Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model.

Given the significant impact of transportation-related carbon emissions on air quality and climate change, understanding the regional dynamics of these emissions is crucial. Despite numerous studies on carbon emissions, there is a lack of comprehensive analysis of China's interprovincial transport carbon emission correlation network. Based on China's provincial data from 2007 to 2021, we analyzed the network's basic structural characteristics and categorized it into four significant plates to investigate their interactions. Subsequently, motif analysis is employed to examine the micro-correlation patterns within the network, and the Exponential random graph model (ERGM) is utilized to analyze the network's formation mechanism. Findings reveal that: (1) Provinces with high correlation intensity are predominantly concentrated in the eastern region, such as Shanghai and Beijing. Additionally, provinces in the eastern region assume a central role in the transport carbon emission correlation network, mainly receiving carbon emissions from other provinces. In contrast, the western region primarily emits carbon emissions to other provinces, continuously converging towards the center. (2) The network is segmented into net beneficiary plate, net overflow plate, bidirectional spillover plate, and broker plate, with distinct roles and influences across different years. (3) Bidirectional correlation motif structures emerge as primary influencers within the network, although specific structures impede interregional communication and collaborative emission reduction. (4) Internal network's structural variables, such as mutuality, cyclic triple, and geometrically weighted edgewise shared partner, along with influencing factors including government intervention, urbanization rate, openness, fiscal expenditure on transport, and province adjacency significantly impact the formation of the transport carbon emission correlation network. The above transportation network research provides a theoretical basis for the country to promote low-carbon transportation and improve air quality, and also has important guiding significance for the cross-regional collaborative emission reduction work of provinces.

Global trends in publications regarding macrophages-related diabetic foot ulcers in the last two decades.

BACKGROUND: Diabetic foot ulcers (DFUs) are one of the most severe and popular complications of diabetes. The persistent non-healing of DFUs is the leading cause of ampu-tation, which causes significant mental and financial stress to patients and their families. Macrophages are critical cells in wound healing and perform essential roles in all phases of wound healing. However, no studies have been carried out to systematically illustrate this area from a scientometric point of view. Although there have been some bibliometric studies on diabetes, reports focusing on the investigation of macrophages in DFUs are lacking. AIM: To perform a bibliometric analysis to systematically assess the current state of research on macrophage-related DFUs. METHODS: The publications of macrophage-related DFUs from January 1, 2004, to December 31, 2023, were retrieved from the Web of Science Core Collection on January 9, 2024. Four different analytical tools: VOSviewer (v1.6.19), CiteSpace (v6.2.R4), HistCite (v12.03.07), and Excel 2021 were used for the scientometric research. RESULTS: A total of 330 articles on macrophage-related DFUs were retrieved. The most published countries, institutions, journals, and authors in this field were China, Shanghai Jiao Tong University of China, Wound Repair and Regeneration, and Aristidis Veves. Through the analysis of keyword co-occurrence networks, historical direct citation networks, thematic maps, and trend topics maps, we synthesized the prevailing research hotspots and emerging trends in this field. CONCLUSION: Our bibliometric analysis provides a comprehensive overview of macrophage-related DFUs research and insights into promising upcoming research.

Microbiological profile of diabetic foot infections in China and worldwide: a 20-year systematic review.

Introduction: Pathogens causing diabetic foot infections (DFIs) vary by region globally; however, knowledge of the causative organism is essential for effective empirical treatment. We aimed to determine the incidence and antibiotic susceptibility of DFI pathogens worldwide, focusing on Asia and China. Methods: Through a comprehensive literature search, we identified published studies on organisms isolated from DFI wounds from January 2000 to December 2020. Results: Based on our inclusion criteria, we analyzed 245 studies that cumulatively reported 38,744 patients and 41,427 isolated microorganisms. DFI pathogens varied according to time and region. Over time, the incidence of Gram-positive and Gram-negative aerobic bacteria have decreased and increased, respectively. America and Asia have the highest (62.74%) and lowest (44.82%) incidence of Gram-negative bacteria, respectively. Africa has the highest incidence (26.90%) of methicillin-resistant Staphylococcus aureus. Asia has the highest incidence (49.36%) of Gram-negative aerobic bacteria with species infection rates as follows: Escherichia coli, 10.77%; Enterobacter spp., 3.95%; and Pseudomonas aeruginosa, 11.08%, with higher local rates in China and Southeast Asia. Linezolid, vancomycin, and teicoplanin were the most active agents against Gram-positive aerobes, while imipenem and cefoperazone-sulbactam were the most active agents against Gram-negative aerobes. Discussion: This systematic review showed that over 20 years, the pathogens causing DFIs varied considerably over time and region. This data may inform local clinical guidelines on empirical antibiotic therapy for DFI in China and globally. Regular large-scale epidemiological studies are necessary to identify trends in DFI pathogenic bacteria. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023447645.

Prediction of carbon emissions in China's construction industry using an improved grey prediction model.

As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucial for energy conservation and emission reduction. In this paper, we utilize the carbon emission data from China's national and provincial construction sectors from 2012 to 2021, employ the grey prediction model optimized by the particle swarm optimization algorithm, coupled with a metabolic algorithm, to forecast the carbon emissions of the construction industry across China and its provinces. The results demonstrate that: (1) The dynamic grey prediction model combined with the metabolism algorithm has a better prediction effect than the classical model, and the relative error is reduced from 5.103 % to 0.874 %. (2) The carbon emissions of China's construction industry will continue to rise in the next decade, but the growth rate will decrease, and the proportion of indirect carbon emissions continues to increase. (3) There is a marked regional disparity in carbon emissions, with the eastern region exhibiting higher emission levels yet slower growth. In contrast, the western region has lower emission levels but experiences faster growth. These studies provide valuable insights for both the existing approaches to energy conservation and emission reduction, as well as for future policy improvements.

Efficiency evaluation of China's transportation system considering carbon emissions: Evidence from big data analytics methods.

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.

MicroRNA-18a regulates the Pyroptosis, Apoptosis, and Necroptosis (PANoptosis) of osteoblasts induced by tumor necrosis factor-α via hypoxia-inducible factor-1α.

BACKGROUND: Tumor necrosis factor-α (TNF-α) is involved in inflammatory responses and promotes cell death and the inhibition of osteogenic differentiation. MicroRNA (miRNA) plays a crucial role in the infected bone diseases, however, the biological role of miRNAs in inflammation-induced impaired osteogenic differentiation remains unclear. This study aimed to explore the role of miRNA-18a-5p (miR-18a) in regulating PANoptosis and osteogenic differentiation in an inflammatory environment via hypoxia-inducible factor-1α (HIF1-α). METHODS: The expression of miR-18a in MC3T3-E1 cells was analyzed using quantitative reverse transcription-polymerase chain reaction in an inflammatory environment induced by TNF-α. The expression of HIF1-α and NLRP3 in LV-miR-18a or sh-miR-18a cells was analyzed using western blotting. Fluorescence imaging for cell death, flow cytometry, and alkaline phosphatase activity analysis were used to analyze the role of miR-18a in TNF-α-induced PANoptosis and the inhibition of osteogenic differentiation. An animal model of infectious bone defect was established to validate the regulatory role of miR-18a in an inflammatory environment. RESULTS: The expression of miRNA-18a in the MC3T3-E1 cell line was significantly lower under TNF-α stimulation than in the normal environment. miR-18a significantly inhibited the expression of HIF1-α and NLRP3, and inhibition of HIF1-α expression further inhibited NLRP3 expression. Furthermore, inhibition of miR-18a expression promoted the TNF-α-induced PANoptosis and inhibition of osteogenic differentiation, whereas miR-18a overexpression and the inhibition of both HIF1-α and NLRP3 reduced the effects of TNF-α. These findings are consistent with those of the animal experiments. CONCLUSION: miRNA-18a negatively affects HIF1-α/NLRP3 expression, inhibits inflammation-induced PANoptosis, and impairs osteogenic differentiation. Thus, it is a potential therapeutic candidate for developing anti-inflammatory strategies for infected bone diseases.

The impact of population agglomeration on ecological resilience: Evidence from China.

Due to climate change and human activities, ecological and environmental issues have become increasingly prominent and it is crucial to deeply study the coordinated development between human activities and the ecological environment. Combining panel data from 31 provinces in China spanning from 2011 to 2020, we employed a fixed-effects model, a threshold regression model, and a spatial Durbin model to empirically examine the intricate impacts of population agglomeration on ecological resilience. Our findings indicate that population agglomeration can have an impact on ecological resilience and this impact depends on the combined effects of agglomeration and crowding effects. Also, the impact of population agglomeration on ecological resilience exhibits typical dual-threshold traits due to differences in population size. Furthermore, population agglomeration not only directly impacts the ecological resilience of the local area, but also indirectly affects the ecological resilience of surrounding areas. In conclusion, we have found that population agglomeration does not absolutely impede the development of ecological resilience. On the contrary, to a certain extent, reasonable population agglomeration can even facilitate the progress of ecological resilience.

Study for Some Eccentricity-based Topological Indices of Second Type of Dominating David-derived Network.

BACKGROUND: Dominating David-derived networks are widely studied due to their fractal nature, with applications in topology, chemistry, and computer sciences. The use of molecular structure descriptors is a standard procedure that is used to correlate the biological activity of molecules with their chemical structures, which can be useful in the field of pharmacology. OBJECTIVE: This article's goal is to develop analytically closed computing formulas for eccentricity-based descriptors of the second type of dominating David-derived derived network. Thermodynamic characteristics, physicochemical properties, and chemical and biological activities of chemical graphs are just a few of the many properties that may be determined using these computation formulas. METHODS: Vertex sets were initially divided according to their degrees, eccentricities, and cardinalities of occurrence. The eccentricity-based indices are then computed using some combinatorics and these partitions. RESULTS: Total eccentricity, average eccentricity, and the Zagreb index are distance-based topological indices utilized in this study for the second type of dominating David-derived network, denoted as D_2 (m). CONCLUSION: These calculations will assist the readers in estimating the fractal and difficult-to-handle thermodynamic and physicochemical aspects of chemical structure. Apart from configuration and impact resistance, the D_2 (m) design has been used for fundamental reasons in a variety of technical and scientific advancements.

Degree Descriptors and Graph Entropy Quantities of Zeolite ACO.

BACKGROUND: Cheminformatics is a fascinating emerging subfield of chemical graph theory that studies quantitative structure-activity and property relationships of molecules and, in turn, uses these to predict the physical and chemical properties, which are extremely useful in drug discovery and optimization. Knowledge discovery can be put to use in pharmaceutical data matching to help in finding promising lead compounds. METHOD: Topological descriptors are numerical quantities corresponding to the chemical structures that are used in the study of these phenomena. RESULT: This paper is concerned with developing the generalized analytical expression of topological descriptors for zeolite ACO structures with underlying degree and degree-sum parameters. CONCLUSION: To demonstrate improved discrimination power between the topological descriptors, we have further modified Shannon's entropy approach and used it to calculate the entropy measures of zeolite ACO structures.

Entropy Related to K-Banhatti Indices via Valency Based on the Presence of C6H6 in Various Molecules.

Entropy is a measure of a system's molecular disorder or unpredictability since work is produced by organized molecular motion. Shannon's entropy metric is applied to represent a random graph's variability. Entropy is a thermodynamic function in physics that, based on the variety of possible configurations for molecules to take, describes the randomness and disorder of molecules in a given system or process. Numerous issues in the fields of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. These applications cover quantifying molecules' chemical and electrical structures, signal processing, structural investigations on crystals, and molecular ensembles. In this paper, we look at K-Banhatti entropies using K-Banhatti indices for C6H6 embedded in different chemical networks. Our goal is to investigate the valency-based molecular invariants and K-Banhatti entropies for three chemical networks: the circumnaphthalene (CNBn), the honeycomb (HBn), and the pyrene (PYn). In order to reach conclusions, we apply the method of atom-bond partitioning based on valences, which is an application of spectral graph theory. We obtain the precise values of the first K-Banhatti entropy, the second K-Banhatti entropy, the first hyper K-Banhatti entropy, and the second hyper K-Banhatti entropy for the three chemical networks in the main results and conclusion.

Combination of D-dimer level and neutrophil to lymphocyte ratio predicts long-term clinical outcomes in acute coronary syndrome after percutaneous coronary intervention.

BACKGROUND: High D-dimer (DD) is associated with short-term adverse outcomes in patients with acute coronary syndrome (ACS). In ACS patients who underwent percutaneous coronary intervention (PCI), however, the value of DD (or combined with neutrophil to lymphocyte ratio [NLR]) to predict long-term major adverse cardiovascular events (MACEs) has not been fully evaluated. METHODS: Patients diagnosed with ACS and receiving PCI were included. The primary outcome was MACEs. Cox proportional hazards regression and logistic regression were used to illustrate the relationship between clinical risk factors, biomarkers and MACEs. Survival models were developed based on significant factors and evaluated by the Concordance-index (C-index). RESULTS: The final study cohort was comprised of 650 patients (median age, 64 years; 474 males), including 98 (15%) with MACEs during a median follow-up period of 40 months. According to the cut-off value of DD and NLR, the patients were separated into four groups: high DD or nonhigh DD with high or nonhigh NLR. After adjusting for confounding variables, DD (adjusted hazard ratio [aHR]: 2.39, 95% confidence interval [CI]: 1.52-3.76) and NLR (aHR: 2.71, 95% CI: 1.78-4.11) were independently associated with long-term MACEs. Moreover, patients with both high DD and NLR had a significantly higher risk in MACEs when considering patients with nonhigh DD and NLR as reference (aHR: 6.19, 95% CI: 3.30-11.61). The area under curve increased and reached 0.70 in differentiating long-term MACEs when DD and NLR were combined, and survival models incorporating the two exhibited a stronger predictive power (C-index: 0.75). CONCLUSIONS: D-dimer (or combined with NLR) can be used to predict long-term MACEs in ACS patients undergoing PCI.

On Topological Properties for Benzenoid Planar Octahedron Networks.

Chemical descriptors are numeric numbers that capture the whole graph structure and comprise a basic chemical structure. As a topological descriptor, it correlates with certain physical aspects in addition to its chemical representation of underlying chemical substances. In the modelling and design of any chemical network, the graph is important. A number of chemical indices have been developed in theoretical chemistry, including the Wiener index, the Randic index, and many others. In this paper, we look at the benzenoid networks and calculate the exact topological indices based on the degrees of the end vertices.

A Paradigmatic Approach to Find the Valency-Based K-Banhatti and Redefined Zagreb Entropy for Niobium Oxide and a Metal-Organic Framework.

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal-organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network.

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

Study on the Rural Revitalization and Urban-Rural Integration Efficiency in Anhui Province Based on Game Cross-Efficiency DEA Model.

By taking the 16 cities in Anhui Province for evaluation, the main influencing factors and indicator system for integrated urban-rural development in the new era were explored, to build the BCC model, cross-efficiency model, and game cross-efficiency model of DEA. The above models were applied for empirical analysis and comparative study on the rural revitalization and urban-rural integration efficiency in Anhui Province, to summarize the conclusions efficiency and give suggestions based on the above calculations.

How Does Inequality Affect the Residents' Subjective Well-Being: Inequality of Opportunity and Inequality of Effort.

Based on the Chinese General Social Survey database (2010-2015), this article explores the relationship between income inequality and residents' subjective well-being from the perspective of inequality of opportunity and inequality of effort. We find that inequality of opportunity has a negative impact on subjective well-being in China, where inequality of effort has a positive impact. Our empirical results are robust for changing the inequality indicators. In the sub-sample studies, consistent conclusions are obtained in rural areas, whereas in urban areas only inequality of effort has a significant impact. The results of mechanism study show that inequality of opportunity decreases residents' sense of fairness, and inequality of effort increases residents' sense of fairness, thus affecting their subjective well-being. The results of this study provide a good response to the inconclusive research findings on the impact of income inequality on subjective well-being.

An Empirical Study on the Improvement of College Students' Employability Based on University Factors.

With the popularization of higher education and the promotion of college enrollment expansion, the number of college graduates increases sharply. At the same time, the continuous transformation and upgrading of the industrial structure put forward higher requirements on the employability of college students, which leads to the imbalance between supply and demand in the labor market. The key to dealing with employment difficulties lie in the improvement of college students' employability. Therefore, we make a regression analysis of 263 valid samples from universities in Anhui Province and extract the factors that influence the improvement of college students' employability in the process of talent cultivation in university. The result shows that there is a positive correlation between course setting, course teaching, club activities, and college students' employability, among which the course teaching and club activities are the most critical factors which may influence college students' employability. In addition, from the viewpoint of individual college students, the overall grades of college students and the time of participating in the internship are also closely related to their employability, i.e., college students with good overall grades and long internship time should also have stronger employability.

[Treatment of sciatica by lumbar nerve root canal injection under X-ray angiography].

OBJECTIVE: To investigate the short-term clinical effect of lumbar nerve root canal injection under X-ray angiography in the treatment of sciatica. METHODS: The clincal data of 78 patients with sciatica underwent lumbar nerve root canal injection under X-ray angiography from December 2017 to February 2020 was retrospectively analyzed. Including 31 males and 47 females, aged from 22 to 88 years old with a median of 65 years. There were 55 cases of lumbar disc herniation and 23 cases of lumbar spinal stenosis, the course of disease ranged from 1 to 8 weeks with a median of 3 weeks. There were 71 cases of single segment disc herniation or stenosis, including L3,4 of 5 cases, L4,5 of 61 cases, L5S1 of 5 cases, and 7 cases of multisegment herniation or stenosis. The pain visual analogue scale (VAS) was recorded and Macnab was used to evaluate the clinical effect. RESULTS: All patients completed standardized treatment without serious adverse reactions. VAS were (3.21±0.76) scores immediately after treatment, (2.89±0.33) scores 1 hour after treatment, (1.80±0.27) scores 6 hours after treatment, (1.10±0.20) scores 24 hours after treatment, (2.53±0.35) scores 1 week after treatment and (4.27±0.36) scores 1 month after treatment. There were significant differences in VAS between before treatment(7.83±0.56) and each time period after treatment(P<0.05). According to Macnab low back pain evaluation standard, 42 cases were effective, 34 cases were markedly effective and 2 cases were ineffective within 24 hours after treatment, with an effective rate of 97.4%;38 cases were effective, 25 cases were markedly effective, 15 cases were ineffective within one week after treatment, the effective rate was 80.0%;32 cases were effective, 22 cases were markedly effective, 24 cases were ineffective within one month after treatment, the effective rate was 69.2%. CONCLUSION: The short-term clinical effect of nerve root canal injection under X-ray radiography in the treatment of sciatica is good and it is an effective method to relieve sciatica.