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Abstract The ‘Electric energy substitution’ project in rural areas of northern China aims to replace coal‐fired heating with electric heating. However, this could potentially lead to congestion problems on the local distribution networks (DNs) due to an increase in heating loads. Instead of expanding the lines, flexible resources such as microgrids (MGs) and heat pumps (HPs) could provide auxiliary services in a more cost‐effective manner. A bi‐level optimisation model that coordinates DNs, MGs, and HP clusters is proposed to address this issue. In the lower level, MGs and HP clusters provide auxiliary services through competitive bidding to maximise their own income over multiple periods, considering a series of technical operational constraints and comfort constraints. In the upper level, DNs clear the market based on the bidding information to minimise its operational costs while guaranteeing network constraints. The bi‐level optimisation model is formulated as a multi‐agent matrix game problem, and the Win or Learn Fast‐Policy Hill‐climbing algorithm is used to achieve fast market equilibrium in a decentralised manner. Simulation results demonstrate that the proposed method can improve the revenue of MGs by up to 2.6 times compared to the single period matrix game method, and reduce the convergence time by up to 81.3% compared to the multi‐agent Q‐learning method.

Abstract Currently, the use of digital technology for the protection and research of cultural heritage has become a trend in this field. Rockeries in Chinese classical gardens have become an important cultural heritage type because of their unique shape, ingenious skills and rich connotations. Based on relevant historical information and 3D digital technology, this paper puts forward a multi-level quantitative analysis method on the scale, shape and quantity of rockeries in Chinese classical gardens, aiming at exploring the objective description and quantitative analysis path of rockeries at different levels. This method develops four levels: overall, regional, hierarchical, and individual and their corresponding quantitative contents, which in turn enable data extraction and analysis of the rockery through the different levels. The proposed method was applied to seven different Chinese classical garden rockeries, and taking Wanfang Anhe Rockery in the Old Summer Palace as an example for objective and comprehensive quantitative analysis and verification. The results show that this method is feasible and effective for quantitative analysis of the scale, shape and quantity of rockeries, which can help understand the basic background of stone rockeries and enhance a refined understanding of stone rockery construction, thereby providing data for preventive protection and informatization management of stone rockeries. The 3D rockery model database based on the overall, regional, hierarchical and individual quantitative analysis and numbering method brings a good application prospect for preventive protection and informatization management of the rockery.

Vision transformer (ViT) has outperformed conventional neural networks (CNNs) on general image classification. Motivated by this, we explore the ViT for Oral Squamous Cell Carcinoma (OSCC) detection from Histopathological Images. Such medical image understanding requires information from multiple spatial resolutions. There, we propose a multiscale transformer to process the information from image patch tokens of variable scales to extract the fine-grained and coarse-grained features. Our transformer model design is based on two branches, a small branch (i.e., small sized patch tokens) and large branch (i.e., large sized patch tokens) where each branch is processed with a separate specialized encoder to represent local and global context information from multiscale image patch tokens, and multi-head cross-attention fusion with lateral connections for information fusion across scales. This information Our ablation shows that MedTrans continuously perform better as patch size becomes smaller and smaller. We present a comprehensive comparison of our model that shows that our model has performed better as compared to different vision transformers and state-of-the-art CNN models on the OSCC dataset. For example, MedTrans-S outperforms the recently proposed CNN-Transformer model named TransPath with Top-1 Acc +3.58% and F1-score +3.91%, and best performing CNN model, EfficientNet, with Top-1 Acc +8.30% and F1-score +7.23%.

With the development of technology, the working processes of rolling equipment have become more and more complex, and the traditional rolling model encounters difficulties in meeting current demands for accuracy. To reduce the thickness error of the rolling system, we propose a high-precision rolling force prediction method based on SSA–Bilstm–Attention, which reduces the thickness error of the rolling system by predicting the high-precision rolling force. Firstly, a mechanical model is established, and the parameters involved are analyzed to extract suitable parameters as inputs to the network to reduce the feature loss of the network inputs. Secondly, an improved sparrow search algorithm is used to search for the hyperparameters of the network to obtain better training results. Finally, the attention mechanism is introduced to increase the network’s training accuracy. A stochastic small-batch gradient descent method is used to improve the training speed of the network. In addition, this paper establishes a web-based host computer, which provides an effective data source for the experimental analysis. The experimental results show that the optimized model has a mean square error of 1.22%, which is more accurate than other models, and has good generalization ability. The experiments confirm the method’s effectiveness in improving the thickness accuracy of the rolling system and provide a new optimization scheme for the industry.

This paper presents methods for vehicle state estimation and prediction for autonomous driving. A round intersection is chosen for application of the methods and to illustrate the results as autonomous vehicles have difficulty in handling round intersections. State estimation based on the unscented Kalman filter (UKF) is presented in the paper and then applied to state estimation of vehicles in a round intersection. The microscopic traffic simulator SUMO (Simulation of Urban Mobility) is used to generate realistic traffic in the round intersection for the simulation experiments. Change point detection-based driving behavior prediction using a multipolicy approach is then introduced and evaluated for the round intersection. Finally, these methods are combined for vehicle trajectory estimation based on UKF and policy prediction and demonstrated using the round intersection.

The accurate and efficient segmentation of the spine is important in the diagnosis and treatment of spine malfunctions and fractures. However, it is still challenging because of large inter-vertebra variations in shape and cross-image localization of the spine. In previous methods, convolutional neural networks (CNNs) have been widely applied as a vision backbone to tackle this task. However, these methods are challenged in utilizing the global contextual information across the whole image for accurate spine segmentation because of the inherent locality of the convolution operation. Compared with CNNs, the Vision Transformer (ViT) has been proposed as another vision backbone with a high capacity to capture global contextual information. However, when the ViT is employed for spine segmentation, it treats all input tokens equally, including vertebrae-related tokens and non-vertebrae-related tokens. Additionally, it lacks the capability to locate regions of interest, thus lowering the accuracy of spine segmentation. To address this limitation, we propose a novel Vertebrae-aware Vision Transformer (VerFormer) for automatic spine segmentation from CT images. Our VerFormer is designed by incorporating a novel Vertebrae-aware Global (VG) block into the ViT backbone. In the VG block, the vertebrae-related global contextual information is extracted by a Vertebrae-aware Global Query (VGQ) module. Then, this information is incorporated into query tokens to highlight vertebrae-related tokens in the multi-head self-attention module. Thus, this VG block can leverage global contextual information to effectively and efficiently locate spines across the whole input, thus improving the segmentation accuracy of VerFormer. Driven by this design, the VerFormer demonstrates a solid capacity to capture more discriminative dependencies and vertebrae-related context in automatic spine segmentation. The experimental results on two spine CT segmentation tasks demonstrate the effectiveness of our VG block and the superiority of our VerFormer in spine segmentation. Compared with other popular CNN- or ViT-based segmentation models, our VerFormer shows superior segmentation accuracy and generalization.

Particle engulfment plays a vital role in the application of particulate reinforced metal matrix composites fabricated by ingot metallurgy. During solidification, particles are nevertheless pushed by an advancing front. As a model system, TiB2p/Al composites were used to investigate the particle engulfment facilitated by acoustic cavitation. The implosion of bubbles drives the particles plunging towards the solid/liquid interface, which increases the engulfment probability. The secondary dendrite arms are refined from 271.2 μm to 98.0 μm as a result of the forced movements of TiB2 particles. Owing to the particle engulfment and dendrite refinement, the composite with ultrasound vibration treatment shows a more rapid work-hardening rate and higher strength.

Background: In the 21st century, as globalization accelerates and global public health crises occur, the One Health approach, guided by the holistic thinking of human-animal-environment and emphasizing interdisciplinary collaboration to address global health issues, has been strongly advocated by the international community. An immediate requirement exists for the creation of an assessment tool to foster One Health initiatives on both global and national scales. Methods: Built upon extensive expert consultations and dialogues, this follow-up study enhances the 2022 global One Health index (GOHI) indicator system. The GOHI framework is enriched by covering three indices, e.g. external drivers index (EDI), intrinsic drivers index (IDI), and core drivers index (CDI). The comprehensive indicator system incorporates 13 key indicators, 50 indicators, and 170 sub I-indicators, utilizing a fuzzy analytic hierarchy process to ascertain the weight for each indicator. Weighted and summed, the EDI, IDI, and CDI scores contribute to the computation of the overall GOHI 2022 score. By comparing the ranking and the overall scores among the seven regions and across 160 countries/territories, we have not only derived an overall profile of the GOHI 2022 scores, but also assessed the GOHI framework. We also compared rankings of indicators and sub I-indicators to provide greater clarity on the strengths and weaknesses of each region within the One Health domains. Results: The GOHI 2022 performance reveals significant disparities between countries/territories ranged from 39.03 to 70.61. The global average score of the GOHI 2022 is 54.82. The average score for EDI, IDI, and CDI are 46.57, 58.01, and 57.25, respectively. In terms of global rankings, countries from North America, Europe and Central Asia, East Asia and Pacific present higher scores. In terms of One Health domains of CDI, the lowest scores are observed in antimicrobial resistance (median: 43.09), followed by food security (median: 53.78), governance (median: 54.77), climate change (median: 64.12) and zoonotic diseases (median: 69.23). Globally, the scores of GOHI vary spatially, with the highest score in North America while lowest in sub-Saharan Africa. In addition, evidence shows associations between the socio–demographic profile of countries/territories and their GOHI performance in certain One Health scenarios. Conclusion: The objective of GOHI is to guide impactful strategies for enhancing capacity building in One Health. With advanced technology and an annually updated database, intensifying efforts to refine GOHI's data-mining methodologies become imperative. The goal is to offer profound insights into disparities and progressions in practical One Health implementation, particularly in anticipation of future pandemics.

IntroductionNumerous smokers are cognizant of the detrimental effects associated with this habit yet exhibit a persistent reluctance to cease their tobacco consumption. Self-exempt beliefs serve as an obstacle to the cessation of this addictive behavior. This research explored the impact of self-exempt beliefs on the readiness to quit smoking based on the Protection Motivation Theory (PMT) model and the mediating roles of threat appraisal and coping appraisal.MethodsSelf-exempt beliefs, PMT constructs, and the intention to quit smoking constituted the theoretical model. The questionnaires were collected from 488 Chinese adult male smokers based on snowball sampling. Exploratory Factor Analysis (EFA) was used to examine the underlying factor structure of the pre-designed self-exempt beliefs scale. The reliability, validity, path coefficients, and explanatory power of the model were calculated using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results and discussionThe results showed that : (1) three common factors (skeptic beliefs, bulletproof beliefs, and “worth it” beliefs) with a total of 11 items were retained after EFA; (2) skeptic beliefs and “worth it” beliefs had a significantly negative effect on both threat appraisal and coping appraisal, while bulletproof beliefs did not; (3) bulletproof beliefs had a significantly positive direct impact on intention to quit, “worth it” beliefs had a significantly negative direct impact on intention, while skeptic beliefs had no significantly direct impact on intention; (4) threat appraisal and coping appraisal positively and significantly predicted cessation intention; and (5) threat appraisal and coping appraisal, as two main cognitive processes, acted as full mediations between skeptic beliefs and the intention to quit, as complementary partial mediations between “worth it” beliefs and the intention, and as non-mediation between bulletproof beliefs and the intention. Our findings suggest that efforts to undermine or “prevent” these self-exempt beliefs, particularly “worth it” and skeptic beliefs, may be an effective tactic for health communication interventions for quitting smoking.

In order to solve the problems of high energy consumption and poor thickness uniformity in traditional hot gas bulging (HGB), a novel hot gas bulging with clustering balls method (HGBB) was proposed and employed to form uniform thickness square pyramid parts using commercial 5083 aluminum alloy. The method realizes the uniform heating and compression of the sheet by adding cluster steel balls with forming temperature and pressure to 450 °C and 2 MPa, respectively, and short time to 15 min. The procedure of new process is discussed, and the conclusion are drawn that the Vickers hardness and tensile strength was increased by 11% and 18.9% after the novel process. The strength of the alloy was improved because refined grains with high-angle grain boundaries appeared near the elongated grains due to the dynamic recrystallization. With the novel process, complex shape parts with high strength and high dimensional accuracy can be obtained.

With the rapid development of large-scale distributed renewable energy in China, the load factor of distribution transformer increases year by year, this directly become a new challenge to the load capacity of the transformer. Once subjected to this condition for a long time will have an impact on the hotspot temperature and consequently, the useful life of the transformer. In this regard, load capacity assessment of the distribution transformer becomes an essential work for unit upgrading or even replacement. This article systematically summarized the evaluation methods of transformer load capacity, as well as the load capacity prediction and dynamic capacity enhancement strategies of transformer. Discussions for the future work regarding this topic are also carried out.

The rotation matching between synthetic aperture radar (SAR) and optical images is essential for their registration. In this article, we introduce a novel fast rotation matching method for SAR and optical images. In the method, the absolute directional derivative histograms (ADDHs) of SAR and optical images can be used to reflect the rotation difference between them, and then the rotation difference is removed by the cross correlation of the two images’ ADDHs. Subsequently, we generate the ADDHs by oriented Gaussian-Gamma-shaped operator. Then, the cross correlation based on fast Fourier transform is used to deal with the ADDHs of SAR and optical images in order to determine the rotation difference of the two images. The experiment results demonstrate that the proposed method can detect the rotation difference in various conditions and achieves rotation matching with high speed.

Fungal populations are commonly found in natural environments and present enormous health care challenges, due to increased resistance to antifungal agents. Paeonol exhibits antifungal activities; nevertheless, the antifungal and antibiofilm activities of paeonol against Candida albicans and Cryptococcus neoformans remain largely unexplored. Here, we aimed to evaluate the antifungal and antibiofilm activities of paeonol against C. albicans and/or C. neoformans (i.e., against mono- or dual-species). The minimum inhibitory concentrations (MICs) of paeonol for mono-species comprising C. albicans or C. neoformans were 250 μg ml−1, whereas the MIC values of paeonol for dual-species were 500 μg ml−1. Paeonol disrupted cell membrane integrity and increased the influx of gatifloxacin into cells of mono- and dual-species cells, indicating an antifungal mode of action. Moreover, paeonol at 8 times the MIC damaged mono- and dual-species cells within C. albicans and C. neoformans biofilms, as it did planktonic cells. In particular, at 4 and 8 mg ml−1, paeonol efficiently dispersed preformed 48-h biofilms formed by mono- and dual-species cells, respectively. Paeonol inhibited effectively the yeast-to-hyphal-form transition of C. albicans and impaired capsule and melanin production of C. neoformans. The addition of 10 MIC paeonol to the medium did not shorten the lifespan of C. elegans, and 2 MIC paeonol could effectively protect the growth of C. albicans and C. neoformans-infected C. elegans. Furthermore, RNA sequencing was employed to examine the transcript profiling of C. albicans and C. neoformans biofilm cells in response to 1/2 MIC paeonol. RNA sequencing data revealed that paeonol treatment impaired biofilm formation of C. albicans by presumably downregulating the expression level of initial filamentation, adhesion, and growth-related genes, as well as biofilm biosynthesis genes, whereas paeonol inhibited biofilm formation of C. neoformans by presumably upregulating the expression level of ergosterol biosynthesis-related genes. Together, the findings of this study indicate that paeonol can be explored as a candidate antifungal agent for combating serious single and mixed infections caused by C. albicans and C. neoformans.

The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first use non-subsampled shearlet transform (NSST) to fuse multi-band and polarization SAR images. The sub-band images decomposed from the NSST are fused by the coefficient of variation (CV) and phase consistency (PC) weighted fusion rules. Subsequently, we extract the band and polarization difference information from the multi-band and polarization SAR images. The fusion image is finally colorized according to the band and polarization differences. In the experiments, we used Ka and S-band multi-polarization SAR images to test the fusion performance. The experiment results prove that the proposed fused images not only preserve much valuable information but also can be interpreted easily.

Clustering ball spinning (CBS) forming is a novel approach to manufacturing a complex curved surface. In order to explore the forming limit of magnesium alloy in the CBS forming process, the modified GTN model was incorporated into the FE simulation to analyze the damage evolution. The theoretical analyses are conducted to investigate the deformation mechanism and to explore the stress state in the CBS forming process. The numerical results show that the modified GTN model can predict the result more accurately compared with the standard GTN model, and the damage parameters for GTN model are determined by the experiments. Besides, the forming limit of AZ31 magnesium alloy can be improved by CBS forming method. To explore the reason for the increased forming limit, the microstructure of curved surface was tested by electron backscattered scattering detection (EBSD). The results demonstrate that the deformation of magnesium alloy plate by the CBS method is dominated initially by the extension twinning, and non-basal slip systems are activated with the development of forming process.

Vaccine hesitancy has become a significant issue. We aimed to elucidate the factors influencing vaccine hesitation in Chinese residents and to analyze and recommend promotional strategies and measures. In total, 92 Chinese residents from 10 provinces were interviewed using semi-structured face-to-face interviews following a predetermined survey framework in this qualitative study. We found trust in vaccine safety, access to professional advice, and vaccine price and effectiveness to be the main factors influencing vaccine hesitation. Additionally, residents in areas with a higher per capita GDP tend to receive more social support, believe that vaccination is beneficial and can prevent diseases, pay more attention to whether the vaccine is safe and has undergone various clinical trials, and are more likely to seek advice from individuals with vaccination experience as opposed to their counterparts in areas with a lower per capita GDP. Notably, as per capita GDP rises, individuals become more concerned about the price of vaccines. Measures such as clarifying vaccine safety and effectiveness, reducing self-funded vaccine prices, offering free vaccination for special groups, strengthening the publicity role of medical staff, and taking advantage of network platforms are essential to reduce vaccine hesitancy among Chinese residents.

Goat milk has been frequently implicated in staphylococcal food poisoning. The potential risk of raw goat milk contaminated by Staphylococcus aureus (S. aureus) in Shaanxi province of China is still not well documented. This study investigated the prevalence, antibiotic resistance, as well as virulence-related genes of S. aureus from raw goat milk samples in Shaanxi, China. A total of 68 S. aureus isolates were cultured from 289 raw goat milk. Most of the isolates were resistant to penicillin and oxacillin, although 41.18%, 33.82%, and 29.41% of the isolates expressed resistance to piperacillin, trimethoprim-sulfamethoxazole, and ciprofloxacin, respectively. Our data demonstrated that 91.18% of the isolates produced biofilm, of which 54.41% isolates belonged to high-biofilm producers. In addition, genotypic analysis of biofilm related genes (fnbA, clfB, fnbB, cna) revealed that 91.18% of the isolates harbored at least one of the genes, in which the most prevalent genes were fnbA (66. 17%), clfB (48.53%), and fnbB (26.47%). 94.8% of the isolates contained at least one toxin-related gene, of which seb (76.47%), tsst (36.76%), and sea (23.53%) genes were the more frequently detected. Further analysis revealed a positive association between fnbA, clfB, fnbB, seb, tsst, and sea genes and certain antibiotic resistance. The results indicated that raw goat milk samples contaminated by S. aureus can be a potential risk to public health.

The carbon emissions trading market is an essential tool for addressing climate change. The carbon emissions trading market has a relatively short history, and the research and management of risks in this market require further development. This paper takes as its research object 1272 pieces of English literature studies published by international scholars and featured on the Web of Science between 2002 and 2024. It uses CiteSpace software to categorize changes in the trends related to carbon market risk research based on time, space, and keyword clustering mapping. The results reveal the following: (1) In terms of the timeline, the risk evolution of the international carbon market is divided into an embryonic period (2002–2007), a developmental period (2008–2018), and a prosperous period (2019–2024); (2) from the perspective of spatial distribution, carbon market risk research institutions are multipolar, with China, the United States, and the United Kingdom, among other countries, issuing more studies on the topic; these studies mainly emerge from universities and research institutions; and (3) in terms of research hotspots, they revolve around four disciplinary issues, namely, primary research related to carbon market risk, carbon market risk categories, carbon market risk measurement, and response programs.

Abstract Background Tumor treatment still remains a clinical challenge, requiring the development of biocompatible and efficient anti-tumor nanodrugs. Carbon dots (CDs) has become promising nanomedicines for cancer therapy due to its low cytotoxicity and easy customization. Results Herein, we introduced a novel type of “green” nanodrug for multi-level cancer therapy utilizing Fe-doped carbon dots (Fe-CDs) derived from iron nutrient supplement. With no requirement for target moieties or external stimuli, the sole intravenous administration of Fe-CDs demonstrated unexpected anti-tumor activity, completely suppressing tumor growth in mice. Continuous administration of Fe-CDs for several weeks showed no toxic effects in vivo, highlighting its exceptional biocompatibility. The as-synthesized Fe-CDs could selectively induce tumor cells apoptosis by BAX/Caspase 9/Caspase 3/PARP signal pathways and activate antitumoral macrophages by inhibiting the IL-10/Arg-1 axis, contributing to its significant tumor immunotherapy effect. Additionally, the epithelial-mesenchymal transition (EMT) process was inhibited under the treatment of Fe-CDs by MAPK/Snail pathways, indicating the capacity of Fe-CDs to inhibit tumor recurrence and metastasis. Conclusions A three-level tumor treatment strategy from direct killing to activating immunity to inhibiting metastasis was achieved based on “green” Fe-CDs. Our findings reveal the broad clinical potential of Fe-CDs as a novel candidate for anti-tumor nanodrugs and nanoplatform.