multirotor UAV SAR faces challenges, including spatially variant low-frequency (LF) errors and severe high-frequency (HF) errors. To deal with these problems, an improved motion compensation approach is proposed for multirotor UAV SAR imaging, which is implemented through two processing steps. 1) The LF errors are eliminated by an improved two-step MoCo approach, which takes into account the spatial variations of both envelope and phase. 2) The HF errors are estimated and corrected by an extended phase gradient autofocus scheme. Different from conventional solutions, our approach can effectively remove the complex motion errors of multirotor UAV SAR step-by-step with high robustness even in high-resolution scenarios. Computer simulation and experimental results verify the effectiveness of our approach.

Objective: To establish a Bama minipigs model with Non-Alcoholic Fatty Liver (NAFL) induced by a high-fat diet and investigate the application of attenuation coefficient (ATT) and ultrasound-derived fat fraction (UDFF) in the diagnosis of NAFL. Methods: Six-month-old male Bama minipigs were randomly divided into normal control and high-fat groups (n = 3 pigs per group), and fed with a control diet and high-fat diet for 32 weeks. Weight and body length were measured every four weeks, followed by quantitative ultrasound imaging (ATT and UDFF), blood biochemical markers, and liver biopsies on the same day. Using the Non-Alcoholic Fatty Liver Disease (NAFLD) Activity Score (NAS) as a reference, we analyzed the correlation between ATT, UDFF, and their score results. Results: Compared with the normal control group, the body weight, body mass index (BMI), and serum levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in the High-fat group were significantly different at Week 12 (P

The difference (DIF) between land surface temperature (Ts) and near surface air temperature (Ta) is the key indicator of the energy budget of the land surface, which has a more complex process than the individual Ts or Ta. However, the spatiotemporal variations and influencing factors of DIF remain incomplete. The contribution of vegetation and soil moisture (SM) as key driving factors to DIF is not yet clear. Here, we analyzed the spatiotemporal variation patterns of DIF in China from 2011 to 2023 using in situ Ts and Ta data. A convergent cross-mapping method was employed to explore the causal relationship between SM, NDVI and DIF, and subsequently calculated the contribution of NDVI and SM variations to DIF under different climatic backgrounds. The results indicate that during the study period, DIF values were all above 0 °C and showed a significant increasing trend with a national mean slope of 0.02 °C/a. In general, vegetation and SM have a driving effect on DIF, with vegetation contributing more to DIF (0.11) than SM (0.08) under different surface properties. The background values of SM and temperature have a significant effect on the spatial and temporal distribution of DIF, as well as the correlation of vegetation and soil moisture to DIF. The study outcomes contribute to a better understanding of the coupling relationship between the land surface and atmosphere, which are also crucial for addressing climate change and ecological environmental management.

It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.

Abstract Unidirectional magnetoresistance (UMR) has been observed in a variety of stacks with ferromagnetic/spin Hall material bilayer structures. In this work, UMR in antiferromagnetic insulator Fe2O3/Pt structure is reported. The UMR has a negative value, which is related to interfacial Rashba coupling and band splitting. Thickness‐dependent measurement reveals a potential competition between UMR and the unidirectional spin Hall magnetoresistance (USMR). This work reveals the existence of UMR in antiferromagnetic insulators/heavy metal bilayers and broadens the way for the application of antiferromagnet‐based spintronic devices.

In the application of long-distance transmission pipeline, the explicit mathematical model method may not completely obtain all accurate values since the parameters along the line cannot be measured point by point. A practical method based on CNN-LSTM neural network was proposed. This method uses CNN network to find spatial features and uses LSTM to explore temporal features. The real data collected from operating pipeline are used to train and verify the deep learning model, and then use the model to predict the flow rate, with an error range from 0.3% to 0.7% of main line's flow rate. By continuously comparing the actual with the predicted values in real time, a pipeline leak can be found. In addition, an improved location algorithm based on the curve distance between relevant pressure points was proposed. The field test on actual pipeline shows that the proposed new algorithm has reliable performance and does not generate false alarms during pressure fluctuation such as the operation of pipeline equipment.

Abstract Genome-wide association studies (GWASs) are the most widely used method to identify genetic risk loci associated with orofacial clefts (OFC). However, despite the increasing size of cohort, GWASs are still insufficient to detect all the heritability, suggesting there are more associations under the current stringent statistical threshold. In this study, we obtained an integrated epigenomic dataset based on the chromatin conformation of a human oral epithelial cell line (HIOEC) using RNA-seq, ATAC-seq, H3K27ac ChIP-seq, and DLO Hi-C. Presumably, this epigenomic dataset could reveal the missing functional variants located in the oral epithelial cell active enhancers/promoters along with their risk target genes, despite relatively less-stringent statistical association with OFC. Taken a non-syndromic cleft palate only (NSCPO) GWAS data of the Chinese Han population as an example, 3664 SNPs that cannot reach the strict significance threshold were subjected to this functional identification pipeline. In total, 254 potential risk SNPs residing in active cis-regulatory elements interacting with 1 718 promoters of oral epithelium-expressed genes were screened. Gapped k-mer machine learning based on enhancers interacting with epithelium-expressed genes along with in vivo and in vitro reporter assays were employed as functional validation. Among all the potential SNPs, we chose and confirmed that the risk alleles of rs560789 and rs174570 reduced the epithelial-specific enhancer activity by preventing the binding of transcription factors related to epithelial development. In summary, we established chromatin conformation datasets of human oral epithelial cells and provided a framework for testing and understanding how regulatory variants impart risk for clefts.

With the rapid development of the automobile industry, the comfort of the cockpit has become the standard for judging the quality of the car. People have also put forward higher requirements for cockpit comfort. In the process of driving, the cockpit environment will constantly change, and the comfort will also change. When the comprehensive comfort level of the cockpit decreases and the occupants feel uncomfortable, the cockpit comfort should be adjusted. In this article, a cockpit comfort evaluation model is established to realize the evaluation of cockpit comfort. In addition, we elaborate the theory of optimal state distance, where the numerical magnitude of the optimal state distance is used to reflect the extent to which an indicator deviates from its optimal state. Also, a cockpit optimal adjustment strategy identification model is established based on the theory, which can obtain the optimal adjustment strategy in a certain cockpit operating environment, facilitate the timely adjustment of the corresponding actuator, and realize the dynamic monitoring and adjustment of cockpit comfort. This project provides a reference direction for cockpit comfort adjustment, which is of great significance for future research and development of automotive cockpit comfort.

Aiming at the comfort evaluation of automobile intelligent cockpit, an evaluation model based on improved combination weighting-cloud model is established. By consulting relevant literature, 4 first-class indexes and 15 second-class indexes, including noise and vibration, light environment, thermal environment and human-computer interaction, are selected to establish a comfort evaluation system. Later the subjective and objective weights obtained by improved Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) are combined by Game Theory. Considering the fuzziness and randomness of the index system, the combination weights obtained by Game Theory are combined with the cloud model. The floating cloud algorithms is used to determine the first-class and second-class index clouds and the comprehensive evaluation cloud parameters. Improvements were made in two commonly used similarity calculation methods, the expectation curve method (ECM) and the maximum boundary curve method (MCM). A new similarity calculation method is defined to optimize the evaluation results and determine the final comfort evaluation grade. Lastly, a 2021 Audi intelligent car under a certain working condition was selected to verify the correctness and rationality of the model using the fuzzy evaluation method. The results show that the cockpit comfort evaluation model based on the improved combination weighting-cloud model can better reflect the comprehensive comfort of automobile cockpit.

Airborne synthetic aperture radar (SAR) is susceptible to atmospheric disturbance and other factors that cause the position offset error of the antenna phase center and motion error. In close-range detection scenarios, the large elevation angle may make it impossible to directly observe areas near the underlying plane, resulting in observation blind spots. In cases where the illumination elevation angle is extremely large, the influence of range variant envelope error and phase modulations becomes more serious, and traditional two-step motion compensation (MOCO) methods may fail to provide accurate imaging. In addition, conventional phase gradient autofocus (PGA) algorithms suffer from reduced performance in scenes with few strong scattering points. To address these practical challenges, we propose an improved phase-weighted estimation PGA algorithm that analyzes the motion error of UAV SAR under a large elevation angle, providing a solution for high-order range variant motion error. Based on this algorithm, we introduce a combined focusing method that applies a threshold value for selection and optimization. Unlike traditional MOCO methods, our proposed method can more accurately compensate for spatially variant motion error in the case of scenes with few strong scattering points, indicating its wider applicability. The effectiveness of our proposed approach is verified by simulation and real data experimental results.

The role of the tumor microenvironment (TME) in the progression of colorectal cancer (CRC) and its acquisition of resistance to treatment become the research hotspots. As an important component of TME, the tumor-associated macrophages (TAMs) regulate multiple critical oncogenic processes, namely, occurrence, proliferation, metastasis, and drug resistance in CRC. In this review, we have discussed the functional and therapeutic significance of TAMs in CRC. M1 macrophages act as the tumor suppressor while M2 macrophages promote CRC. The polarization of TAMs is mainly regulated by the pathways such as NFKB1 pathways, STAT3 pathways, WNT5A pathways, and PI3K pathways in CRC. Furthermore, the M2 polarization of TAMs is not only controllable but also reversible. Finally, we provide insights into the TAMs-targeted therapeutic strategies.

The interconversion of spin and charge is fundamental to the operation of spintronic devices. Here the authors demonstrate spin-to-charge conversion in the correlated material vanadium dioxide, and show that the efficiency changes dramatically across the metal-insulator transition.

Transcoelomic spread of cancer cells across the peritoneal cavity occurs in most initially diagnosed ovarian cancer (OC) patients and accounts for most cancer-related death. However, how OC cells interact with peritoneal stromal cells to evade the immune surveillance remains largely unexplored. Here, through an in vivo genome-wide CRISPR/Cas9 screen, we identified IL20RA, which decreased dramatically in OC patients during peritoneal metastasis, as a key factor preventing the transcoelomic metastasis of OC. Reconstitution of IL20RA in highly metastatic OC cells greatly suppresses the transcoelomic metastasis. OC cells, when disseminate into the peritoneal cavity, greatly induce peritoneum mesothelial cells to express IL-20 and IL-24, which in turn activate the IL20RA downstream signaling in OC cells to produce mature IL-18, eventually resulting in the polarization of macrophages into the M1-like subtype to clear the cancer cells. Thus, we show an IL-20/IL20RA-mediated crosstalk between OC and mesothelial cells that supports a metastasis-repressing immune microenvironment.

For unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) imaging, motion errors cannot be obtained accurately when high precision motion sensors are not equipped on the platform. This means that traditional data-based motion compensation (MOCO) cannot be directly implemented due to large measurement errors. In addition, classic autofocusing techniques, such as phase gradient autofocus (PGA) or map-drift algorithm (MDA), do not perform well with spatially variant errors, greatly affecting the imaging qualities, especially for high-resolution and large-swath cases. In this study, an improved spatially variant MOCO approach based on an MDA is developed to effectively eliminate the spatially variant errors. Based on the coarse and precise MDA chirp rate error estimation, motion errors are optimally acquired by the random sample consensus (RANSAC) iteration. Two-dimensional (2D) mapping is used to decouple the spatially variant residual errors into two linear independent dimensions so that the chirp-z transform (CZT) can be performed for echo data correction. Unlike traditional approaches, the spatially variant components can be compensated without any measured motion information, which indicates that the proposed approach can be applied to the common UAV SAR system with significant measurement errors. Simulations and real data experiments were used to evaluate the performance of the proposed method. The simulation results show that the proposed algorithm is able to effectively minimize spatially variant errors and generate much better imaging results.

Lithium-ion batteries have led the revolution in portable electronic devices and electrical vehicles due to their high gravimetric energy density. In particular, layered cathode material Li(Ni0.6Mn0.2Co0.2)O2 (NMC 622) can deliver high specific capacities of about 180 mAh/g. However, traditional cathode manufacturing involves high processing costs and environmental issues due to the use of organic binder polyvinylidenfluoride (PVDF) and highly toxic solvent N-methyl-pyrrolidone (NMP). In order to overcome these drawbacks, aqueous processing of thick-film NMC 622 cathodes was studied using carboxymethyl cellulose and fluorine acrylic hybrid latex as binders. Acetic acid was added during the mixing process to obtain slurries with pH values varying from 7.4 to 12.1. The electrode films could be produced with high homogeneity using slurries with pH values smaller than 10. Cyclic voltammetry measurements showed that the addition of acetic acid did not affect the redox reaction of active material during charging and discharging. Rate capability tests revealed that the specific capacities with higher slurry pH values were increased at C-rates above C/5. Cells with laser structured thick-film electrodes showed an increase in capacity by 40 mAh/g in comparison to cells with unstructured electrodes.

Monotonic push-out tests were carried out on 11 specimens having high-strength bolt, T-type perforated plate, or slot-type perforated plate connections to investigate the influence of different connection types on the interface performance of glass fiber reinforced polymer (GFRP)−concrete composite beams. The effects of the number of rows and spacing of high-strength bolts on the failure mode, load−slip relationship, and shear capacity were analyzed. The effects of the number and spacing of holes in the perforated plates, and the inclusion of transverse rebar were analyzed. The results show that the failure mode of the bolt specimens is brittle failure and the perforated plate is ductile failure. The single bolt connection has the lowest ultimate bearing capacity, while the single T-shaped and slotted perforated plates are 170% and 270% times greater. The rows and spacing have no difference in bolts. The perforated plate gradually decreased with an increase in rows and gradually increased with an increase in spacing. The transverse rebar can increase the ultimate bearing capacity and ductility in the plastic stage for perforated plate. Accordingly, there are good choices for interface connectors for GFRP−concrete composite beams, while bolt connectors need to be carefully chosen.

Introduction and Objectives: Type 2 Diabetes Mellitus (T2DM), a prevalent metabolic disorder, often coexists with a range of complications, with retinopathy being particularly common. Recent studies have shed light on a potential connection between diabetic retinopathy (DR) and hepatic fibrosis, indicating a possible shared pathophysiological foundation in T2DM. This study investigates the correlation between retinopathy and hepatic fibrosis among individuals with T2DM, as well as evaluates the diagnostic value of DR for significant hepatic fibrosis. Materials and Methods: Our cross-sectional analysis incorporated 5413 participants from the National Health and Nutrition Examination Survey (NHANES) 2005-2008. The Fibrosis-4 score (FIB-4) classified hepatic fibrosis into different grades (F0-F4), with significant hepatic fibrosis marked as F2 or higher. Retinopathy severity was determined using retinal imaging and categorized into four levels. The analysis of variance or Chi-square tests facilitated group comparisons. Additionally, the receiver operating characteristic (ROC) analysis appraised the predictive accuracy of retinopathy for significant hepatic fibrosis in the T2DM population. Results: Among 5413 participants, the mean age was 59.56 § 12.41, with 50.2% male. And 20.6% were diagnosed with T2DM. Hepatic fibrosis grading was positively associated with retinopathy severity (OR [odds ratio]: 1.521, 95%CI [confidence interval]: 1.152-2.008, P = 0.003) across the entire population. The association was amplified in the T2DM population according to Pearson’s analysis results. The ROC curve demonstrated retinopathy’s diagnostic capacity for significant hepatic fibrosis in the T2DM population (AUC [area under curve] = 0.72, 95%CI: 0.651-0.793, P < 0.001). Conclusions: Retinopathy could serve as an independent predictor of significant hepatic fibrosis in T2DM population. Ophthalmologists are advised to closely monitor T2DM patients with retinopathy. [ABSTRACT FROM AUTHOR]