Global Theory Research Articles

Underwater multi-sensor fusion localization with visual-inertial-depth using hybrid residuals and efficient loop closing

Unmanned underwater vehicles (UUVs) need robust localization systems to complete their missions safely. To address the degradation of the localization accuracy of UUVs using visual-based simultaneous localization and mapping (SLAM) in underwater environments. A full SLAM system that fuses visual-inertial-depth measurements using hybrid residuals and efficient loop closing is proposed. Specifically, a novel visual hybrid residual is presented with an adaptive dynamic weighting factor. Reprojection and photometric double constraints are constructed simultaneously using the selected robust features. These constraints are then optimized jointly with the IMU measurements and the relative depth measurements to obtain a more accurate pose. Furthermore, a new three-step image-to-sequence strategy and a convolutional auto-encoder (CAE)-based global image representation are adopted to select candidate frames in order to improve the recall of underwater visual loop closure detection. Quantitative and qualitative analyses of real-world artificial pool and ocean experiments demonstrate the accuracy and effectiveness of the proposed system.

SOA: Seed point offset attention for indoor 3D object detection in point clouds

Three-dimensional object detection plays a pivotal role in scene understanding and holds significant importance in various indoor perception applications. Traditional methods based on Hough voting are susceptible to interference from background points or neighboring objects when casting votes for the target’s center from each seed point. Moreover, fixed-size set abstraction modules may result in the loss of structural information for large objects. To address these challenges, this paper proposes a three-dimensional object detection model based on seed point offset attention. The objective of this model is to enhance the model’s resilience to voting noise interference and alleviate feature loss for large-scale objects. Specifically, a seed point offset tensor is first defined, and then the offset tensor self-attention network is employed to learn the weights between votes, thereby establishing a correlation between the voting semantic features and the object structural information. Furthermore, an object surface perception module is introduced, which incorporates detailed features of local object surfaces into global feature representations through vote backtracking and surface mapping. Experimental results indicate that the model achieved excellent performance on the ScanNet-V2 (mAP@0.5, 60.3%) and SUN RGB-D (mAP@0.5, 64.0%) datasets, respectively improving by 2.6% (mAP@0.5) and 5.4% (mAP@0.5) compared to VoteNet.

Consciousness: A Tangled Hierarchy

Consciousness is one of the greatest unsolved mysteries of the brain. The simple and yet fundamental question, “How do we have subjective experience?” remains a matter of great debate among neuroscientists. These debates have generated some challenges for consciousness research, namely the hard problem—do normal scientific approaches work for consciousness— and the binding problem—how are sensory inputs experienced in a unified manner? Historically, consciousness has been studied by searching for the neural correlates of consciousness which are defined as the minimal brain areas jointly necessary for conscious experience. One of the most popular experimental paradigms used to investigate the neural correlates of consciousness is binocular rivalry. In binocular rivalry, separate images are simultaneously presented to each eye and perception vacillates between them, providing a way of determining which neural changes are due to shifts in perceptual consciousness rather than changes in stimuli. As the research progressed, however, scientists realized that finding correlates of consciousness was insufficient and that explanatory theories of consciousness were needed to make further progress. This review article aims to introduce the field of consciousness research by presenting some of the core challenges to the field: tracing the search for the neural correlates of consciousness through the lens of binocular rivalry, and exploring four popular theories of consciousness. These are higher-order theory, global neuronal workspace theory, integrated information theory, and predictive processing. It concludes with a glance at the future directions of research that aim to resolve the beguiling phenomenon of consciousness.

Dual-modal non-local context guided multi-stage fusion for indoor RGB-D semantic segmentation

Complementarily fusing RGB and depth images while effectively suppressing task-irrelevant noise is crucial for achieving accurate indoor RGB-D semantic segmentation. In this paper, we propose a novel deep model that leverages dual-modal non-local context to guide the aggregation of complementary features and the suppression of noise at multiple stages. Specifically, we introduce a dual-modal non-local context encoding (DNCE) module to learn global representations for each modality at each stage, which are then utilized to facilitate cross-modal complementary clue aggregation (CCA). Subsequently, the enhanced features from both modalities are merged together. Additionally, we propose a semantic guided feature rectification (SGFR) module to exploit rich semantic clues in the top-level merged features for suppressing noise in the lower-stage merged features. Both the DNCE-CCA and the SGFR modules provide dual-modal global views that are essential for effective RGB-D fusion. Experimental results on two public indoor datasets, NYU Depth V2 and SUN-RGBD, demonstrate that our proposed method outperforms state-of-the-art models of similar complexity.

O-087 Use and safety of assisted reproductive technology in young BRCA carriers with a pregnancy after treatment for breast cancer: results from an international cohort study

Abstract Study question Does assisted reproductive technology (ART) to achieve pregnancy after breast cancer (BC) adversely affect maternal or fetal outcomes in germline BRCA1/2 pathogenic variants (PVs) carriers? Summary answer This global study showed that ART is safe in BC survivors harboring BRCA1/2 PVs, with no apparent worsening of maternal prognosis or fetal outcomes. What is known already Very little information is currently available on the safety of fertility treatments in BC survivors harboring BRCA1/2 PVs. The previous published work on this topic included only 22 cases of patients who used any kind of ART to become pregnant; the small number of included patients did not allow for statistical analysis. Hence, concerns remain among physicians counseling young BRCA carriers with prior history of BC on the safety of using ART to attempt pregnancy Study design, size, duration International, multicenter, hospital-based, retrospective cohort study across 78 centers worldwide. The study included 4732 women harboring known BRCA1/2 PVs and diagnosed at ≤ 40 years with stage I-III BC between January 2000 and 2020 (Lambertini M et al., JAMA 2024;331:49-59). This specific analysis included 543 women with a pregnancy after prior history of BC harboring a BRCA1/2 PV. Participants/materials, setting, methods Among 543 young BRCA carriers with a pregnancy after BC, 436 conceived naturally and 107 using ART. ART procedures included embryo transfer under hormonal replacement therapy following oocyte or embryo cryopreservation at diagnosis, treatment with oocyte donation, ovulation induction and In-Vitro-Fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) carried out after anticancer treatments. Survival analyses compared patients achieving a pregnancy spontaneously (spontaneous pregnancy group) vs. using ART (ART group). Main results and the role of chance Of 107 pregnancies achieved through ART, 45 (42.1%) were obtained with oocytes or embryo cryopreserved at diagnosis, 33 (30.8%) with ovarian stimulation and IVF/ICSI after completion of anticancer therapies, 21 (19.6%) with embryo transfer following oocyte donation and for 8 ART type was missing. As compared to survivors in the spontaneous pregnancy group, those in the ART group were older at the time of conception (37.1 vs. 34.3 years, p < 0.001), had more often hormone receptor-positive BC (46 (43.4%) vs. 132 (30.8%), p = 0.016), and a longer median time from BC diagnosis to conception (4.2 vs. 3.3 years, p = 0.004). No statistically significant differences in pregnancy complications were observed between cohorts (p = 0.382). However, patients who conceived with ART had more miscarriages (12, 11.3%) than those who conceived spontaneously (38, 8.8%). The opposite was true for induced abortion (1 (0.9%) vs. 36 (8.3%), respectively). At a median follow up of 9.1 years (interquartile range 6.4-13.4), no detrimental effect of ART on disease-free survival (DFS) was observed with 13 DFS events observed among the 99 patients undergoing ART and 118 DFS events in patients with spontaneous pregnancy (log-rank p = 0.147). Limitations, reasons for caution Despite the global representation, this retrospective study included a small number of patients for this specific analysis. Nevertheless, this is the largest analysis to date investigating safety of ART in BC survivors harboring BRCA1/2 PVs. Wider implications of the findings Young BRCA carriers can be reassured that the use of ART to achieve pregnancy is safe. Oocyte/embryo cryopreservation in young BRCA carriers allows access to pre-implantation genetic testing, if the patients are interested. Trial registration number NCT03673306

A methodological approach for enhancing visualization of country data representation in the presence of significant spatial disparity

In this article, we present a methodological approach to address spatial disparity in global data representation, introducing an algorithm called Flexible Mapping to Understand Spatial Analysis (FLEMUSA). We utilize world maps to depict various data points across countries, revealing substantial variation among them. However, conventional choropleth maps often fail to effectively represent regions with sparse data, obscuring valuable insights. To mitigate this issue, we propose interactive graphical methods in both two and three dimensions, implemented through open-source Python code accessible via Google Colab. Our approach includes several contributions such as excluding countries without data from the representation, scaling magnitudes within country borders, focusing on regional analysis, and using logarithmic scales for bubble maps proportional to country sizes. Additionally, we offer interactive 2D and 3D representations, rotatable 3D representations, and zoomable options, facilitating enhanced visualization of regional similarities amidst data heterogeneity. Through this algorithm, we aim to improve the clarity and interpretability of spatial data analysis, integrating solutions for extreme data overdispersion, all programmed with open-source code.-Utilization of world maps for visual representation of data across countries mitigating the overdispersion step by step.-Implementation of graphical methods, including interactive 2D and 3D maps, to address spatial disparity.-Provision of open-source code for customizable graphical representations, facilitating implementation in online journals as interactive code snippets.

Extent of magnitude representation deficit and relationship with arithmetic skills in children with 22q11.2DS

BackgroundPrevious studies have produced conflicting results concerning the extent of magnitude representation deficit and its relationship with arithmetic achievement in children with 22q11.2 deletion syndrome. More specifically, it remains unclear whether deficits are restricted to visuospatial content or are more general and whether they could explain arithmetical impairment.MethodsFifteen 5- to 12-year-old children with 22q11.2 deletion syndrome and 23 age-matched healthy controls performed a non-symbolic magnitude comparison task. Depending on the trial, participants had to compare stimuli with high or low visuospatial load (visuospatial stimuli or temporal sequence of visual stimuli). The participants also completed a battery of arithmetic skills (ZAREKI-R) and a battery of global cognitive functioning (WISC-V or WPPSI-IV), from which working memory and visuospatial indices were derived.ResultsChildren with 22q11.2DS responded as fast as healthy controls did but received fewer correct responses, irrespective of visuospatial load. In addition, their performance in the non-symbolic magnitude comparison task did not correlate with the ZAREKI total score, while the working memory index did.ConclusionChildren with 22q11.2DS might suffer from a global magnitude representation deficit rather than a specific deficit due to visuospatial load. However, this deficit alone does not seem to be related to arithmetic achievement. Working memory might be a better concern of interest in favoring arithmetic skills in patients with 22q11.2 deletion syndrome.Trial registrationClinicaltrials, NCT04373226. Registered 16 September 2020.

P-003 Impact of SARS-CoV-2 infection on circulating male sex hormones and human semen quality: a systematic review and meta-analysis

Abstract Study question Does SARS-CoV-2 infection alter human male sex steroid hormones and semen quality? Summary answer SARS-CoV-2 infection reduced circulating testosterone but increased LH and prolactin levels. Also, SARS-CoV-2 infection reduced ejaculate volume, sperm count, concentration, viability, motility, and normal morphology. What is known already SARS-CoV-2 infection has been reported to be associated with a decline in semen quality, although this remains debatable given the novelty of the viral infection. In addition, available data on semen quality and circulating male reproductive hormones in association with SARS-CoV-2 infection is limited and inconsistent. Study design, size, duration This study provides a systematic review and meta-analysis of the impacts of SARS-CoV-2 infection on circulating male sex hormones and semen quality. Participants/materials, setting, methods This study was conducted according to PRISMA guidelines. Following a pre-defined strategic protocol, 852 studies were screened, and 40 articles were identified as eligible for this study. Included studies were from China (7), Germany (1), India (1), Indonesia (1), Iran (6), Iraq (2), Italy (5), Jordan (2), Russia (1), Turkey (12), UK (1), and USA (1). Main results and the role of chance A total of 4807 male subjects were included in the meta-analysis. SARS-CoV-2 infection significantly reduced circulating testosterone and testosterone/LH ratio (an index of Leydig cell function) but increased LH and prolactin levels even after sensitivity analysis when compared to SARS-CoV-2 uninfected patients. Also, SARS-CoV-2 infection significantly reduced ejaculate volume, sperm count, concentration, viability, total and progressive motility when compared to SARS-CoV-2 uninfected patients, although only ejaculate volume and sperm viability remained significant after sensitivity analysis. More so, ejaculate volume, sperm viability, and progressive motility were significantly reduced in SARS-CoV-2-infected patients before treatment when compared with after treatment; nonetheless, only sperm viability, and progressive motility remained significant after sensitivity analysis. Furthermore, ejaculate volume, sperm count, concentration, viability, total and progressive motility were significantly reduced in SARS-CoV-2-infected patients when compared with their pre-COVID state; these alterations, except sperm progressive motility, persisted after sensitivity analysis. Limitations, reasons for caution Although there is an increasing amount of data on the subject matter, SARS-CoV-2-infection is yet novel, and a continuous report and meta-analysis remains pertinent. Also, the included studies are from a few countries, which may not necessarily be a good global representation. Wider implications of the findings This study revealed that SARS-CoV-2-infection significantly reduced circulating testosterone, LH, and semen quality. SARS-CoV-2-induced reduction in semen quality is, at least in part, via a testosterone-dependent mechanism. These findings strengthen existing evidence in the literature on the negative impact of SARS-CoV-2-infection on male reproductive health. Trial registration number N/A

A discussion on coloniality and global social theory

AbstractThe conversation between Ali Meghji and Manuela Boatcă focuses on how modernity/coloniality may (or may not) be a productive sociological concept in the remit of global social theory and wider political movements. Speaking from within different locations in the imperial core (England and Germany respectively), we discuss how the concept of modernity/coloniality has traveled over to our respective European contexts, and the ways it has informed our sociological approaches to matters such as inter‐imperiality, creolization, decolonization, global racisms and anti‐racisms, and historical sociology. The conversational approach is meant to tease out the multiple ways that modernity/coloniality can inform different—albeit related—aspects of sociological work, and also the varying contexts from which global social theory can be produced.

PMTT: Parallel multi-scale temporal convolution network and transformer for predicting the time to aging failure of software systems

Software aging is one of the significant factors affecting the reliability and availability of long-running software systems, such as Android, Cloud systems, etc. The time to aging failure (TTAF) prediction for software systems plays a crucial role in proactive rejuvenation scheduling through machine learning or statistical analysis techniques, due to its ability to determine when to perform rejuvenation to mitigate the aging effects. However, software aging characterization is relatively complicated, and only fitting the variations for a single aging indicator cannot grasp the comprehensive degradation process across different case systems; moreover, since software systems often exhibit long and short-term inherent degradation characteristics, existing prediction models possess a poor ability for modeling both global and local information simultaneously. To tackle the above problems, a novel TTAF prediction framework based on the parallel multi-scale temporal convolution network and transformer (named PMTT) is proposed, by mapping various system running indicators reflecting the software aging to TTAF. PMTT possesses the following distinctive characteristics. First, a local feature extraction module that contains multiple channel TCNs with different scales is developed to extract inherent local information from the raw input. Second, in a parallel manner, a global feature extraction module integrating transformer blocks is built to extract global information representation synchronously using the self-attention mechanism. Afterward, high-level global–local features extracted from different channels are fused, and TTAF is estimated through two fully connected regression layers using the fused features. The proposed PMTT has been compared to seven competitors using run-to-failure data collected from Android and OpenStack systems. The experiments have demonstrated the superiority of PMTT, showing an average improvement of 11.2%, 9.0%, and 9.3% in performance across three evaluation metrics compared with the optimal baseline model.

Modernity and inter‐imperiality: Rethinking social theory in East Asia

AbstractThis paper aims to contribute to global social theory by contextualizing modernity/coloniality in East Asia. It begins with a reconstruction and evaluation of three sociological accounts of East Asian modernity, namely Kazuko Tsurumi's theory of endogenous development, Sun Liping's theory of communist civilization, and Chang Kyung Sup's theory of compressed modernity. Despite their insights, these theorists fail to transcend the Eurocentrism and methodological nationalism implicit in the paradigm of latecomer modernization. These problems can be better addressed by anti‐colonial perspectives, including Tani Barlow's colonial modernity, Chen Kuan‐Hsing's inter‐Asia, Xie Lizhong's post‐Western sociology, and Wang Mingming's civilizational anthropology. Yet European hegemony continues to define the spatiotemporal frame of these perspectives. To overcome the lingering problem of Eurocentrism, Laura Doyle's notion of inter‐imperiality is employed to highlight the power‐laden interaction and interdependence among imperial powers in the global and regional history of East Asia. This alternative approach, I argue, can shed new light on East Asian modernity and delineate its dialectical formation.

Perceptual Awareness in Human Infants: What is the Evidence?

Perceptual awareness in infants during the first year of life is understudied, despite the philosophical, scientific, and clinical importance of understanding how and when consciousness emerges during human brain development. Although parents are undoubtedly convinced that their infant is conscious, the lack of adequate experimental paradigms to address this question in preverbal infants has been a hindrance to research on this topic. However, recent behavioral and brain imaging studies have shown that infants are engaged in complex learning from an early age and that their brains are more structured than traditionally thought. I will present a rapid overview of these results, which might provide indirect evidence of early perceptual awareness and then describe how a more systematic approach to this question could stand within the framework of global workspace theory, which identifies specific signatures of conscious perception in adults. Relying on these brain signatures as a benchmark for conscious perception, we can deduce that it exists in the second half of the first year, whereas the evidence before the age of 5 months is less solid, mainly because of the paucity of studies. The question of conscious perception before term remains open, with the possibility of short periods of conscious perception, which would facilitate early learning. Advances in brain imaging and growing interest in this subject should enable us to gain a better understanding of this important issue in the years to come.

Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.

Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and a top-down modulator. We first use a common CNN block to form the local pathway that aims to extract fine local features of the input image. Then, we use a transformer encoder to form the global pathway to capture global structural and contextual information among local parts in the input image. Finally, we construct the learnable top-down modulator where fine local features of the local pathway are modulated by global representations of the global pathway. For ease of use, we encapsulate the dual-pathway computation and modulation process into a building block, called the global-local block (GL block), and a CogNet of any depth can be constructed by stacking a necessary number of GL blocks one after another. Extensive experimental evaluations have revealed that the proposed CogNets have achieved the state-of-the-art performance accuracies on all the six benchmark datasets and are very effective for overcoming the "texture bias" and the "semantic confusion" problems faced by many CNN models.

TDG-Diff: Advancing customized text-to-image synthesis with two-stage diffusion guidance

Recently, there has been widespread attention and significant progress in customized text-to-image synthesis based on diffusion models. However, reconstructing multiple concepts in the same scene remains highly challenging. Therefore, we propose a novel framework called TDG-Diff, which employs a two-stage diffusion guidance to achieve customized image synthesis with multiple concepts. TDG-Diff focuses on improving the sampling process of the diffusion model. Specifically, TDG-Diff subdivides the sampling process into two key stages: attribute separation and appearance refinement, introducing spatial constraints and concept representations for sampling guidance. In the attribute separation stage, TDG-Diff introduces a novel attention modulation method. This method effectively separates the attributes of different concepts based on spatial constraint information, reducing the risk of entanglement between attributes of different concepts. In the appearance refinement stage, TDG-Diff proposes a fusion sampling approach, which combines global text descriptions and concept representations to optimize and enhance the model’s ability to capture and represent fine-grained details of concepts. Extensive qualitative and quantitative results demonstrate the effectiveness of TDG-Diff in customized text-to-image synthesis.

Local-aware global attention network for person re-identification based on body and hand images

Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. The global and local branches intends to capture global context and fine-grained information, respectively. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.

Analysis Global of The Effect of Corporate Social Responsibility On Financial Performance

This study investigates the effect of CSR on corporate financial performance globally. Data is drawn from various countries to provide a broader picture. Meta analysis was used to collect data from various online sources mainly from Google Scholar. The research results from seven indexed international journals showed mixed findings. Some journals show a positive relationship between CSR and financial performance, while others highlight a negative impact, especially in the short term. Factors such as firm size, industry, and geographical location moderate the relationship. The discussion outlines the importance of CSR in a global context, relevant theories, and challenges in this research. In conclusion, firms are advised to integrate CSR in their various business strategies to improve financial performance, although it requires further research to confirm the causal relationship.

Global well-posedness and asymptotic behavior for the Euler-alignment system with pressure

We study the Cauchy problem of the compressible Euler system with strongly singular velocity alignment. We establish a global well-posedness theory for the system with small smooth initial data. Additionally, we derive asymptotic emergent behaviors for the system, providing time decay estimates with optimal decay rates. Notably, the optimal decay rate we obtain does not align with the corresponding fractional heat equation within our considered range, where the parameter α∈(0,1). This highlights the distinct feature of the alignment operator.

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decision-making. Deep learning methods demonstrate superiority in TSS classification. However, they often overfit task-irrelevant features due to insufficient paired labeled data, resulting in poor generalization. We observed that unpaired data are readily available and inherently carry task-relevant cues, but are less often considered and explored. Based on this, in this paper, we propose to fully excavate the potential of unpaired unlabeled data and use them to facilitate the downstream AIS analysis task. We first analyze the utility of features at the varied grain and propose a multi-grained contrastive learning (MGCL) framework to learn task-related prior representations from both coarse-grained and fine-grained levels. The former can learn global prior representations to enhance the location ability for the ischemic lesions and perceive the healthy surroundings, while the latter can learn local prior representations to enhance the perception ability for semantic relation between the ischemic lesion and other health regions. To better transfer and utilize the learned task-related representation, we designed a novel multi-task framework to simultaneously achieve ischemic lesion segmentation and TSS classification with limited labeled data. In addition, a multi-modal region-related feature fusion module is proposed to enable the feature correlation and synergy between multi-modal deep image features for more accurate TSS decision-making. Extensive experiments on the large-scale multi-center MRI dataset demonstrate the superiority of the proposed framework. Therefore, it is promising that it helps better stroke evaluation and treatment decision-making.

Multi-level Symmetric Semantic Alignment Network for image–text matching

Image–text matching has attracted much attention as one of the visual-linguistic tasks. Most of the existing methods tend to concentrate on single-level semantic similarity by global embeddings or local alignment, which fail to distinguish the ambiguous image–text instances with highly similar contexts due to insufficient interactions of cross-modal features. To mitigate this problem, in this paper, we propose a novel Multi-level Symmetric Semantic Alignment Network (MSSAN), which fully exploits the multi-level semantic features to capture the complicated correlations between images and text. Specifically, global–global-level alignment is first performed based on visual–textual global representations. Then, considering the local semantic consistency, local–local-level alignment is carried out through inter-modal bidirectional cross-attention module to model fine-grained region–word relations. Moreover, a Multi-granularity Feature Fusion Module (MFFM) is constructed to learn intra-modal distribution of significance and incorporate global concepts into local features to obtain more comprehensive semantic representations, thus achieving global–local-level alignment. Finally, in order to achieve a strong separation of semantically similar samples, we develop a novel generic triplet ranking loss with adaptively updatable margins to train our model. Extensive experimental results on Flickr8K, MSCOCO and Flickr30K datasets demonstrate that our proposed MSSAN is superior to other state-of-the-art methods by a considerable margin.

The core meaning of the three major global initiatives and the logic of global governance

In the new era, Chinese President Xi Jinping has continuously put forward the Global Development Initiative, the Global Security Initiative and the Global Civilization Initiative, which complement each other and aim to build a peaceful, prosperous and diverse world. Development is the foundation of security and civilization, and security is the premise of development and civilization. We need to build a community with a shared future for mankind based on the global development initiative, the global security initiative, and the global civilization initiative as the clarion call. The "Three Global Initiatives" have supplemented and improved western global governance theories from many aspects, demonstrated the unique understanding and practice of Chinese-style modernization, injected new impetus into the building of a more just and equitable international order, and provided new ideas and solutions for global governance.