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Abstract The frontier molecular orbitals of organic semiconductor materials play a crucial role in the performance of photoelectric devices, including organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), and organic photodetectors (OPDs). In this work, a model for predicting frontier molecular orbital of organic materials, including HOMO and LUMO levels, is established with the extreme gradient boosting algorithm and Klekota-Roth fingerprints. The correlation coefficients of HOMO or LUMO energy levels in the testing set are 0.75 and 0.84 in the transfer model from 11,626 DFT data in Harvard Energy database to 1198 experimental data in literature. The difference between the ML predicted value and the experimental value is smaller than the difference between ML prediction and DFT calculation, always less than 10%. Moreover, based on correlation and SHAP interpretability analysis, 13 key structural fragments influencing energy levels are selected to further verify the effective regulation of the frontier molecular orbital by the key structural fragments in practical applications. Considering the completely opposite regulatory functions of key structural fragments on HOMO and LUMO energy levels, four new Y6 derivatives, Y-PCP, Y-P6F, Y-PCF, and Y-P4FC, are designed to flexibly modify the HOMO and LUMO energy levels. The prediction trends of ML align closely with the computational trends from DFT. It is worth noting that the accuracy of LUMO energy level prediction by the prediction model makes up for the instability of DFT calculation on LUMO energy level. This work offers a cost-effective method to accelerate the acquisition of electronic properties of organic materials.

Background: Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis due to the absence of effective targeted therapies. Emerging evidence indicates that the gut microbiota and its metabolites play a key role in the occurrence and development of TNBC. This study aimed to explore the metabolic changes and potential mechanisms associated with TNBC. Objectives: This study aimed to explore the potential relationship between targeted metabolites and the gut microbiota in TNBC. Design: We recruited 8 participants, including 4 with TNBC and 4 with benign fibroadenomas as controls. Methods: The gut microbiota was analyzed using metagenomics on fecal samples. Liquid chromatography–mass spectrometry (LC-MS) was employed to identify differential metabolites in serum and fecal samples. The correlation between the gut microbiota and metabolites was analyzed using Spearman’s correlation analysis. Results: Analysis of altered serum metabolites in the TNBC group revealed changes, particularly in carboxylic acids and derivatives, benzene, and substituted derivatives. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis revealed significant enrichment in 18 pathways. Regarding fecal metabolites, differences between the 2 groups also included carboxylic acids and derivatives, benzene, and substituted derivatives, with 28 metabolic pathways enriched based on KEGG pathway analysis. Metagenomics analysis showed differences in the relative abundance of Anaerococcus , Fischerella , and Schizosaccharomyces at the genus level, which have been previously associated with breast cancer. Furthermore, 4 serum metabolites—L-glutamine, citrate, creatinine, and creatine—along with 9 fecal metabolites, were associated with the aforementioned microbiota. Conclusion: Our findings highlight distinct metabolite profiles in the serum and feces of patients with TNBC. The identification of gut microbiota and their associated metabolites provides new insights into the pathophysiological mechanisms underlying TNBC.

The resistance of Helicobacter pylori (H. pylori) has increased in recent years, prompting a trend in the research and development of new drugs. In our study, three derivatives (JF-1, JF-2, and JF-3) were synthesized using 6-gingerol as the main component, while JF-4, containing both 6-gingerol and 6-shogaol as the main components, was extracted from dried ginger. The minimum inhibitory concentrations (MICs), determined using the ratio dilution method, were 80 μg/mL for JF-1, 40 μg/mL for JF-2, 30 μg/mL for JF-3, 40 μg/mL for JF-4, 60 μg/mL for 6-gingerol standard (SS), and 0.03 μg/mL for amoxicillin (AMX). After treating H. pylori-infected mice, the inflammation of the gastric mucosa was suppressed. The eradication rate of H. pylori was 16.7% of JF-3 low-dose treatment (LDT), 25.0% of JF-3 high-dose treatment (HDT), 16.7% of JF-4 LDT, 16.7% of JF-4 HDT, 30% of SS LDT, 50% of SS HDT, and 36.4% of the positive control group (PCG). The levels of gastrin, somatostatin (SST), IFN-γ, IL-4, and IL-8 were significantly recovered in the JF-3 and JF-4 administration groups, but the effect was stronger in the high-dose group. These results demonstrate that 6-gingerol and its derivatives have significant anti-Helicobacter pylori effects and are promising potential treatments for H. pylori infection.

The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.

Abstract This paper studies data‐driven distributed fault diagnosis for large‐scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise‐induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach.

BackgroundFrom the end of 2019 to December 2023, the world grappled with the COVID-19 pandemic. The scope and ultimate repercussions of the pandemic on global health and well-being remained uncertain, ushering in a wave of fear, anxiety, and worry. This resulted in many individuals succumbing to fear and despair. Acupoint massage emerged as a safe and effective alternative therapy for anxiety relief. However, its efficacy was yet to be extensively backed by evidence-based medicine. This study aimed to enhance the clinical effectiveness of acupoint massage and extend its benefits to a wider population. It undertakes a systematic review of the existing randomized controlled trials (RCTs) assessing the impact of acupoint massage on anxiety treatment, discussing its potential benefits and implications. This research aims to furnish robust evidence supporting anxiety treatment strategies for patients afflicted with COVID-19 disease and spark new approaches to anxiety management.ObjectivesThis study evaluates the evidence derived from randomised controlled trials (RCTs), quantifies the impact of acupressure on anxiety manifestations within the general population, and proposes viable supplementary intervention strategies for managing COVID-19 related anxiety.Materials and methodsThis review included RCTs published between February 2014 and July 2023, that compared the effects of acupressure with sham control in alleviating anxiety symptomatology as the outcome measure. The studies were sourced from the multiple databases, including CINAHL, EBM Reviews, Embase, Medline, PsycINFO, Scopus and Web of Science. A meta-analysis was performed on the eligible studies, and an overall effect size was computed specifically for the anxiety outcome. The Cochrane Collaboration Bias Risk Assessment Tool (RevMan V5.4) was employed to assess bias risk, data integration, meta-analysis, and subgroup analysis. The mean difference, standard mean deviation, and binary data were used to represent continuous outcomes.ResultsOf 1,110 studies of potential relevance, 39 met the criteria for inclusion in the meta-analysis. The majority of the studies reported a positive effect of acupressure in assuaging anticipatory anxiety about treatment. Eighteen studies were evaluated using the STAI scale. The acupressure procedures were thoroughly documented, and studies exhibited a low risk of bias. The cumulative results of the 18 trials showcased a more substantial reduction in anxiety in the acupressure group compared to controls (SMD = −5.39, 95% CI −5.61 to −5.17, p

The aiming jamming emitted by self-defense jammers renders various passive anti-jamming measures based on signal processing ineffective, posing severe threats to modern radars. Frequency agility, as an active countermeasure, enables the resistance of aiming jamming. In response to issues such as the unstable anti-jamming performance of traditional random frequency hopping, limited freedom in frequency selection, and the long time required for strategic learning, the paper proposes a fast-adaptive frequency-hopping strategy for a frequency agile radar. First, a frequency agile waveform with repeatable frequency selection is designed, providing more choices for an optimal solution. Accordingly, using the data collected through continuous confrontation between a radar and a jammer, and the exploration and feedback mechanism of deep reinforcement learning, a frequency-selection strategy is continuously optimized. Specifically, considering radar frequency from the previous time and jamming frequency perceived at the current time as reinforcement learning inputs, the neural network intelligently selects each subpulse frequency at the current time and optimizes the strategy until it is optimal based on the anti-jamming effectiveness evaluated by the target detection result and Signal-to-Jamming-plus-Noise Ratio (SJNR). To improve the convergence speed of the optimal strategy, the designed input state is independent of the historical time step, the introduced greedy strategy balances the search-utilization mechanism, and the SJNR differentiates rewards more. Multiple sets of simulations show that the proposed method can converge to the optimal strategy and has high convergence efficiency.