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Academic dishonesty is becoming a big concern for the education systems worldwide. Despite much research on the factors associated with academic dishonesty and the methods to alleviate it, it remains a common problem at the university level. In the current study, we conducted a survey to link personality traits (using the HEXACO model) and people’s general attitudes towards the rule (i.e., “rule conditionality” and “perceived obligation to obey the law/rule”) to academic dishonesty among 370 university students. Using correlational analysis and structural equation modeling, the results indicated that both personality traits and attitudes towards the rule significantly predicted academic misconduct. The findings have important implications for researchers and university educators in dealing with academic misconduct.

The urban development of China is changing from incremental expansion to stock renewal mode. The study of urban functional areas has become one of the important fundamental works in current urban renewal and high-quality urban development. In recent years, big spatiotemporal data has been well applied in the urban function field. However, the study of spatial–temporal evolution characteristics and forecasting optimization for mixed-use urban functional areas has not been examined well. Thus, in this study, we proposed a new approach that applies a revised information entropy method to analyze the degrees of mixing for urban functional areas. We applied our approach in Jinan City, Shandong Province as the study area. We used Point-of-Interest, OpenStreetMap and other datasets to identify the mixed-use urban functional areas in Jinan. Then, the CA–Markov model simulated the urban layout in 2025. The results showed that: (1) the combination of road network and kernel density method has the highest accuracy of identifying urban functional areas. (2)The mixing degree model is constructed by using the improved information entropy, which makes up for the shortcoming of identifying the mixed functional areas simply by the frequency ratio of POI data. (3) The “residence and business” functional area has the highest proportion in the central area of Jinan from 2015 to 2020, and the total area of mixed-use unban functional areas continuously increased during this period. (4) The total area of the central area in Jinan has significantly increased in 2025. The optimization of urban functions should expand mixed-use functional areas and increase the proportion of infrastructure. Also, Jinan should improve the efficiency of space development.

Exploring dominant strategies in iterated games holds theoretical and practical significance across diverse domains. Previous studies, through mathematical analysis of limited cases, have unveiled classic strategies such as tit-for-tat, generous-tit-for-tat, win-stay-lose-shift, and zero-determinant strategies. While these strategies offer valuable insights into human decision-making, they represent only a small subset of possible strategies, constrained by limited mathematical and computational tools available to explore larger strategy spaces. To bridge this gap, we propose an approach using multi-agent reinforcement learning to delve into complex decision-making processes that go beyond human intuition. Our approach has led to the discovery of a strategy that we call memory-two bilateral reciprocity strategy. Memory-two bilateral reciprocity strategy consistently outperforms a wide range of strategies in pairwise interactions while achieving high payoffs. When introduced into an evolving population with diverse strategies, memory-two bilateral reciprocity strategy demonstrates dominance and fosters higher levels of cooperation and social welfare in both homogeneous and heterogeneous structures, as well as across various game types. This high performance is verified by simulations and mathematical analysis. Our work highlights the potential of multi-agent reinforcement learning in uncovering dominant strategies in iterated and evolutionary games. The authors propose a multi-agent reinforcement learning approach to exploring complex decision-making. They uncover the memory-two bilateral reciprocity strategy that outperforms a wide range of strategies in iterated and evolutionary games.

Taking lactic acid bacteria is an important strategy to alleviate or prevent diabetes, but the candidate strains with good genetic stability and excellent functions still need to be supplemented. In this study, the hypoglycemic ability (α-amylase, α-glucosidase and dipeptidyl peptidase 4), probiotic property and antioxidant activity of lactic acid bacteria were comprehensively evaluated by a principal component analysis (PCA) and analytic hierarchy process (AHP). The results showed that Lactobacillus paracasei(L. paracasei) had a higher survival rate (82.78%) in gastric juice and good tolerance to bile salt, and can be colonized in HT-29 cells. L. paracasei had a remarkable inhibitive activity of α-amylase (82.21%), α-glucosidase (84.29%) and dipeptidyl peptidase 4 (42.51%). L. paracasei had better scavenging activity of free radicals, total antioxidant activity (FRAP) and superoxide dismutase activity. According to the scores of the PCA, L. paracasei had the best hypoglycemic ability, and Lactococcus lactis (L. lactis) had the highest probiotic property. According to AHP, L. paracasei was the best potential hypoglycemic probiotic; furthermore, L. lactis showed the highest comprehensive performance except Lactobacillus. All lactic acid bacteria in this test had good safety. L. paracasei is expected to become a new potential hypoglycemic strain.

Non-contact acoustic detection methods for blades have gained significant attention due to their advantages such as easy installation and immunity to mechanical noise interference. Numerical simulation investigations on the aerodynamic noise mechanism of blade erosion provide a theoretical basis for acoustic detection. However, constructing a three-dimensional erosion model remains a challenge due to the uncertainty in external natural environmental factors. This study investigates a leading-edge erosion calculation model for wind turbine blades subjected to rain erosion. A rain erosion distribution model based on the Weibull distribution of raindrop size is first constructed. Then, the airfoil modification scheme combined with the erosion distribution model is presented to calculate leading-edge erosion mass. Finally, for a sample National Renewable Energy Laboratory 5 MW wind turbine, a three-dimensional erosion model is investigated by analyzing erosion mass related to the parameter of the attack angle. The results indicate that the maximum erosion amount is presented at the pressure surface near the leading edge, and the decrease in erosion on the pressure surface is more rapid than the suction side from the leading edge to the trailing edge. With an increase in the attack angle, the erosion on the pressure side is more severe. Furthermore, a separation vortex appears at the leading edge of the airfoil under computational non-uniform erosion. For aerodynamic noise, a larger sound pressure level with significant fluctuation occurs at 400–1000 Hz.

Sonodynamic therapy (SDT) is a promising non-invasive therapeutic modality. Compared to photo-inspired therapy, SDT provides many opportunities and benefits, including deeper tissue penetration, high precision, less side effects, and good patient compliance. Thanks to the facile engineerable nature of nanotechnology, nanoparticles-based sonosensitizers exhibit predominant advantages, such as increased SDT efficacy, binding avidity, and targeting specificity. This review aims to summarize the possible mechanisms of SDT, which can be expected to provide the theoretical basis for SDT development in the future. We also extensively discuss nanoparticle-assisted sonosensitizers to enhance the outcome of SDT. Additionally, we focus on the potential strategy of combinational SDT with other therapeutic modalities and discuss the limitations and challenges of SDT toward clinical applications.

Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

A bstract We investigate the composite systems consisting of topological orders separated by gapped domain walls. We derive a pair of domain-wall Verlinde formulae, that elucidate the connection between the braiding of interdomain excitations labeled by pairs of anyons in different domains and quasiparticles in the gapped domain wall with their respective fusion rules. Through explicit non-Abelian examples, we showcase the calculation of such braiding and fusion, revealing that the fusion rules for interdomain excitations are generally fractional or irrational. By investigating the correspondence between composite systems and anyon condensation, we unveil the reason for designating these fusion rules as symmetry fractionalized (irrationalized) fusion rules. Our findings hold promise for applications across various fields, such as topological quantum computation, topological field theory, conformal field theory, and parton physics.

Summary Maize ARGOS8 is a negative regulator of ethylene responses. A previous study has shown that transgenic plants constitutively overexpressing ARGOS8 have reduced ethylene sensitivity and improved grain yield under drought stress conditions. To explore the targeted use of ARGOS8 native expression variation in drought‐tolerant breeding, a diverse set of over 400 maize inbreds was examined for ARGOS8 mRNA expression, but the expression levels in all lines were less than that created in the original ARGOS8 transgenic events. We then employed a CRISPR‐Cas‐enabled advanced breeding technology to generate novel variants of ARGOS8. The native maize GOS2 promoter, which confers a moderate level of constitutive expression, was inserted into the 5′‐untranslated region of the native ARGOS8 gene or was used to replace the native promoter of ARGOS8. Precise genomic DNA modification at the ARGOS8 locus was verified by PCR and sequencing. The ARGOS8 variants had elevated levels of ARGOS8 transcripts relative to the native allele and these transcripts were detectable in all the tissues tested, which was the expected results using the GOS2 promoter. A field study showed that compared to the WT, the ARGOS8 variants increased grain yield by five bushels per acre under flowering stress conditions and had no yield loss under well‐watered conditions. These results demonstrate the utility of the CRISPR‐Cas9 system in generating novel allelic variation for breeding drought‐tolerant crops.

Understanding aerobic denitrification has become an important focus of environmental microbiology. Aerobic denitrification can be performed by various genera of microorganisms and describes the use of nitrate (NO ₃ ⁻) as oxidizing agents under an aerobic atmosphere. Isolation of aerobic denitrifiers, enzymes involved in aerobic denitrifiers, phylogenetic distribution of aerobic denitrifiers, factors affecting the performance of aerobic denitrifiers, attempts of applications and possible future trends are depicted. The periplasmic nitrate reductase is vital for aerobic denitrifiers and NapA gene may be the proof of aerobic denitrification. Phylogenetic analysis revealed that aerobic denitrifiers mainly belong to α-, β- and γ-Proteobacteria. Aerobic denitrifiers tend to work efficiently at 25 ~ 37°C and pH 7 ~ 8, when dissolved oxygen concentration is 3 ~ 5 mg/L and C/N load ratio is 5 ~ 10. In addition, recent progresses and applications on aerobic denitrifiers are described, including single aerobic reactors, sequencing batch reactor and biofilm reactors. The review attempts to shed light on the fundamental understanding in aerobic denitrification.