Influence Ranking Research Articles

Ester-Modified Sodium Silicate Grout Material for Moraine Stabilization: Synthesis and Freeze-Thaw Resistance.

To achieve effective consolidation of fine particles in moraine and enhance the freeze-thaw resistance of the consolidated body, this study developed a novel grouting material using sodium silicate, lipid-based curing agents, and acidic catalysts. The gelation time and rheological properties of this material were tested. The freeze-thaw resistance was studied through changes in uniaxial compressive strength (UCS) after freeze-thaw cycles, while the consolidation mechanism was analyzed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The experimental results indicate that the material's gelation time can be controlled between 30s and 1600s, with an initial viscosity ranging from 5.9 to 9.8 mPa·s. Predictive models for these two indicators were established, and variance analysis revealed the influence ranking for gelation time: phosphoric acid dosage had the greatest effect, followed by EGDA content, with the Baume degree of sodium silicate having the least effect. The initial viscosity positively correlated with the Baume degree of sodium silicate and exhibited exponential growth over time. EGDA addition enhanced UCS by over 450%, reaching 1.2 MPa. During freeze-thaw cycles, strength degradation of the consolidated body was reduced by 10% to 30%. Microstructural tests showed that EGDA promotes silica gel formation and creates a network structure with unreacted sodium silicate, forming a dense consolidated body with moraine fine particles, thereby enhancing freeze-thaw resistance. These findings provide design references and theoretical support for moraine grouting in cold regions.

Buckling analysis and parametric optimization of the metal-composite shell with voids under hydraulic pressure combined with sensitivity analysis

Structural design parameters always impact the buckling properties of the composite shells due to the differences in the anisotropy of the materials and the varying enhancement capabilities of each component. This paper aims to analyze the parametric effect on the buckling load and optimize the design parameters of metal-composite shells with voids under hydraulic pressure. Firstly, the theoretical model of buckling load is established based on the microscopic model of hybrid materials and the Galerkin method. Then, four design parameters including fiber volume content, winding angles, and CNTs volume content are selected to discuss the parametric effect on the critical buckling load. Further, the global sensitivity analysis is conducted to determine the influence rank, and the GA-PSO algorithm is used to confirm the optimal parametric combination. The results imply that the fiber volume content and internal and external helical symmetrical angles significantly affect the buckling load. Besides, The winding layers of internal and external helical symmetrical angles enhance the hydraulic resistance of the anti-symmetric structure. The middle helical layer can be influenced by the outer layer, leading to directional changes in the critical buckling load at an angle of around 50°.

3D FEM forming simulation optimization and experiment on bevel thread-forming screw

Abstract This study adopts 3 forming passes and 1 thread rolling process as the multi-pass schedule of bevel thread-forming screw. The finite element simulation software is used to simulate the plastic behaviour of forming and thread rolling processes. The geometry shapes, the part dimensions of each pass, the effective stress, the effective strain, the forming force and the die stress of each pass can be reached via FEM simulation. Moreover, especially Taguchi method for the maximum force pass is conducted to optimize the forming processes. Considering 4 control factors such as A: head arc radius (rH), B: flange thickness (t), C: corner radius (r), and D: frictional factor(m) and 3 levels to establish the orthogonal array table for optimum forming force, the influence rank and the optimal parameters combination are obtained to carry out the realistic experiment for confirming the feasibility of FEM simulation optimization. In a word, the influence rank of control factors: flange thickness (t) > head arc radius (rH) > corner radius (r) > frictional factor (m), and the optimal parameters combination is A3B3C3D1 (rH=42 mm, t=1.3 mm, r=0.7 mm, and m=0.12).

High-risk nuclide screening and parameter sensitivity analysis based on numerical simulation and machine learning

During nuclear accidents, large quantities of radionuclides will be released into the environment, posing serious health hazards to local residents. The screening of high-risk nuclides is critical for the development of subsequent nuclear emergency response measures. In order to overcome the shortcomings of traditional screening methods, a machine learning method was proposed to screen high-risk nuclides and predict their contamination to groundwater more effectively. The performances of Support Vector Machine (SVM), Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms were compared, and sensitivity analyses of the initial leakage concentration ratio (C0/Cp), distribution coefficient (Kd) and decay coefficient (λ) on the model outputs were performed. Results showed that RF classification model achieved the highest prediction accuracy for screening high-risk nuclides. The contribution of the input parameters ranked as Kd > λ > C0/Cp. BPNN regression model was found to be the best for predicting when high-risk nuclides would pollute groundwater. The output was negatively correlated with C0/Cp and positively correlated with Kd and λ, with the parameter influence ranking as Kd > C0/Cp > λ. The contribution of Kd mainly came from itself, and the contribution of C0/Cp and λ mainly due to their interaction with other parameters.

Analyzing spatial technology trends in urban heritage preservation: A bibliometrics study

This study addresses the notable absence of comprehensive syntheses and bibliometric analyses concerning the utilization of spatial technology within urban heritage preservation contexts. The research aims to elucidate the current status, methodologies, advancements, and future trajectories of spatial technology applications in urban heritage preservation. Employing a systematic literature review and bibliometric analysis, the study examines pertinent 157 literature through Scopus, categorizes research applications, analyzes bibliometric data, evaluates influence rankings across multiple dimensions, identifies historical trends, and outlines prospective directions. The study findings unveil the evolving characteristics of spatial technology applications. Firstly, it reveals a gradual development trend, with spatial technologies experiencing an annual growth rate of 7.7% between 2010 and 2020. Nevertheless, a significant disparity persists, with more than 63.2% of countries still fully integrating spatial technology on-site at heritage sites. Furthermore, the study highlights varied collaboration levels between research institutions, with limited international cooperation and exchange, particularly evident between developed and developing nations. Lastly, the increasing demand for interdisciplinary research signals a shift towards fostering innovative approaches and solutions in heritage site preservation and management.

Global climate governance inequality unveiled through dynamic influence assessment

This study defines the concept of major and minor countries and establishes a multidimensional and dynamic assessment system to evaluate the influence of countries participating in global climate governance. We systematically calculate 45 indicators for 53 countries across 9 global climate negotiation interest groups over the seven years. Our findings indicate a significant difference in influence between major and minor countries. This gap is particularly significant regarding losses incurred from climate disasters, with a more than fourfold difference between the top 25% and the bottom 25% in the influence rankings. The influence-climate disaster Index Gini coefficient further indicates that climate governance is characterized by extreme inequality. We argue that this phenomenon arises because those with more decisive influence in global climate governance do not bear losses from climate disasters commensurate with their influence. Therefore, their willingness to participate in climate governance is lower than those who suffer greater consequences.

Estimating user influence in social networks under independent cascade model

The rapid increase in social applications emphasizes the importance of estimating user influence. Heuristic methods like degree and betweenness centralities usually differ from the actual propagation process and yield unsatisfactory results. Traditional methods like Monte Carlo simulation are time-consuming. We modify the duplicate forwarding model to analyze the propagation process, which is proved to be close to the independent cascade model. We calculate the influence of a given source on a target. This approach allows for relatively accurate user influence estimation. Although this method is more efficient than traditional methods, it still requires traversing all users. Therefore, we introduce a virtual user who is connected to all users. By estimating the influence of any user on the virtual user, we can approximate the user influence efficiently. Experiments on real-world networks demonstrate that our method achieves not only better accuracy in user influence ranking but also lower computational cost.

Structural integrity assessment of CANDU pressure tubes using Sobol indices for global sensitivity analysis

CANDU11CANDU is a registered trademark of Atomic Energy of Canada Limited. reactors are a channel-type design where up to 480 zirconium alloy pressure tubes (PTs) act as the reactor pressure boundary. Fitness-for-service standards such as CSA N285.8 (CSA Group, 2015) have been established to prescribe the requirements of pressure tube evaluations to maintain their integrity throughout their lifetime. Probabilistic leak-before-break (LBB) assessments can be used in evaluations to demonstrate that the likelihood of a flaw growing past the stability point while undetected (or before a safe shutdown can be achieved) is below regulatory limits. The assessments typically require a ranking of model parameters based on their influence. The ranking process traditionally uses expert judgment and local sensitivity analysis methods. There has been recent interest in implementing global methods for LBB assessments as they can manage large numbers of variables and extensive sampling spaces. However, their adoption is hindered by the computational cost of the models involved.The current study proposes a method for performing global sensitivity parameter rankings during probabilistic LBB assessments. The study is the continuation of the CANDU nuclear power plant (NPP) pressure tube (PT) case study in El Bouzidi et al., (2024) where a probabilistic approach was implemented in RAVEN (Rabiti and Alfonsi, 2019) to estimate whether the probability of unstable crack growth in PTs is within acceptable limits. This follow-up work proposes surrogate modeling using a high-dimensional model representation to derive Sobol indices and establish a parameter influence ranking. The methodology is demonstrated in a Monte Carlo simulation campaign of a pressure tube inlet rolled joint. The implemented approach successfully identified and quantified critical parameters of the model while efficiently ranking and quantifying the interactions between input variables.

Efficacy and Safety of Orally Administered East Asian Herbal Medicine Combined with Narrowband Ultraviolet B against Psoriasis: A Bayesian Network Meta-Analysis and Network Analysis.

Psoriasis is a chronic, immune-mediated inflammatory skin disease with many complications and a poor prognosis that imposes a significant burden on individuals and society. Narrowband ultraviolet B (NB-UVB) represents a cost-effective non-drug therapeutic intervention for psoriasis. East Asian herbal medicine (EAHM) is currently being investigated for its potential as a safe and effective psoriasis treatment. Consequently, it has the potential to be employed as a combination therapy with NB-UVB. The objective was to ascertain the efficacy and safety of the EAHM with NB-UVB combination therapy and to identify important drugs for further research. In this study, randomized controlled trials (RCTs) were retrieved from ten databases in Korea, China, and Japan. All statistical analyses were conducted using R software version 4.3.0. The primary outcomes were the Psoriasis Area and Severity Index (PASI) and the incidence rate of adverse events (AEs), while the secondary outcomes were hematologic markers and the Dermatology Life Quality Index (DLQI), which reflect the immune-mediated inflammatory pathology of psoriasis. The analysis of 40 RCTs, including 3521 participants, demonstrated that EAHM with NB-UVB combination therapy exhibited a statistically significant superiority over NB-UVB monotherapy with respect to primary and secondary outcomes. The Bayesian network meta-analysis revealed that Investigator Presciption 3 and Ziyin Liangxue Decoction exhibited a consistent relative advantage with respect to each PASI-based efficacy metric. The network analysis estimated the potential influence ranking for all individual herbs according to PageRank centrality. The findings of this study suggest that EAHMs co-administered with NB-UVB may provide additional efficacy and safety-related benefits for patients with psoriasis. However, the quality of evidence is still low, and further high-quality trials are needed to reach more definitive conclusions.

Empirically measuring online social influence

Social influence pervades our everyday lives and lays the foundation for complex social phenomena, such as the spread of misinformation and the polarization of communities. A disconnect appears between psychology approaches, generally performed and tested in controlled lab experiments, and quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues; the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work bridges this gap by introducing a human-in-the-loop active learning method that empirically quantifies social influence by crowdsourcing pairwise influence comparisons. We develop simulation and fitting tools, allowing us to estimate the required budget based on the design features and the worker’s decision accuracy. We perform a series of pilot studies to quantify the impact of design features on worker accuracy. We deploy our method to estimate the influence ranking of 500 X/Twitter users. We validate our measure by showing that the obtained empirical influence is tightly linked with agency and communion, the Big Two of social cognition, with agency being the most important dimension for influence formation.

Evaluation and Comparison of Academic Impact and Disruptive Innovation Level of Medical Journals: Bibliometric Analysis and Disruptive Evaluation.

As an important platform for researchers to present their academic findings, medical journals have a close relationship between their evaluation orientation and the value orientation of their published research results. However, the differences between the academic impact and level of disruptive innovation of medical journals have not been examined by any study yet. This study aims to compare the relationships and differences between the academic impact, disruptive innovation levels, and peer review results of medical journals and published research papers. We also analyzed the similarities and differences in the impact evaluations, disruptive innovations, and peer reviews for different types of medical research papers and the underlying reasons. The general and internal medicine Science Citation Index Expanded (SCIE) journals in 2018 were chosen as the study object to explore the differences in the academic impact and level of disruptive innovation of medical journals based on the OpenCitations Index of PubMed open PMID-to-PMID citations (POCI) and H1Connect databases, respectively, and we compared them with the results of peer review. First, the correlation coefficients of the Journal Disruption Index (JDI) with the Journal Cumulative Citation for 5 years (JCC5), Journal Impact Factor (JIF), and Journal Citation Indicator (JCI) were 0.677, 0.585, and 0.621, respectively. The correlation coefficient of the absolute disruption index (Dz) with the Cumulative Citation for 5 years (CC5) was 0.635. However, the average difference in the disruptive innovation and academic influence rankings of journals reached 20 places (about 17.5%). The average difference in the disruptive innovation and influence rankings of research papers reached about 2700 places (about 17.7%). The differences reflect the essential difference between the two evaluation systems. Second, the top 7 journals selected based on JDI, JCC5, JIF, and JCI were the same, and all of them were H-journals. Although 8 (8/15, 53%), 96 (96/150, 64%), and 880 (880/1500, 58.67%) of the top 0.1%, top 1%, and top 10% papers selected based on Dz and CC5, respectively, were the same. Third, research papers with the "changes clinical practice" tag showed only moderate innovation (4.96) and impact (241.67) levels but had high levels of peer-reviewed recognition (6.00) and attention (2.83). The results of the study show that research evaluation based on innovative indicators is detached from the traditional impact evaluation system. The 3 evaluation systems (impact evaluation, disruptive innovation evaluation, and peer review) only have high consistency for authoritative journals and top papers. Neither a single impact indicator nor an innovative indicator can directly reflect the impact of medical research for clinical practice. How to establish an integrated, comprehensive, scientific, and reasonable journal evaluation system to improve the existing evaluation system of medical journals still needs further research.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study

BackgroundEarly recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. MethodsThe prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. ResultsIn total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. ConclusionThe model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

Exploring academic influence of algorithms by co-occurrence network based on full-text of academic papers

PurposeIn the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.Design/methodology/approachWe use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.FindingsThe results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.Originality/valueTo the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Construction and validation of a new media marketing influence assessment model

Abstract In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.

Analysis of energy loss in the helical hedge flow channel of fruit tree root emitter

This paper proposes the design of a helical hedge flow channel with a high energy loss, which shows promising potential for application in fruit tree root emitters. The aim is to investigate the relationship between the energy loss form in the channel and its influencing factors. The hydraulic performance testing method is employed to analyze the factors that affect energy loss. The main influencing factors are determined using the response surface methodology (RSM) for experimental design. Based on the obtained experimental results, the energy loss form and influencing factors are analyzed, and a prediction model for the energy loss coefficient (ξ) is established. The results indicate that the ξ exhibits a decreasing trend with an increase in the diversion angle (α), a trend of first increasing and then decreasing with an increase in the channel width (b), and an increasing trend with an increase in the number of channel units (n). The effects of the straight section length (l1), convergence section length (l2), and bend radius (r) on the ξ can be neglected. The ranking of the geometric parameters' influence on the ξ is as follows: n > b > α > l1 > r > l2. The experimental results reveal that the ξ ranges from 19.2 to 234.3. Furthermore, the head loss along the flow channel constitutes merely 0.06–0.47% of the local head loss, The main form of energy loss in the spiral counter flow channel is local head loss. There is a significant linear relationship between α, b, n and the ξ, The established prediction model (R2 = 0.9691) can accurately predict the ξ of the channel.

An Assessment of the Impact of the COVID-19 Pandemic on Consumer Behavior Using the Analytic Hierarchy Process Model

Consumer behavior is dynamic and can shift rapidly due to various factors. The COVID-19 pandemic introduced unprecedented market disruptions, prompting unique consumer reactions. Our foundational study dissected factors affecting consumer habits, laying the groundwork for a focused analysis of how individual consumption was impacted during the pandemic. Significantly, psychological influences emerged as a key determinant of consumer behavior in our prior findings, leading us to centralize the pandemic’s effect in our recent research. We undertook this analysis using data from 559 participants from the Republic of Croatia. Utilizing the Analytic Hierarchy Process (AHP), typically applied in analyses of decision making in businesses, we ranked the influences on consumer purchasing patterns amidst the pandemic. Our findings underscored the profound influence of these factors on consumer behavior and the resulting implications for sustainable development. This highlights the urgency for businesses to adopt flexible strategies that also support overarching sustainability objectives. The objective of the study is to analyze the impact of the pandemic on individual consumption and to understand the key psychological factors influencing consumer behavior. The main contribution of this work is the identification and ranking of pandemic influences using the AHP model, as well as understanding the implications of these behavioral changes for future planning.

Effects of Terrain near Taiwan Island on Typhoons with Different Tracks and Typhoon Waves

The terrain, such as Taiwan Island, have been shown to have complex effects on typhoons and the associated typhoon waves. Terrain effects change with typhoon tracks. In this study, three types of typhoon tracks (northern, middle and southern) were defined according to the relationship between the typhoon tracks and Taiwan Island. Typhoons on these three tracks and typhoon waves were simulated using the Weather Research and Forecasting–Simulating Waves Nearshore model. In each type of typhoon, a control case without the Taiwan topography was simulated to compare with real cases. The results showed that typhoons on different tracks were affected by the terrain of Taiwan Island in different ways. Taiwan Island had weakening, decelerating and deflective effects on typhoons. The ranking for the weakening effect was middle track (81%) > southern track (69%) > northern track (3%). The decelerating effect was 7% in the northern track and 25% in the southern track. The deflective effect of the terrain makes typhoons on the northern (southern) track deflect toward the south (north). When a typhoon on a middle track passed over Taiwan Island, a new center of low pressure would replace the former center and make the track discontinuous. In addition, the influence of typhoons on regions near Taiwan Island changed with the typhoon’s tracks. The influence ranking of typhoons in Taiwan Island was the middle track > southern track > northern track, which was consistent with that of typhoons in the Taiwan Strait and opposite to that of typhoons in Fujian Province. The influence ranking of typhoons on the Taiwan Strait was the opposite of the typhoon intensity ranking, which suggests that the intensities of wind and waves in the strait were more related to typhoon tracks than typhoon intensity. The variations in the significant wave height were similar to those of the wind speed, but there was a time lag (2 h) between them due to the wave growth process and swells. In addition, the significant wave height distribution sometimes differed from the wind speed distribution under the influence of swells and terrain.

Identifying influential nodes through an improved k-shell iteration factor model

Measuring the importance of nodes is a rapidly developing field in network science, with centrality measures playing a critical role in various applications, including epidemic control, influence maximization, and network integration. The commonly used coreness calculated by the k-core decomposition process for identifying influential spreaders has limitations, which fail to consider the topological position of nodes and lead to low node discrimination. To address these issues, we propose a novel approach called the structural iteration factor ranking method to identify the influential nodes in complex networks, which considers the remaining structural properties when implementing the k-core decomposition. Our method considers both local and global hierarchy information and selects the neighbor information attribute to avoid problems with overlapping neighbor information propagation caused by the significant clustering coefficient of networks. We consider ground-truth influence ranking based on the Susceptible–Infected–Recovered model, on which the performance of the proposed ranking method is verified. Our proposed method outperforms previous methods in calculating monotonicity, similarity, and accuracy. It provides a more comprehensive evaluation of nodes and enables more in-depth identification of critical nodes. These findings imply that considering the remaining structural properties when implementing the k-core decomposition dominates the spreading strength of nodes and catches more details for ranking the node influence in complex networks.

Integrated data mining for prediction of specific capacitance of porous carbon materials for flexible energy storage devices

Porous carbon materials have gained wide attention owing to their remarkable electrical conductivity and large surface area. Predicting the specific capacitance based on these materials is a crucial step towards designing and manufacturing flexible energy storage devices. To achieve this objective, we employed integrated data mining to construct a database comprising 18 input variables (such as precursor material, activation type, reference electrode, electrolyte, annealing temperature, annealing time, activation temperature, ramp rate of activation temperature, activation time, specific surface area of the pore, total volume of the pore, mesoporous volume, micropore volume, current density, activator material ratio, mass fraction, average pore size, nitrogen content) and one output variable (specific capacitance). We selected characteristic variables to build a porous carbon electrode material database and imported it into a layer fusion model and single-model such as XGBoost, LightGBM, and linear regression to calculate the influence ranking of characteristic variables. The layer fusion model was compared with the single-model by importing a blind dataset into the training, and the prediction correlation coefficient R2 of the layer fusion model was found to be 0.981, indicating superior performance compared to the single-model. This layer fusion model enables simulation predictions for the selection and preparation of porous carbon electrode materials, guides experimental design, optimizes experimental schemes, and targets experimental parameters by referencing the influence of characteristic variables to obtain optimal experimental results.