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Fuzzy preference modeling in intelligent decision support systems aims to improve the efficiency and accuracy of decision-making processes by incorporating fuzzy logic and preference modeling techniques. While network public opinion (NPO) has the potential to drive judicial reform and progress, it also poses challenges to the independence of the judiciary due to the negative impact of malicious public opinion. To tackle this issue within the context of intelligent decision support systems, this study provides an insightful overview of current NPO monitoring technologies. Recognizing the complexities associated with handling large-scale NPO data and mitigating significant interference, a novel judicial domain NPO monitoring model is proposed, which centers around semantic feature analysis. This model takes into account time series characteristics, binary semantic fitting, and public sentiment intensity. Notably, it leverages a bidirectional long short-term memory (Bi-LSTM) network (S-Bi-LSTM) to construct a judicial domain semantic similarity calculation model. The semantic similarity values between sentences are obtained through the utilization of a fully connected layer. Empirical evaluations demonstrate the remarkable performance of the proposed model, achieving an accuracy rate of 85.9% and an F1 value of 87.1 on the test set, surpassing existing sentence semantic similarity models. Ultimately, the proposed model significantly enhances the monitoring capabilities of judicial authorities over NPO, thereby alleviating the burden on public relations faced by judicial institutions and fostering a more equitable execution of judicial power.

Two-dimensional conjugated metal organic frameworks (2D c-MOFs) hold significant promise as electrode materials for alkali metal ion batteries while their electrochemical properties still lack reasonable and effective regulation. Here, two representative 2D c-MOFs (M-HHTQ/M-HHTP, M=Cu or Ni) as positive electrodes are used as models to explore the basic/microscopic principles of their complex storage mechanism in sodium ion batteries (SIBs). It is demonstrated that the energy storage mechanism of 2D c-MOFs is determined by the interaction between coordination covalent bonds and organic linkers. Theoretical calculations and experiment results have jointly demonstrated that the redox potential and theoretical capacity can be regulated based on the valence of M-O bond and the utilization of anions and cations, respectively. As a result, Cu-HHTQ achieves a high discharge voltage at 2.55 V (vs. Na + /Na), a higher stable specific capacity of 208 mAh g −1 at 0.05 A g −1 , and long cyclability with the capacity retention rate of 100% at 1 A g −1 after 2000 cycles. Two-dimensional conjugated metal organic frameworks are potential electrode materials for alkali ion batteries. Here, the authors study two representative framework materials to elucidate the charge storage mechanisms based on metal-ligand coordination and organic linkers.

A bstract Despite the large baryon-anti-baryon asymmetry in the observable Universe, the closely related phenomenon — the violation of the combined charge and parity symmetry ( CP V) — has not been observed in the baryon sector in laboratories. In this paper, a new strategy for searching for CP V in heavy hadron multi-body decays is proposed, in which a set of novel observables measuring CP V in such decays — the partial wave CP asymmetries (PW CP As) — are introduced. This strategy is model-independent and applicable to multi-body decays of heavy hadrons with arbitrary spin configurations in both initial and final states, and with any number of particles in the final state. It is especially applicable for CP V investigations in multi-body decays of heavy baryons. As applications of this strategy, we suggest to measure the PW CP As in some decay channels of bottom baryons such as Λ b 0 → p π − π + π − , Λ b  →  pK − π + π − , Λ b 0 → p π − K + K − , Λ b 0 → Λ K + π − , and Λ b 0 → p π − K s .

The metal-support interfaces between metals and oxide supports have long been studied in catalytic applications, thanks to their significance in structural stability and efficient catalytic activity. The metal-rare earth oxide interface is particularly interesting because these early transition cations have high electrophilicity, and therefore good binding strength with Lewis basic molecules, such as H 2 O. Based on this feature, here we design a highly efficient composite Ni-Y 2 O 3 catalyst, which forms abundant active Ni-NiO x -Y 2 O 3 interfaces under the water-gas shift (WGS) reaction condition, achieving 140.6 μmol CO g cat −1 s −1 rate at 300 °C, which is the highest activity for Ni-based catalysts. A combination of theory and ex/in situ experimental study suggests that Y 2 O 3 helps H 2 O dissociation at the Ni-NiO x -Y 2 O 3 interfaces, promoting this rate limiting step in the WGS reaction. Construction of such new interfacial structure for molecules activation holds great promise in many catalytic systems. Developing effective and stable catalytic interfaces in the medium temperature region is a practical route to replace the existing water gas shift (WGS) process. Here the authors designed a composite Ni-Y 2 O 3 catalyst achieving the highest WGS activity for Ni based catalysts.

Background Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. Methods The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). Results The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p <0.05. Conclusion KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

Marine-derived fungi are a significant source of pharmacologically active metabolites with interesting structural properties, especially terpenoids with biological and chemical diversity. In the past five years, there has been a tremendous increase in the rate of new terpenoids from marine-derived fungi being discovered. In this updated review, we examine the chemical structures and bioactive properties of new terpenes from marine-derived fungi, and the biodiversity of these fungi from 2015 to 2019. A total of 140 research papers describing 471 new terpenoids of six groups (monoterpenes, sesquiterpenes, diterpenes, sesterterpenes, triterpenes, and meroterpenes) from 133 marine fungal strains belonging to 34 genera were included. Among them, sesquiterpenes, meroterpenes, and diterpenes comprise the largest proportions of terpenes, and the fungi genera of Penicillium, Aspergillus, and Trichoderma are the dominant producers of terpenoids. The majority of the marine-derived fungi are isolated from live marine matter: marine animals and aquatic plants (including mangrove plants and algae). Moreover, many terpenoids display various bioactivities, including cytotoxicity, antibacterial activity, lethal toxicity, anti-inflammatory activity, enzyme inhibitor activity, etc. In our opinion, the chemical diversity and biological activities of these novel terpenoids will provide medical and chemical researchers with a plenty variety of promising lead compounds for the development of marine drugs.

The smoothing parameter on lattices is crucial for lattice-based cryptographic design. In this study, we establish a new upper bound for the lattice smoothing parameter, which represents an improvement over several significant classical findings. For one-dimensional integer lattices, under specific and optimized conditions, we have achieved a more precise upper bound compared to previous research. Regarding general high-dimensional lattices, when the lattice dimension is large enough and the error parameter is within a particular range, we have derived a new upper bound. In the practical applications of lattice-based cryptography, where the lattice dimension is typically large, our new bound enables a more natural and smaller setting for the error parameter, thereby improving the upper bounds on all known smoothing parameters.

Objective This study aims to describe the epidemiological features of craniofacial soft tissue injuries in a major plastic emergency department in northwest China. Methods A retrospective review of emergency medical records (2019-2023) was conducted for craniofacial soft tissue injury patients. Demographic and clinical data were collected and analyzed, stratified by age and with consideration of the COVID-19 period. Results A total of 22,887 patients with 24,050 craniofacial soft tissue injuries were included. The mean age was 13.46±15.52 years, with a male to female ratio of 1.57. Collisions were the primary cause of injury (86.5%), and contusion and laceration were the most common types of wounds (97.4%). The most frequent injury locations were the forehead (24.4%), chin (13.8%), cheeks (13.0%), and supercilium (12.3%). Peak visiting times were in April, May, June, September, and October during the year, on weekends during the week, and in the afternoon and evening during the day. The average time interval between injury and hospital visit was 6.17±5.68 hours, with a median time of 4 hours. Epidemiological characteristics were also described for different age subgroups (underage [0-17 years], working-age [18-65 years], elderly [≥65 years]) and within each underage subgroup (infant-toddler [0-2 years], preschool [3-5 years], primary school [6-11 years], secondary school [12-17 years]). The COVID-19 pandemic led to a decrease in the frequency of facial injuries and a change in hospital visiting pattern, but had no apparent influence on other epidemiological characteristics. Conclusions This study provides a detailed epidemiological description of craniofacial soft tissue injuries in a large single-center retrospective cohort. The findings can contribute to optimizing treatment strategies, resource allocation, and the development of public health policies.

Photonic crystals are utilized in many noteworthy applications like optical communications, light flow control, and quantum optics. Photonic crystal with nanoscale structure is important for the manipulation of light propagation in visible and near-infrared range. Herein, we propose a novel multi beam lithography method to fabricate photonic crystal with nanoscale structure without cracking. Using multi-beam ultrafast laser processing and etching, parallel channels with subwavelength gap are obtained in yttrium aluminum garnet crystal. Combining optical simulation based on Debye diffraction, we experimentally show the gap width of parallel channels can be controlled at nanoscale by changing phase holograms. With the superimposed phase hologram designing, functional structures of complicated channel arrays distribution can be created in crystal. Optical gratings of different periods are fabricated, which can diffract incident light in particular ways. This approach can efficiently manufacture nanostructures with controllable gap, and offer an alternative to the fabrication of complex photonic crystal for integrated photonics applications.

Systemic inflammation markers have been highlighted recently as related to cardiac and non-cardiac disorders. However, few studies have estimated pre-diagnostic associations between these markers and hypertension. In the National Health and Nutritional Examination Survey from 1999 to 2010, 22,290 adult participants were included for analysis. We assessed associations between four systemic inflammation markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and hypertension prevalence in multivariate logistic regression analysis with odds ratio (OR) and 95% confidence interval (CI). To further explore their associations, subgroup and sensitivity analyses were performed. In continuous analyses, the ORs for hypertension prevalence per ln-transformed increment in SII and NLR were estimated at 1.115 and 1.087 (95% CI: 1.045-1.188; 1.008-1.173; respectively). Compared to those in the lowest tertiles, the hypertension risks for subjects in the highest SII and NLR tertiles were 1.20 and 1.11 times, respectively. Conversely, we found that PLR and LMR were negatively associated with hypertension prevalence in continuous analyses (1.060, 0.972-1.157; 0.926, 0.845-1.014; respectively), and the highest PLR and LMR tertiles (1.041, 0.959-1.129; 0.943, 0.866-1.028; respectively). Also, subgroup and sensitivity analyses indicated that SII had a greater correlation to hypertension. In conclusion, we find positive associations between SII and NLR and the prevalence of hypertension in this cross-sectional study. Our findings highlight that SII may be a superior systemic inflammation warning marker for hypertension.