Key Index Research Articles

Segmented sequence decomposition-Informer model for deformation of arch dams

Deformation serves as a key index to characterize the operational condition of dams. However, the prediction accuracy of deformation in dams remains limited due to the influence of multiple factors. Accordingly, this study innovatively combines the Informer with the segmented sequence decomposition and proposes a segmented sequence decomposition-Informer model (SD-Informer) for the deformation prediction of arch dams, which significantly improves the prediction accuracy and stability. The segmented sequence decomposition divides the predicted time series into annual segments and decomposes them in a segment-by-segment manner, thereby minimizing the reduction of prediction accuracy over long sequences and the boundary effects in decomposition. In addition, the Informer extracts macro- and micro-level information from deformation sequences using a multi-head attention mechanism, which significantly improves the prediction accuracy. LYX arch dam and XW arch dam, which have been in operation for more than 20 years, are taken as case studies. The results show that the performance of the SD-Informer surpasses that of wavelet neural networks, long short-term networks, and Informer, demonstrating that the SD-Informer is an accurate, robust, practical deformation prediction of arch dams for engineering applications.

Self-Supervised Transformers for Unsupervised SAR Complex Interference Detection Using Canny Edge Detector

As the electromagnetic environment becomes increasingly complex, a synthetic aperture radar (SAR) system with wideband active transmission and reception is vulnerable to interference from devices at the same frequency. SAR interference detection using the transform domain has become a research hotspot in recent years. However, existing transform domain interference detection methods exhibit unsatisfactory performance in complex interference environments. Moreover, most of them rely on label information, while existing publicly available interference datasets are limited. To solve these problems, this paper proposes an SAR unsupervised interference detection model that combines Canny edge detection with vision transformer (CEVIT). Using a time–frequency spectrogram as input, CEVIT realizes interference detection in complex interference environments with multi-interference and multiple types of interference by means of a feature extraction module and a detection head module. To validate the performance of the proposed model, experiments are conducted on airborne SAR interference simulation data and Sentinel-1 real interference data. The experimental results show that, compared with the other object detection models, CEVIT has the best interference detection performance in a complex interference environment, and the key evaluation indexes (e.g., Recall and F1-score) are improved by nearly 20%. The detection results on the real interfered echo data have a Recall that reaches 0.8722 and an F1-score that reaches 0.9115, which are much better than those of the compared methods, and the results also indicate that the proposed model achieves good detection performance with a fast detection speed in complex interference environments, which has certain practical application value in the interference detection problem of the SAR system.

Biomechanical Characterization of Abdominal Aortic Aneurysm: The Rupture Mechanism

In this work, a four-week-old male C57Bl/6 mouse model of abdominal aortic aneurysm (AAA) was developed to examine the AAA rupture mechanism. Immunofluorescence staining was adopted for quantifying the degradation of elastin, and Picrosirius Red staining was adopted for evaluating the density of collagen. Atomic force microscopy with two probe tip sizes of 5 µm and 20 nm was adopted for mechanical characterization of the AAA. The microstructure changes and stiffness changes in both AAA samples and controlled samples were inspected. The degradation of elastin, wall thickening, formation of micro vessels, and increased density of collagen were observed in the AAA samples. The AAA samples also exhibited fragmented texture from AFM scanning. The histogram of stiffness measurements of the AAA samples with a 20 nm tip demonstrated two unique peak frequencies of stiffness intervals (0–10 kPa and 40–50 kPa). The stiffer regions were correlated with the increased density of collagen, as shown in the immunofluorescence images. The softer regions, combined with the fragmented texture, could be the key index contributing to the initiation and propagation of AAA rupture. Overall, the AAA group showed a higher stiffness than the control group (50.77 ± 62.4 kPa vs. 40.6 ± 51.86 kPa). The findings from this work may help in explaining ruptures in small AAA (<5.5 mm), which account for ten percent of all AAA ruptures. Additionally, the observations in this study may help develop early detection methods and innovative treatments for AAA.

In Vivo Raman Spectroscopy Reveals Biochemical Composition of the Esophageal Tissue in Pediatric Eosinophilic Esophagitis.

Biochemical alterations in the esophagus of patients with eosinophilic esophagitis (EoE) are poorly understood. We used Raman spectroscopy through a pediatric endoscope to identify key Raman features reflective of the esophageal biochemical composition to differentiate between children with EoE from non-EoE controls and between children with active (aEoE) and inactive EoE (iEoE). Spectral measurements were obtained using a customized pediatric endoscope-compatible fiber-optic Raman probe in real time during an esophagogastroduodenoscopy. Chemometric analysis was performed to identify key Raman features associated with EoE. Pearson correlation analysis was used to assess relationship between the key Raman features and EoE activity indices. Their diagnostic utility was ascertained using the receiver operator characteristic curve analysis. Forty-three children were included in the study (EoE = 32 [74%] and non-EoE control = 11 [26%]; aEoE = 20 [63%] and iEoE = 12 [37%]). Raman intensities assigned to lipids, proteins, and glycogen:protein ratio accurately distinguished children with EoE from those without EoE and aEoE from iEoE. They significantly correlated with EoE activity indices. The Raman peak ratio for lipids had 90.6% sensitivity, 100% specificity, and an area under the curve of 0.95 to differentiate children with EoE from non-EoE controls. The glycogen:protein ratio had 70% sensitivity, 91.7% specificity, and an area under the curve of 0.75 to distinguish children with aEoE from iEoE. Real-time intraendoscopy Raman spectroscopy is an effective method for identifying spectral markers reflective of the esophageal biochemical composition in children with EoE. This technique may aid in the diagnosis and monitoring of EoE and help to elucidate EoE pathogenesis.

Effective flame detection and classification using deep feature of QuickDenseNet and Ensemble Score Voted SVM

Fire poses a significant threat to both lives and property, necessitating effective early detection measures. Despite challenges in identifying smoke and fire in their initial stages, we have devised a cost-efficient visual detection system. Early fire detection enhances its potential effectiveness. CCTV surveillance systems are now commonplace in developed countries, serving as tools for periodic monitoring of various locations. However, fluctuating ambient light conditions, camera angles, and seasonal variations can introduce data distortions, occlusions, and impact model accuracy. To address these issues, we’ve implemented a method combining deep learning networks and machine learning strategies for flame detection and direction classification. Our innovative QuickDenseNet extracts dense features from segmented flame video frames. We introduce the Ensemble Score Voted SVM (ESV-SVM), employing SVM as the primary learner and score voting as the auxiliary learner. Our approach is rigorously evaluated through simulations, measuring accuracy and various Key Performance Indices (KPIs), including Precision, F1-score, Recall, Correlation, Error, FPR, and Correlation Coefficients. Remarkably, our proposed method achieves an impressive precision rate of approximately 99.5%.

A hybrid approach to dynamic cognitive psychometrics

Dynamic cognitive psychometrics measures mental capacities based on the way behavior unfolds over time. It does so using models of psychological processes whose validity is grounded in research from experimental psychology and the neurosciences. However, these models can sometimes have undesirable measurement properties. We propose a “hybrid” modeling approach that achieves good measurement by blending process-based and descriptive components. We demonstrate the utility of this approach in the stop-signal paradigm, in which participants make a series of speeded choices, but occasionally are required to withhold their response when a “stop signal” occurs. The stop-signal paradigm is widely used to measure response inhibition based on a modeling framework that assumes a race between processes triggered by the choice and the stop stimuli. However, the key index of inhibition, the latency of the stop process (i.e., stop-signal reaction time), is not directly observable, and is poorly estimated when the choice and the stop runners are both modeled by psychologically realistic evidence-accumulation processes. We show that using a descriptive account of the stop process, while retaining a realistic account of the choice process, simultaneously enables good measurement of both stop-signal reaction time and the psychological factors that determine choice behavior. We show that this approach, when combined with hierarchical Bayesian estimation, is effective even in a complex choice task that requires participants to perform only a relatively modest number of test trials.

Innovative Method for Reliability Assessment of Power Systems: From Components Modeling to Key Indicators Evaluation

Power systems comprise different electrical, electronic, electromechanical and electrochemical components. Adequacy, security, resilience and reliability represent essential requirements for grids functioning mode. The evaluation of such aspects can constitute a delicate task in the presence of heterogeneous components. Focusing on reliability assessment, several Reliability Prediction Models are available. They are suitably applied according to the type of component under evaluation. The lack of homogeneity of these models forbids the comparison of performance and identification of unreliable systems and grid section. This paper aims to face the mentioned issue proposing a unique reliability assessment methodology able to characterize different equipment connected to radial/meshed/ring grids and subjected to different stressing and ageing factors. It is customized for electrical lines, transformers, circuit breakers, converters and renewables plants. Component and systemic key indices are calculated. Furthermore, a novel “load feeding reliability“ indicator is evaluated for providing information about the supply reliability of a specific load. This index is meaningful for the identification of unreliable grids, microgrids and systems. Such an approach can contribute to improve power systems design, planning and control. The proposed method is integrated in a software application implemented for grid reliability assessment. The obtained results are reported for an urban grid including an underground transportation area.

Volatility, Uncertainty, and Option Pricing

Amidst global and Chinese uncertainties, this paper delves into stock market volatility and option pricing within the current economic policy context. Focusing on China's stock market, it calculates returns and volatility changes in key indices, analyzing differences among indices and funds, along with temporal variations. Shared data characteristics and reflections on new market trends emerge. Employing methods such as the GARCH model, Black-Scholes formula, and Monte Carlo algorithm, it analyzes volatility and option pricing using extensive annual and monthly data. Visualizations showcase market change patterns. The study defines volatility as a measure of financial asset price fluctuation extent, reflecting asset risk. Higher volatility indicates pronounced price fluctuations and uncertainty, while lower volatility signifies smoother fluctuations and greater certainty. Merging data with volatility's significance, the study probes China's securities market uncertainty, investigating the link between option pricing and volatility. It concludes by identifying the connection between volatility, uncertainty, and option pricing, pointing to future research directions and challenges. Future work will track the latest market trends to enrich understanding.

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.

Theoretical characterization of the temperature‐dependent mode I fracture toughness of rocks

AbstractMode I fracture toughness, as a key index to reflect rocks' resistance to crack propagation and failure, which is significantly affected by temperature, plays an important role in accident prevention in underground engineering. In this work, a temperature‐damage‐dependent fracture surface energy model of rocks was developed first. Then, the theoretical characterization model of the temperature‐dependent mode I fracture toughness of rocks was proposed according to the relationship between the fracture surface energy and fracture toughness. All obtained experimental results are used for verification, and the predicted values are in good agreement with the experimental results. The temperature‐dependent mode I fracture toughness model of rocks established in this study not only provides a new method to predict the fracture toughness of rocks at different temperatures conveniently but also provides the theoretical basis to the research on stability analysis, monitoring, warning, and disaster control of underground rock engineering.

Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India

The spatio-temporal distribution of COVID-19 across India’s states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space–time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space–time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062–6.205) and deaths (RR: 2.477; 95% CI: 1.361–4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers’ rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space–time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.

Grassland biomass spatiotemporal patterns and response to climate change in eastern Inner Mongolia based on XGBoost model estimates

Aboveground biomass (AGB) is a key index that reflects grassland health and is of great importance for grassland conservation and sustainable utilization. Many previous studies on grassland AGB were based on the analysis of the whole study period, can’t reflect the change of grassland in the short term, which was not conducive to the implementation and revision of policies. And it is still a challenge to accurately describe the response of grassland AGB to climate factors. In this study, we used four machine learning algorithms (kNN, k-nearest neighbor; RF, Random Forest; SVR, Support Vactor Regression; and XGBoost, eXtreme Gradient Boosting) to construct the estimation model of grassland AGB in eastern Inner Mongolia based on grassland AGB measurement data, MODIS(MOD13Q1) data and environmental data (climate and topography). The average distribution of grassland AGB from 2003 to 2021 was calculated based on the optimal grassland AGB estimation model. The changes of grassland AGB from 2003 to 2021 and in every five-year period during the study series were comprehensively analyzed by the Theil–Sen Median trend analysis and the Mann–Kendall test. The response of grassland AGB to climatic factors was investigated using sensitivity analysis. The results showed that (1) the XGBoost model constructed by NDVI, longitude, latitude, temperature, precipitation, elevation, slope, and aspect (NXYTPESA) is the optimal grassland AGB model (R2 = 0.87, RMSE = 14.27 g/m2). (2) The 20-year average grassland AGB in Eastern Inner Mongolia showed heterogeneous, with higher values in the northeast and lower values in the southwest. (3) The inter-annual variation fluctuated substantially, and the overall trend was increasing. Compared with other time periods, most grassland AGBs were in an increasing trend during 2008–2012 (79 %) and 2018–2021 (84 %). (4) The grassland AGB in the study area was regulated by temperature and precipitation, and the influence of precipitation was greater than that of temperature. This study provides a new method for estimating grassland AGB and its changes and a scientific basis for the sustainable development of grassland ecosystems in eastern Inner Mongolia.

Real-world data of tenecteplase vs. alteplase in the treatment of acute ischemic stroke: a single-center analysis.

This retrospective observational cohort study aimed to evaluate whether tenecteplase's use for acute ischemic stroke (AIS) has time management advantages and clinical benefits. 144 AIS patients treated with alteplase and 120 with tenecteplase were included. We compared baseline clinical characteristics, key reperfusion therapy time indices [onset-to-treatment time (OTT), door-to-needle time (DNT), and door-to-puncture time (DPT)] and clinical outcomes (24-h post-thrombolysis NIHSS improvement, and intracranial hemorrhage incidence) between the groups using univariate analysis. We assessed hospital stay durations and used binary logistic regression to examine tenecteplase's association with DNT and DPT target times, NIHSS improvement, and intracranial hemorrhage. Baseline characteristics showed no significant differences except hyperlipidemia and atrial fibrillation. OTT (133 vs. 163.72, p = 0.001), DNT (36.5 vs. 50, p < 0.001) and DPT (117 vs. 193, p = 0.002) were significantly faster in the tenecteplase group. The rates of DNT ≤ 45 min (65.83% vs. 40.44%, p < 0.001) and DPT ≤ 120 min (59.09% vs. 13.79%, p = 0.001) were significantly higher in the tenecteplase group. Tenecteplase was an independent predictor of achieving target DNT (OR 2.951, 95% CI 1.732-5.030; p < 0.001) and DPT (OR 7.867, 95% CI 1.290-47.991; p = 0.025). Clinically, the proportion NIHSS improvement 24 h post-thrombolysis was higher in the tenecteplase group (64.17% vs. 50%, p = 0.024). No significant differences were observed in symptomatic intracranial hemorrhage (sICH) or any intracranial hemorrhage (ICH). Patients receiving tenecteplase had shorter hospital stays (6 vs. 8 days, p < 0.001). Tenecteplase was an independent predictor of NIHSS improvement at 24 h (OR 1.715, 95% CI 1.011-2.908; p = 0.045). There was no significant association between thrombolytic choice and sICH or any ICH. Tenecteplase significantly reduced DNT and DPT. It was associated with early neurological function improvement (at 24 h), without compromising safety compared to alteplase. The findings support tenecteplase's application in AIS.

Digital transformation and business intelligence for a SME: systems thinking action research using PrOH modelling

This paper discusses the digital transformation journey of a small and medium enterprise (SME) based in the UK. With the proposed digital transformation archetype, the paper highlights the improvement in various key performance indices (KPIs) for the case SME. The core KPIs and operational KPIs show improvement through the technology adoption as part of the digital manufacturing initiative. While embracing technology, such as Industry 4.0, it is important to highlight the importance of the change and other benefits of technological changes. The paper uses socio-technological system principles to achieve a successful transition. An action research approach and a specific soft system thinking methodology known as Process-Oriented Holonic (PrOH) modeling were used in this paper. A digital twin architecture is presented in the paper that showcases the use of integrated technologies for a digital manufacturing roadmap.

Value for Money to Measure Performance of Public Broadcasting Programs at Radio Republik Indonesia Semarang: Evidence From Indonesia

Abstract This study focuses on using value for money to measure the performance of the Public Broadcasting Programs at Radio Republik Indonesia (RRI) Semarang, the national radio station. Objective: This study aims to identify the Key Performance Indicator (KPI) of the Public Broadcasting Programs at RRI Semarang (LPP RRI) based on the value-for-money ratio from 2018 to 2022. Method: This study used a qualitative descriptive method. This study involved administration planners and implementers at LPP RRI Semarang as informants. Findings: Based on the concept of value for money, the Key Performance Indicators of the Public Broadcasting Program of LPP RRI Semarang showed that the budget used was economical, efficient, and effective. Implication Theory and Policy: LPP RRI Semarang needs to consider the results of this study for designing policies to improve their performance in using state budget (APBN) and transparency in budget use. Novelty: This study evaluates the Key Performance Index of LPP RRI Semarang to improve its public broadcasting performance. The study concerns the budget realization report to identify the budget management process for the public broadcasting program.

Exploring urban land surface temperature with geospatial and regression modelling techniques in Uttarakhand using SVM, OLS and GWR models

Given the climate change challenges, Uttarakhand has become crucial for examining land dynamics and regional climate interactions. This study employed a Support Vector Machine (SVM) for land use and land cover mapping for 2024, achieving 94% accuracy and a Kappa coefficient of 0.90, indicating robust mapping. Key land indices such as NDVI, NDWI, NDBI, NDSI, and NDBaI were calculated, along with Land Surface Temperature (LST) from Landsat 8 imagery. These indices were selected for their relevance in representing vegetation health (NDVI), measuring water content (NDWI), assessing urban areas (NDBI), identifying snow cover (NDSI), and highlighting the barren land (NDBaI), which all influence LST. Hotspot analysis with Getis-Ord Gi∗ revealed spatial distribution patterns of LST. Regression analysis showed significant relationships: a strong positive correlation between LST and NDBI (R2 = 0.78) and a substantial negative correlation between LST and NDSI (R2 = −0.80). The strong positive correlation highlights how urbanization contributes to rising surface temperatures, while the substantial negative correlation underscores the cooling effect of snow cover, which is particularly relevant as reduced snow cover could lead to higher LST in the context of climate change. These correlations offer deeper insights into how land cover changes can exacerbate or mitigate climate impacts in Uttarakhand. Two regression models were used for statistical modeling and spatial analysis: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). In OLS, the results reveal non-stationarity (p = 0.000) with an R2 value of 0.79 while GWR significantly enhanced performance, achieving an R2 value of 0.94. The improved performance of GWR (R2 = 0.94) compared to OLS (R2 = 0.79) can be attributed to GWR’s ability to account for spatial non-stationarity. This method allows for variations in relationships between LST and explanatory variables across different locations, effectively capturing local patterns that OLS may overlook. Spatial autocorrelation analysis, utilizing Moran’s I, exhibited a decrease from 0.606 (OLS) to 0.02 (GWR), This reduction indicates that GWR effectively captures spatial non-stationarity, minimizing residual autocorrelation by modeling local relationships between LST and its predictors that often remain in global models like OLS, thereby demonstrating its advantages in heterogeneous regions. The findings underscore the importance of employing GWR to better elucidate the connection between LST and its predictors, specifically in regions characterized by spatial non-stationarity, thereby offering insights crucial for informed decision-making amidst changing climatic conditions.

Therapeutic potential of virgin coconut oil in mitigating sodium benzoate- model of male infertility: Role of Nrf2/Hmox-1/NF-kB signaling pathway.

Male infertility is a major public health issue due to increased prevalence, so there is an urgent need for a therapeutic solution. The search for a natural dietary substance that could modulate redox balance and inflammation and protect testicular function is in demand. Virgin Coconut Oil (VCO) has found use in the treatment of diabetes, and cancer owing to the presence of polyphenols. However, there is a dearth of information on its effect on testicular toxicity. The present study investigated VCO as a possible treatment for testicular toxicity in the Sodium Benzoate (SB) model of male infertility by evaluating the oxidative and inflammatory status, circulating hormonal levels, and key sperm indices. Twenty adult male rats were randomly assigned to four groups of 5 rats each and were treated with normal saline, sodium benzoate, sodium benzoate+5% VCO, and sodium benzoate+15% VCO for 30 days respectively. Biochemical analysis of reproductive hormones was assessed. Sperm parameters assessed include sperm function tests and sperm kinematics. One-way analysis of variance (ANOVA) followed by post hoc Tukey tests was performed. 5% VCO reverts the deranged serum reproductive hormones caused by sodium benzoate. 5% VCO was more potent as an antioxidant and anti-inflammatory treatment than 15% VCO. However, both doses prevented SB's effect on the sperm function test and kinematics. VCO-supplemented diet can ameliorate SB-induced testicular toxicity by inhibiting its mechanisms of toxicity that are related to oxidative stress, apoptosis, and inflammation.

Big Data Analysis Methods for Higher Education Physical Education Teaching Interactive Platform and Its Teaching Effectiveness

Abstract The use of big data comprehensively serves physical education, so that students can clarify their learning situation and make reasonable changes in physical education teaching. In this paper, firstly, we do an analysis and investigation on the application of big data in physical education teaching in colleges and universities, apply the tree hierarchy algorithm to collect the key data indexes of physical education teaching classrooms, and use the SMDA aggregation algorithm to aggregate and analyze the collected key data. The weight of each index system of teaching evaluation is calculated using the hierarchical analysis evaluation method. The role and application of analytical methods in physical education teaching are discussed in the context of big data. This paper constructs a system for evaluating teaching effects, which includes 5 primary indicators and 20 secondary indicators. The evaluation results of the 10-factor index system in the teaching process are divided into five grades to obtain the distribution intervals of excellent, good, medium, qualified, and poor students’ learning performance. The results of data analysis of the attributes of this paper show that the percentages of Cluster l, Cluster 2, and Cluster 3 are 22%, 66%, and 12% respectively. The weights of teaching ability, method, content, attitude, and effect in the actual teaching process were 20.97, 24.29, 24.08, 3.89, and 6.31, respectively. This paper elaborates on the effects and roles of four aspects of the interactive platform Big Data application in physical education teaching and learning in the context of Big Data analytics methods. Ideas for the use of big data analysis methods in sports teaching in colleges and universities are presented.

Higher-order modal parameter estimation and verification of cross-laminated timber plates for structural-acoustic analyses

Cross-Laminated Timber (CLT) is a wood composite that is popular due to its favourable stiffness-to-mass-density ratio and environmental benefits, among other positive attributes. This paper presents the estimation of higher-order modal parameters and their use in the verification and validation of an Equivalent Single-Layer (ESL) CLT model. The eigenvectors, eigenfrequencies, and modal damping ratios of 20 out-of-plane vibration modes were experimentally determined. The experimentally determined eigenfrequencies and eigenmodes were correlated and compared to those derived from a numerical model. The modal damping ratios were compared to damping loss factors derived from the power injection method. A broadband frequency view is also considered, with the comparison of experimental and numerical forced response models. As part of the verification and validation process, a framework of key indices and respective criteria is suggested and discussed in this contribution. Over the course of the paper, an ESL CLT model which derives its properties from a layerwise basis is validated in detail against experimental measurements. The results are intended to be of relevance to both structural and acoustic domains.

Are EFI data valuable? Evidence from the 2022 FIFA World Cup group stage.

The 2022 World Cup used new Enhanced Football Intelligence (EFI) data, stoppage time calculation methods and substitution rules that were introduced by FIFA. The aim of this study is to explore the effectiveness of EFI in match analysis and to identify the key indicators that influence the match and provide a reference for coaches' tactical design and training. Data were derived from the FIFA website, including EFI data for 48 matches at the group stage of the Qatar World Cup. A total of 46 indicators were used for analysis and the average values of the corresponding indicators for the different competition results were used in the analysis to identify the key index that determines the outcome of the competition. Apart from scoring more goals and having more assists, winning teams had significantly more attempts on target than drawing and losing teams (p < 0.05); Winning teams had significantly more attempts inside the penalty area, completed defensive line breaks and receptions behind the defensive line than losing teams (p < 0.05). There is no difference in possession between matches with different results (p > 0.05). Goals were significantly correlated with completed defensive line breaks and receptions behind the defensive line (r = 0.27-0.30, p < 0.01). Attempts on target was significantly positively correlated with receptions, final third entries and line breaks (r = 0.31-0.67, p < 0.01) and negatively correlated with defensive pressures applied (r = -0.35, p < 0.01). The efficiency of the offense is more important. Teams need to have more receptions, final third entries and line breaks to get more shots on target rather than possession. This study may help coaches to interpret the game from a multi-dimensional perspective and coaches can use EFI to help their teams improve their match performance.