Knowledge Of Structure Research Articles

Pregestational fructose-induced metabolic syndrome in Wistar rats causes sexually dimorphic behavioral changes in their offspring.

Metabolic syndrome (MetS), marked by enduring metabolic inflammation, has detrimental effects on cognitive performance and brain structure, influencing behavior. This study aimed to investigate whether maternal MetS could negatively impact the neurodevelopment and metabolism of offspring. To test this hypothesis, 2 months old female Wistar rats were subjected to a 10-week regimen of tap water alone or supplemented with 20% fructose to induce MetS. Dams were mated with healthy males to generate litters: OC (offspring from control dams) and OMetS (offspring from dams with MetS). To isolate prenatal effects, all pups were breastfed by control nurse dams, maintaining a standard diet and water ad libitum until weaning. Behavioral assessments were conducted between postnatal days (PN) 22 and 95, and metabolic parameters were analyzed post-sacrifice on PN100. Results from the elevated plus maze, the open field, and the marble burying tests revealed a heightened anxiety-like phenotype in OMetS females. The novel object recognition test showed that exclusively OMetS males had long-term memory impairment. In the reciprocal social interaction test, OMetS displayed a lower number of social interactions, with a notable increase in "socially inactive" behavior observed exclusively in females. Additionally, in the three-chamber test, social preference and social novelty indexes were found to be lower solely among OMetS females. An increase in visceral fat concomitantly with hypertriglyceridemia was the relevant postmortem metabolic finding in OMetS females. In summary, maternal MetS leads to enduring damage and adverse effects on offspring neurobehavior and metabolism, with notable sexual dimorphism.

A blind image super-resolution network guided by kernel estimation and structural prior knowledge

The goal of blind image super-resolution (BISR) is to recover the corresponding high-resolution image from a given low-resolution image with unknown degradation. Prior related research has primarily focused effectively on utilizing the kernel as prior knowledge to recover the high-frequency components of image. However, they overlooked the function of structural prior information within the same image, which resulted in unsatisfactory recovery performance for textures with strong self-similarity. To address this issue, we propose a two stage blind super-resolution network that is based on kernel estimation strategy and is capable of integrating structural texture as prior knowledge. In the first stage, we utilize a dynamic kernel estimator to achieve degradation presentation embedding. Then, we propose a triple path attention groups consists of triple path attention blocks and a global feature fusion block to extract structural prior information to assist the recovery of details within images. The quantitative and qualitative results on standard benchmarks with various degradation settings, including Gaussian8 and DIV2KRK, validate that our proposed method outperforms the state-of-the-art methods in terms of fidelity and recovery of clear details. The relevant code is made available on this link as open source.

Research on Chinese-English Patent Machine Translation Based on Fusion Strategy Model

In order to improve the translation quality of the machine translation model in the field of patent text, this paper proposes a patent knowledge fusion strategy. On the one hand, it integrates the structural knowledge, special phrases and professional terms in the patent text into the translation model as embedded comments, so as to improve its translation effect. On the other hand, based on XLNet, the basic translation model of Transformer is improved, and the translation performance of the model is further improved on the basis of integrating professional knowledge. The simulation results show that compared with the basic Transformer model and the Transformer model that only introduces fusion strategy, BLEU values of the proposed machine translation model combining fusion strategy and improved Transformer for the Chinese-English and English-Chinese translation tasks has increased by 3.77, 1.79, and 2.17, 0.46, respectively. It has a significant improvement in improving the quality of patent literature translation and is worthy of further research and promotion.

University presidents’ international education experience and the internationalization of higher education institutions: a study of leadership in China

ABSTRACT In the context of deepening globalization, accelerating the internationalization of higher education is the core development mission and important development mode of higher education today. This study investigates the impact of university presidents’ international education experience on the associated Chinese institutions’ internationalization. Based on imprinting theory, we argue that international education experiences received during sensitive periods leave an imprint on presidents concerning overseas countries, which may further impact their cognitive structures, enabling them to make strategic decisions conducive to international expansion. We manually collected multidimensional data from 868 Chinese general higher education institutions (HEIs) and 1,863 university presidents between 2001 and 2017. The results reveal that university presidents’ international education experiences significantly enhance the level of internationalization in HEIs. Additionally, when the country where presidents obtained international experiences has a closer cultural distance to China or the government in the location where presidents serve shows greater attention to higher education, can strengthen the positive impact of presidents’ international experiences on HEIs internationalization. This study makes important theoretical contributions to leadership literature, internationalization of higher education literature, and imprinting theory. It also has practical significance for the development and promotion of university presidents.

Sustainable Indigenous Governance: Catalysts for Economic Development in India

In India we have too many different communities with vast tradition and culture. In order to enhance their own way of life we have to protect the indigenous nature of governance. The idea of indigenous governance for economic development in India combines traditional knowledge and governance structures with modern development strategies. This model of indigenous governance is based on the central role of community-led initiatives, practices and management to promote sustainable economic growth. This research paper aims to make a correlation between indigenous governance and economic development. This paper also advocates a top-down approach to prioritizes the aspirations, needs and values of indigenous communities, to ensure their active participation in decision-making processes. This indigenous governance model is important to rethink development paradigms and make them more inclusive and equitable. By following this indigenous governance and leadership models, our country can find the way for a development strategy that is not only economically valuable but also socially and environmentally sustainable. India can harness the potential of its diverse cultures for inclusive and sustainable economic development by creating an enabling political environment that respects and takes into account indigenous governance systems. The principle of self-determination is a key to this indigenous model, which can allow different communities to follow their own development paths in accordance with their cultural heritage and environmental protection. To improve the economic well-being of indigenous peoples and to preserve their natural resources, cultural identity and traditional knowledge for future generations, the government should follow the indigenous approach of governance.

Structural Basis for the Interaction of Redβ Single-Strand Annealing Protein with Escherichia coli Single-Stranded DNA-Binding Protein

Redβ is a protein from bacteriophage λ that binds to single-stranded DNA (ssDNA) to promote the annealing of complementary strands. Together with λ-exonuclease (λ-exo), Redβ is part of a two-component DNA recombination system involved in multiple aspects of genome maintenance. The proteins have been exploited in powerful methods for bacterial genome engineering in which Redβ can anneal an electroporated oligonucleotide to a complementary target site at the lagging strand of a replication fork. Successful annealing in vivo requires the interaction of Redβ with E. coli single-stranded DNA-binding protein (SSB), which coats the ssDNA at the lagging strand to coordinate access of numerous replication proteins. Previous mutational analysis revealed that the interaction between Redβ and SSB involves the C-terminal domain (CTD) of Redβ and the C-terminal tail of SSB (SSB-Ct), the site for binding of numerous host proteins. Here, we have determined the x-ray crystal structure of Redβ CTD in complex with a peptide corresponding to the last nine residues of SSB (MDFDDDIPF). Formation of the complex is predominantly mediated by hydrophobic interactions between two phenylalanine side chains of SSB (Phe-171 and Phe-177) and an apolar groove on the CTD, combined with electrostatic interactions between the C-terminal carboxylate of SSB and Lys-214 of the CTD. Mutation of any of these residues to alanine significantly disrupts the interaction of full-length Redβ and SSB proteins. Structural knowledge of this interaction will help to expand the utility of Redβ-mediated recombination to a wider range of bacterial hosts for applications in synthetic biology.

Platelet Physiology.

Platelets are the smallest blood cells, numbering 150 to 350 × 109/L in healthy individuals. The ability of activated platelets to adhere to an injured vessel wall and form aggregates was first described in the 19th century. Besides their long-established roles in thrombosis and hemostasis, platelets are increasingly recognized as pivotal players in numerous other pathophysiological processes including inflammation and atherogenesis, antimicrobial host defense, and tumor growth and metastasis. Consequently, profound knowledge of platelet structure and function is becoming more important in research and in many fields of modern medicine. This review provides an overview of platelet physiology focusing particularly on the structure, granules, surface glycoproteins, and activation pathways of platelets.

A Lightweight Model of Underwater Object Detection Based on YOLOv8n for an Edge Computing Platform

The visual signal object detection technology of deep learning, as a high-precision perception technology, can be adopted in various image analysis applications, and it has important application prospects in the utilization and protection of marine biological resources. While the marine environment is generally far from cities where the rich computing power in cities cannot be utilized, deploying models on mobile edge devices is an efficient solution. However, because of computing resource limitations on edge devices, the workload of performing deep learning-based computationally intensive object detection on mobile edge devices is often insufficient in meeting high-precision and low-latency requirements. To address the problem of insufficient computing resources, this paper proposes a lightweight process based on a neural structure search and knowledge distillation using deep learning YOLOv8 as the baseline model. Firstly, the neural structure search algorithm was used to compress the YOLOv8 model and reduce its computational complexity. Secondly, a new knowledge distillation architecture was designed, which distills the detection head output layer and NECK feature layer to compensate for the accuracy loss caused by model reduction. When compared to YOLOv8n, the computational complexity of the lightweight model optimized in this study (in terms of floating point operations (FLOPs)) was 7.4 Gflops, which indicated a reduction of 1.3 Gflops. The multiply–accumulate operations (MACs) stood at 2.72 G, thereby illustrating a decrease of 32%; this saw an increase in the AP50, AP75, and mAP by 2.0%, 3.0%, and 1.9%, respectively. Finally, this paper designed an edge computing service architecture, and it deployed the model on the Jetson Xavier NX platform through TensorRT.

Pro-environmental behavior and the theory of planned behavior: a state of the art science mapping

PurposeThis science mapping analysis aims to discern current, emerging and future trends of pro-environmental behavior and the theory of planned behavior (TPB).Design/methodology/approachBibliometric analysis through bibliographic coupling and co-word analysis were used to reveal the progress of this phenomenon. Of the 1,120 documents search in Web of Science (WoS) database, 1,031 were used in this analysis after restricting to journal publications and studies after the year 2000.FindingsThe results show that four themes emerged, namely the fundamentals of TPB for pro-environmental behavior, antecedents of pro-environmental behavior, integration of TPB with the norm activation model and value belief theory and studies of pro-environmental behavior in developing countries. Environmental concern, environmental awareness, environmental knowledge and environmental education were the most commonly integrated variables.Research limitations/implicationsThis research is unique in the sense that the integration between TPB and other prominent theories of pro-environmental behavior is vital to predict individual pro-environmental behavior and understand the fundamental scientific importance of the domain. The norm activation model has been integrated with TPB in many pro-environmental behaviors. Mainstream media stakeholders should design and implement a plan for strategic communication and awareness campaigns in the community to encourage consumers to engage in many behaviors that lead to environmental sustainability.Originality/valueThis study presents a science mapping approach to uncover crucial knowledge structure related to pro-environmental behavior and the theory of planned behavior.

Religion would be a missing link in the case formulation of adolescents with conduct problems: an eclectic approach.

Psychiatry is concerned with mental health. Cognition is one of the key mental functions and manifests itself primarily as behavior. A behavior exhibited in response to a stimulus is influenced by biological (inherited), psychological (individual), and social (environmental) factors. During consolidation of an exhibited behavior, the factors affecting the individual's cognitive structure and personality play crucial roles. Underlying factors for a problematic behavior, and their weakness/strength levels are determined via the Biopsychosocial model. Empirically effective current practices to intervene the problematic behaviors do not always result in success. One of the reasons may be other elements that were omitted during the case formulation process. This article aims to stress the idea that whatever the underlying factor of a problematic behavior is, the most crucial determinant and/or pre-emptive factor in developing or maintaining that behavior might actually be the religion as a governing and directive philosophy on how to conduct oneself. In this instance, the key is in the hands of the parents or caregivers.

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

The field of carbon emissions in the construction industry has drawn extensive attention from researchers and practitioners due to the issue of global warming. In this study, an in-depth analysis of the research status, trends, and frontiers in the field of carbon emissions in the construction industry was carried out. The CiteSpace tool was used to visualize and analyze relevant papers from 1985 to 2023, to describe the overall knowledge structure in the field of carbon emissions in the construction industry using dual-map overlay analysis, journal co-citation network analysis, and keyword co-occurrence network analysis, to apply cluster analysis and burst detection to identify research trends in the field and the frontiers, and to analyze the scientific collaborations in the field. Further, the core issues in the field of carbon emissions in the construction industry were explored and relevant recommendations were proposed. The results are of great significance in identifying and analyzing knowledge systems and research patterns in the field of carbon emissions in the construction industry and help us to discover and understand the current deficiencies, trends, and frontiers in this field, thus providing useful suggestions and reflections for policymakers, practitioners, researchers, and other stakeholders.

Developing the general intertheory of documentology

The author examines the current status of documentology. The difference between documentary, ontological and theoretical knowledge is explained as related to the sciences of documentary information, and development of documentology is discussed through the lens of the theory of the structure of scientific knowledge. Based on the logical typology of cognition objects as subjects of thought, the just identifications of subjects of thought and concepts related to information units denoted by the term “document” and related terms, are offered. The author analyzes several popular interpretations of various information units. The need for the common intertheory of documentology addressing the phenomena, processes, info units within the documentation activities and provision of socially significant information to the society, is substantiated.

Context-based local-global fusion network for 3D point cloud classification and segmentation

3D point clouds have gained much research attention because of their ability to represent the spatial information of real-world environments in a detailed manner. Despite recent progress in point cloud processing with deep neural networks, most of them either implement sophisticated local feature aggregation methods or imitate 2D convolution operations in the range of K nearest neighbors with limited local context information. These methods may struggle to distinguish between similar geometric shapes within the local region of K nearest neighbors, such as doors and walls. To address this issue, we propose a novel local–global fusion network that captures the diverse local geometric shapes with global structure information. The proposed local–global fusion network comprises two main modules. Firstly, we have developed an effective approach for local context learning using incremental dilated KNN (IDKNN) as the neighbor selecting mechanism to enlarge the receptive field and incorporate more reliable points for local geometric shape learning. Secondly, a three-direction region-wise spatial attention (TRSA) algorithm has been developed to explore the global contextual dependencies. For global context learning, we first split the entire 3D space into regions with equal numbers of points, and, then, intra-region context features are extracted to learn the inter-region relations from three orthogonal directions, taking global structural knowledge into account. By fusing the local context information and global contextual dependencies, we establish a Local-Global Fusion Network, end-to-end framework, called LGFNet. Extensive experimental results on several benchmark datasets clearly demonstrate our approach can achieve state-of-the-art (SOTA) performance on point cloud classification, part segmentation, and indoor semantic segmentation. In addition, TRSA and IKDNN can be easily used in a plug-and-play fashion with various existing SOTA networks to substantially improve their performance. Our code is available at https://github.com/jasonwjw/IDKNN

Green Logistics Research Review Report

With the rapid development of the social economy, the modern logistics industry is, on the one hand, an important part of supporting the development of the national economy and enhancing the national economic competitiveness. On the other hand, logistics activities are also one of the main factors leading to the deterioration of the ecological environment and the waste of social resources, the development of green logistics has become the key to the transformation of the logistics industry. The article uses CiteSpace software to conduct a quantitative analysis of the literature on the theme of "green logistics", aiming to sort out the knowledge structure and research hot spots in the field of green logistics, summarize the research process of green logistics at home and abroad; secondly, classify the research and development directions of logistics Explore.

Plug‐and‐play adaptive surrogate modeling of parametric nonlinear dynamics in frequency domain

AbstractWe present an algorithm for constructing efficient surrogate frequency‐domain models of (nonlinear) parametric dynamical systems in a non‐intrusive way. To capture the dependence of the underlying system on frequency and parameters, our proposed approach combines rational approximation and smooth interpolation. In the approximation effort, locally adaptive sparse grids are applied to effectively explore the parameter domain even if the number of parameters is modest or high. Adaptivity is also employed to build rational approximations that efficiently capture the frequency dependence of the problem. These two features enable our method to build surrogate models that achieve a user‐prescribed approximation accuracy, without wasting resources in “oversampling” the frequency and parameter domains. Thanks to its non‐intrusiveness, our proposed method, as opposed to projection‐based techniques for model order reduction, can be applied regardless of the complexity of the underlying physical model. Notably, our algorithm for adaptive sampling can be used even when prior knowledge of the problem structure is not available. To showcase the effectiveness of our approach, we apply it in the study of an aerodynamic bearing. Our method allows us to build surrogate models that adequately identify the bearing's behavior with respect to both design and operational parameters, while still achieving significant speedups.

Collective cognition based analysis of community structure discovery algorithms

Social network topology can shape collective cognition and group behavior. Different social network topologies can facilitate various forms of collective cognition, leading to diverse collective cognition and group function. We analyzed the characteristics of contract networks and compared the performance of community structure discovery algorithms in social networks, using modularity as the assessment index. By examining the speed and effectiveness of these algorithms, we found that the Louvain algorithm and Girvan–Newman algorithm are suitable for discovering the network structure of sparse social networks. Experimental results have shown that the Louvain algorithm outperforms the Girvan–Newman algorithm on sparse networks across multiple scales. Finally, we learned a close relationship between collective cognition and community structure in contract networks, particularly influenced by the central nodes within these communities.

Transformative experiences: a conceptual analysis of the integration process

ABSTRACT Transformative experiences are becoming an important economic offer in various service contexts. Current research primarily focuses on what triggers transformation. However, what is much less known is the aftermath and integration of such experiences. A transformative experience can only be considered complete when glimpses of new insights are accommodated, new knowledge structures emerge, and epistemic expansion occurs. This renders the integration stage one of the most critical parts of the transformative process. This conceptual research paper aims to zoom in on transformative experience integration by proposing a multi-phasic process model. The paper is novel and advances transformation theory and practice in that it focuses on the overlooked mechanisms of integration. By bridging the fields of transformative service research, positive psychology, and experience design, the paper contributes an original model as a missing puzzle piece to intentionally design and guide life-changing and long-term personal development experiences in a service setting.

Validating the knowledge represented by a self-organizing map with an expert-derived knowledge structure.

Professionals are reluctant to make use of machine learning results for tasks like curriculum development if they do not understand how the results were generated and what they mean. Visualizations of peer reviewed medical literature can summarize enormous amounts of information but are difficult to interpret. This article reports the validation of the meaning of a self-organizing map derived from the Medline/PubMed index of peer reviewed medical literature by its capacity to coherently summarize the references of a core psychiatric textbook. Reference lists from ten editions of Kaplan and Sadock's Comprehensive Textbook of Psychiatry were projected onto a self-organizing map trained on Medical Subject Headings annotating the complete set of peer reviewed medical research articles indexed in the Medline/PubMed database (MedSOM). K-means clustering was applied to references from every edition to examine the ability of the self-organizing map to coherently summarize the knowledge contained within the textbook. MedSOM coherently clustered references into six psychiatric knowledge domains across teneditions (1967-2017). Clustering occurred at the abstract level of broad psychiatric practice including General/adult psychiatry, Child psychiatry, and Administrative psychiatry. The uptake of visualizations of published medical literature by medical experts for purposes like curriculum development depends upon validation of the meaning of the visualizations. The current research demonstrates that a self-organizing map (MedSOM) can validate the stability and coherence of the references used to support the knowledge claims of a standard psychiatric textbook, linking the products of machine learning to a widely accepted standard of knowledge.

Understanding Digital Turn in Urban Research: A Bibliometric Analysis of Contemporary Global Urban Literature

This paper aims to examine the effects of digital technologies on academic knowledge production in the field of urban research. It provides a comprehensive overview of the evolution of technology-based urban research literature, delineates the knowledge structure, and investigates prevalent trends. The study employs bibliometric analysis to analyze bibliographical and textual data extracted from scientific documents, which allows for the discovery of the existing epistemological structure of the discipline or research domain. The research was conducted by conducting a comprehensive and up-to-date review of the literature available in the Web of Science Core Collection regarding how concepts such as big data, artificial intelligence (AI), and the Internet of Things (IoT) are discussed in academic papers in the field of urban studies. A total of 2055 academic papers that met the established criteria were identified and analyzed using bibliometric analysis software called Bibliometrix. The results of the research aid in examining the integration of technological advancements into urban research, and reveal the temporal, spatial, and disciplinary distribution of scientific articles, as well as their reflections on new research areas and both frequently studied and yet unexplored topics.

Key structural requirements of benzamide derivatives for histone deacetylase inhibition: design, synthesis and biological evaluation.

Background: Histone deacetylase inhibitors (HDACIs) are important as anticancer agents. Objective: This study aimed to investigate some key structural features of HDACIs viathedesign, synthesisand biological evaluation of novel benzamide-based derivatives. Methods: Novel structures, designed using amolecular modification approach, were synthesized and biologically evaluated. Results: The results indicated that asubset of molecules with CH3/NH2 at R2 position possessselective antiproliferative activity. However, only those with an NH2 group showed HDACI activity. Importantly, the shorter the molecule length, the stronger HDACI. Among all, 7j was the most potent HDAC1-3 inhibitor and antiproliferative compound. Conclusion: The results of the present investigation couldprovide valuable structural knowledge applicable for the development of the HDACIs and benzamide-based antiproliferative agents in the future.