Weight Method Research Articles

Research on Statistical Assessment Methods of Stock Investment Value-Taking Education Industry as Example

Abstract: This paper selects the financial statement data of 18 listed companies in the field of education and selects 9 financial indicators from the four aspects of solvency, profitability, development ability and operation ability that can reflect the comprehensive strength of listed companies. Finally, the stocks of 18 listed companies were divided into three categories: high-value stocks, potential stocks and low-value stocks according to the comprehensive strength of the companies. The results show that: (1) From the comparison between the rankings of the selected 18 stocks combined with the traditional methods by combining principal component analysis and entropy weight method, it can be seen that for the comprehensive multi-index evaluation analysis in the past five years, the ranking agreement between the two methods can reach 80% or more. Due to the unstable fluctuations of the stock market, the consistency between the analysis results and investment recommendations obtained in this paper and the actual investment situation of the stock market can also exceed 60%. (2) From the results of cluster analysis, Chuanzhi Education, Entrepreneurship Dark Horse, Guoxin Culture, China Hi-Tech and Action Education are all high-value stocks in the education category, while Meijim, Qinshang Co., Ltd., and Kaiwen Education are potential education stocks.

Bidirectional causal relationship between glucose-lipid metabolism, obesity indicators, and myocardial infarction: a bidirectional Mendelian randomization analysis study

Objective: To explore the causal association of glucose-lipid metabolism and obesity indicators with myocardial infarction by a two-sample Mendelian randomization analysis. Methods: Single nucleotide polymorphisms (SNPs) related to phenotypes were obtained from genome-wide association study databases. The body mass index (BMI) and glycated hemoglobin dataset includes 99 998 samples and 8 126 035 SNPs; the waist-to-hip ratio dataset includes 224 459 samples and 2 562 516 SNPs; the waist circumference and hip circumference dataset includes 462 166 samples and 9 851 867 SNPs; the fasting glucose dataset includes approximately 12 million SNPs; the low-density lipoprotein cholesterol (LDL-C) dataset includes 201 678 samples and 12 321 875 SNPs; the high-density lipoprotein cholesterol (HDL-C), and triglycerides dataset includes 156 109 samples and 15 784 414 SNPs; and the body fat percentage, whole-body fat mass, trunk fat percentage, and trunk fat mass dataset includes 454 588 samples and 9 851 867 SNPs. This study primarily used inverse-variance weighted method to analyze the associations between various exposure factors and outcomes. Heterogeneity among SNPs was assessed using Cochran's Q test, and horizontal pleiotropy of SNPs was examined using the MR-Egger method. Additionally, a multivariable MR approach was used to adjust for BMI, further validating associations between exposure factors and the risk of myocardial infarction. Results: Higher BMI (OR=1.070, 95%CI: 1.041-1.100), waist-to-hip ratio (OR=1.366, 95%CI: 1.113-1.677), LDL-C (OR=1.638, 95%CI: 1.488-1.803), triglycerides (OR=1.445, 95%CI: 1.300-1.606), waist circumference (OR=1.841, 95%CI: 1.650-2.055), hip circumference (OR=1.247, 95%CI: 1.132-1.372), body fat percentage (OR=1.795, 95%CI: 1.568-2.055), whole-body fat mass (OR=1.519, 95%CI: 1.381-1.670), trunk fat percentage (OR=1.538, 95%CI: 1.374-1.723), and trunk fat mass (OR=1.421, 95%CI: 1.294-1.561), as well as lower HDL-C (OR=0.799, 95%CI: 0.729-0.875), have causal effects on myocardial infarction (all P<0.05). After adjusting for BMI, hip circumference, trunk fat percentage, and trunk fat mass were no longer associated with myocardial infarction. However, waist-to-hip ratio (OR=1.457, 95%CI: 1.132-1.877), fasting glucose (OR=1.191, 95%CI: 1.024-1.383), glycated hemoglobin (OR=1.129, 95%CI: 1.034-1.233), LDL-C (OR=1.592, 95%CI: 1.314-1.929), triglycerides (OR=1.410, 95%CI: 1.279-1.553), waist circumference (OR=1.922, 95%CI: 1.448-2.551), body fat percentage (OR=1.421, 95%CI: 1.072-1.884), and whole-body fat mass (OR=1.295, 95%CI: 1.031-1.626) remained positively associated with myocardial infarction, while HDL-C (OR=0.809, 95%CI: 0.729-0.897) remained negatively associated. Conclusions: Abdominal obesity and dysregulation of glucose-lipid metabolism are risk factors for myocardial infarction. Screening for glucose-lipid metabolism (fasting glucose, HDL-C, LDL-C, triglycerides) and obesity-related indicators (waist circumference, waist-to-hip ratio, body fat percentage, and whole-body fat mass) is of great importance for the primary prevention of myocardial infarction.

A paradoxical perspective on the time dimension of corporate sustainability: Having both finite and infinite mindsets to play the game

Adopting a paradoxical perspective, we propose that companies need to have both finite and infinite mindsets to play the infinite game of corporate sustainability (CS). The current development of the paradoxical approach to CS focuses primarily on tension management, with limited attention directed to the time dimension and insufficient empirical evidence on how tensions between the present and the future should be dealt with. To bridge this gap, we empirically explore the effect of CS practices, driven by both finite and infinite mindsets, on CS performance. Data collected from a large-scale survey of Chinese automotive manufacturers were analyzed using the Entropy Weight Method (EWM) and Ordinary Least Squares (OLS) regressions. Findings suggest that a time-based paradoxical perspective on CS exists in firms, and they have realized short- and long-term benefits from implementing different types of CS practices. Our findings complement the CS literature on tension management and encourage businesses and policymakers to take a paradoxical approach to CS processes and outcomes.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

Analyzing Potential Non-Ignorable Selection Bias in an Off-Wave Mail Survey Implemented in a Long-Standing Panel Study

Abstract Typical design-based methods for weighting probability samples rely on several assumptions, including the random selection of sampled units according to known probabilities of selection and ignorable unit nonresponse. If any of these assumptions are not met, weighting methods that account for the probabilities of selection, nonresponse, and calibration may not fully account for the potential selection bias in a given sample, which could produce misleading population estimates. This analysis investigates possible selection bias in the 2019 Health Survey Mailer (HSM), a sub-study of the longitudinal Health and Retirement Study (HRS). The primary HRS data collection has occurred in “even” years since 1992, but additional survey data collections take place in the “off-wave” odd years via mailed invitations sent to selected participants. While the HSM achieved a high response rate (83 percent), the assumption of ignorable probability-based selection of HRS panel members may not hold due to the eligibility criteria that were imposed. To investigate this possible non-ignorable selection bias, our analysis utilizes a novel analysis method for estimating measures of unadjusted bias for proportions (MUBP), introduced by Andridge et al. in 2019. This method incorporates aggregate information from the larger HRS target population, including means, variances, and covariances for key covariates related to the HSM variables, to inform estimates of proportions. We explore potential non-ignorable selection bias by comparing proportions calculated from the HSM under three conditions: ignoring HRS weights, weighting based on the usual design-based approach for HRS “off-wave” mail surveys, and using the MUBP adjustment. We find examples of differences between the weighted and MUBP-adjusted estimates in four out of ten outcomes we analyzed. However, these differences are modest, and while this result gives some evidence of non-ignorable selection bias, typical design-based weighting methods are sufficient for correcting for it and their use is appropriate in this case.

The Impact of Digitalization on Agricultural Green Development: Evidence from China’s Provinces

Agricultural green development is crucial for achieving the United Nations 2030 Sustainable Development Goals, with the digital technology revolution acting as a catalyst for both China’s green agricultural transformation and global sustainable development efforts. This study utilizes panel data from 30 Chinese provinces (including autonomous regions and municipalities) from 2012 to 2022 to assess the digitalization level and agricultural green development through a combined entropy weight and TOPSIS method. It also investigates the spatial agglomeration of agricultural green development using Moran’s I index and empirically evaluates the impact of digitalization on agricultural green development through OLS and spatial Durbin models. The findings reveal that, while China’s agricultural green development has progressed slowly yet steadily during the study period, it demonstrates significant spatial agglomeration, driven primarily by agricultural production efficiency and resource recycling. Notably, a non-linear U-shaped relationship exists between digitalization and agricultural green development, suggesting that digitalization fosters agricultural green development only after surpassing a certain threshold. Additionally, digitalization has spatial spillover effects: advancements in neighboring provinces correlate with local agricultural green development in a U-shaped manner, with an initial “siphon effect” followed by a “trickle-down effect.” These insights inform policy recommendations aimed at optimizing the use of digital technology to facilitate green agricultural transformation, offering valuable guidance for policymakers.

Causal relationship between circulating inflammatory proteins and risk of different types of encephalitis: A two-sample Mendelian randomization study

BackgroundCytokines are potent molecules of the immune response. They act at the site of inflammation and circulate in the bloodstream. However, there are few studies on encephalitis and circulating inflammatory proteins. MethodsIn this study, Mendelian randomization (MR) was used to explore the potential causal effect of 91 circulating inflammatory proteins on 3 different types of encephalitis. Causal effects were examined using Steiger, MR-Egger, weighted median, and inverse variance weighting (IVW) methods. IVW methods were primarily used for results interpretation. In addition, sensitivity analyses were performed, including assessment of heterogeneity, horizontal pleiotropy, and Leave-one-out techniques. ResultsWe subjected 91 circulating inflammatory proteins to MR analysis of causality with each of the three types of encephalitis. The results suggested that the inflammatory factors with a potential causal relationship with viral encephalitis were caspase 8, C-X-C motif chemokine 6, interleukin-10, interleukin-15 receptor subunit alpha, interleukin-7, and TNF-beta. Inflammatory factors potentially causally associated with acute disseminated encephalomyelitis are beta-nerve growth factor, cystatin D, interleukin-7,Latency-associated peptide transforming growth factor beta 1,and neurotrophin-3.Inflammatory factors potentially causally associated with autoimmune encephalitis are C–C motif chemokine 25, hepatocyte growth factor, latency-associated peptide transforming growth factor beta 1, programmed cell death 1 ligand 1, sulfotransferase 1A1, and tumor necrosis factor. ConclusionThis finding identifies potential causal effects of certain circulating inflammatory factors on susceptibility to three types of encephalitis. It also suggests the therapeutic potential of modulating the levels of these cytokines. A basis for further research is provided.

Genetic insights into the causal linkage between systemic inflammatory regulators and frailty

ObjectivesPrevious studies have suggested the associations between systemic inflammation and the risk of frailty, but causal relationships between them remain not well established. We conducted a bi-directional Mendelian randomization (MR) analysis to investigate the causal links between systemic inflammatory regulators and frailty. MethodsGenetic variants associated with systemic inflammatory regulators were obtained from a comprehensive genetic study on 41 circulating cytokines, such as interleukin-4 (IL-4), eotaxin, and macrophage inflammatory protein-1β (MIP1β). We integrated summary-level data on frailty from two independent genetic studies on frailty index (FI) and Fried frailty score (FFS). The inverse-variance weighted method was used to assess the causal estimate. Sensitivity and heterogeneity analysis was performed to evaluate the stability of the estimates. The false discovery rate (FDR) method was used for P value adjustment of multiple comparisons. ResultsGenetically elevated levels of MIP1β and decreased levels of eotaxin were suggestively associated with increased FI (MIP1β: β = 0.016, Praw = 0.006, PFDR = 0.083; eotaxin: β = -0.030, Praw = 0.007, PFDR = 0.083) and FFS (MIP1β: β = 0.008, Praw = 0.027, PFDR = 0.247; eotaxin: β = -0.015, Praw = 0.014, PFDR = 0.247). In contrast, genetically predicted FI was suggestively associated with decreased levels of IL-4 (β = -0.395, Praw = 0.040, PFDR = 0.638) and platelet-derived growth factor BB (PDGF-BB, β = -0.385, Praw = 0.047, PFDR = 0.638) and increased levels of stem cell factor (SCF, β = 0.527, Praw = 0.005, PFDR = 0.204). Similar results were obtained from different sensitivity analysis. ConclusionsThe present study demonstrates that increased MIP-1β levels and decreased eotaxin levels might lead to a higher risk of frailty, whereas frailty might reduce the levels of IL-4 and PDGF-BB and increase the levels of SCF.

Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China

Water system connectivity is an important measure to optimize the balanced spatial allocation of water resources and water security patterns. Inland river basins in arid zones are generally insufficiently connected, so the scientific evaluation of the current status of water system connectivity and the centrality of its nodes in the water system network has practical significance for the scientific construction of regional water networks. Taking the Tarim River Basin in Xinjiang, China, as an object, this study conducted a comprehensive evaluation of basin water system connectivity by constructing a water system connectivity evaluation system with a total of 12 indicators for the three aspects of pattern connectivity, structural connectivity and functional connectivity. Subsequently, the TOPSIS model, with combined weights of the analytic hierarchy process and the entropy weight method, was used to comprehensively evaluate the connectivity of the watershed’s water system. The research evaluated the node centrality of the water system network that was closely related to the basin water system connectivity by using complex network analyses. The study results indicated the following: (1) among the source streams in the Tarim River Basin, the connectivity of the Aksu Basin was the best and that of the Keriya Basin was the worst and (2) the distribution patterns of the eigenvector centrality and betweenness centrality of the basin hydrological network nodes were similar, with nodes of highest centrality concentrated in the vicinity of the mainstems and the source–mainstem intersections. This work provides a basis and reference for the construction of water system connectivity and the selection of key control sections for ecological flow in the Tarim River Basin.

Unraveling shared and unique genetic causal relationship between gut microbiota and four types of uterine-related diseases: Bidirectional Mendelian inheritance approaches to dissect the "Gut-Uterus Axis"

BackgroundThe gut microbiota has emerged as a pivotal factor in the etiology of uterine-related diseases. This study aims to elucidate the genetic causal link between gut microbiota composition and these conditions, focusing on the systemic impact and uterine pathology to better understand the "Gut-Uterus Axis." MethodsWe utilized pooled data from two different GWAS databases, including data from 209 gut microbiota traits and data from four uterus-related diseases. Bidirectional Mendelian Randomization (MR) approaches, incorporating Bayesian weighting and traditional inverse variance weighting (IVW) methods, were employed to explore causal relationships. The robustness of findings was ensured through sensitivity analyses, outlier testing, and MR-PRESSO analysis. ResultsSeventeen significant associations were identified between gut microbiota traits and uterine-related diseases, suggesting potential causal links. These associations were consistent across sensitivity analyses, affirming the reliability of our results. Conversely, reverse MR analyses did not reveal statistically significant associations between uterine diseases and bacterial traits, indicating a unidirectional influence of gut microbiota on uterine health. These findings highlight the complex interplay within the "Gut-Uterus Axis." ConclusionThis research establishes a causal relationship between gut microbiota and uterine diseases, advocating for targeted interventions to mitigate associated risks. It underscores the interconnectedness of gut and reproductive health, promoting a holistic approach to management and treatment within the "Gut-Uterus Axis".

Association between antithrombotic agents use and hepatocellular carcinoma risk: a two-sample mendelian randomization analysis

BackgroundHepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide. Multiple observational studies demonstrated a negative association between the use of antithrombotic agents and the risk of HCC. However, the precise causal relationship between these factors remains uncertain. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to assess the causal link between these two factors.MethodThe summary statistics of single nucleotide polymorphisms (SNPs) associated with the use of antithrombotic agents were acquired from a genome-wide association study (GWAS) performed on individuals of European descent. A two-sample MR analysis was performed using the inverse variance weighting (IVW), the weighted median estimate, the MR-Egger regression, and the weighted-mode estimate. Sensitivity analysis of the primary findings was performed using MR-PRESSO, MR-Egger regression, Cochran’s Q test, and Leave-one-out analysis.ResultsTen SNPs associated with the use of antithrombotic agents were selected as instrumental variables. The MR analysis performed using the four methods mentioned above revealed a negative causal association between the use of antithrombotic agents and HCC. Univariate MR estimates based on the inverse variance weighting (IVW) method suggested a negative causal association between the use of antithrombotic agents and HCC [odds ratio (OR) 0.444, 95% confidence interval (CI) 0.279 to 0.707, P = 0.001]. The other methods also produced similar results. No heterogeneity and horizontal pleiotropy were found.ConclusionOur findings suggested an inverse causal association of antithrombotic agents with the risk of HCC.

Walking pace is a protective factor for rheumatoid arthritis: a mendelian randomization study

Walking pace is a simple and functional form of exercise and a strong predictor of health, but little is known about its causal association with rheumatoid arthritis. This study aimed to investigate the causal effect of WP on the developing RA using Mendelian randomization analysis. The genetic variation associated with WP was selected as an instrumental variable from the latest genome-wide association studies. Summary-level data for the outcomes were obtained from the corresponding GWAS. The inverse-variance weighted method was used as the primary MR analysis. The results were further tested using a multivariable MR approach based on Bayesian model averaging. Confounders (BMI, SMK, HBP, TD) with close associations with RA were included in the analysis. An observational study with individual data from UK Biobank was performed to reinforce our findings. The MR results indicated the significant inverse associations of WP with the risk of RA (odds ratio (OR), 0.31; 95% confidence interval (CI), 0.15, 0.62; p = 1.05 × 10 −3). After adjusting for the risk factors, the associations for WP and RA did not change substantially. Observational study results demonstrated the same effect of WP on reducing the risk of RA. The Mendelian randomization analysis and observational study provide evidence suggesting that walking pace is a protective factor for rheumatoid arthritis. Given its simple measurement, walking pace may be a pragmatic target for interventions.

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was &gt;99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level.

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Risk assessment of transmission towers based on variable weight theory and matter-element extension model

Abstract The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze-thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.

Factors Influencing Water Resource Levels under the Water Resource Carrying Capacity Framework: A Dynamic Qualitative Comparative Analysis Based on Provincial Panel Data

Most existing evaluation frameworks for water resource carrying capacity (WRCC) neglect the interdependencies between subsystems. To fill this gap, we introduce a dynamic qualitative comparative analysis (QCA) model to evaluate WRCC and apply it to a vital economic development corridor, the Yangtze River Economic Belt (YREB). Ecological, social, and economic subsystems are defined as condition subsystems, while the water resource subsystem is defined as the outcome subsystem. The entropy weight method is used to calculate and calibrate the comprehensive score of each subsystem. By analyzing the necessity of a single condition subsystem and the sufficiency of condition subsystem configuration via a dynamic QCA, we qualitatively analyze the impact extent and pathways of the ecological, social, and economic subsystems on the water resource subsystem within the WRCC framework. The results reveal generally stable water resource levels despite regional variances, thereby pinpointing the influence pathways, including ecological–social and ecological–economic configurations. The 2011–2015 period saw poor stability, which subsequently improved until 2019 before declining in 2020 in the YREB. The middle-reach urban cluster showed the highest stability, which was less impacted by condition subsystems. These findings could enable provinces and municipalities to tailor policies and enhance subsystem levels for better water resource management.

Research on Safety Evaluation of Stadium Reconstruction Construction Based on Combination Weighting Extension Model

As an important carrier of sports services and the main force participating in the “short board” project of urban development, the renovation and upgrading of old stadiums have become an important trend for the sustainable development of venues in the context of urban renewal, consumption upgrading, and national fitness. However, owing to the complexity of the transformation process, the probability of safety accidents continues to increase, posing a serious threat to national property security. In order to reasonably evaluate the safety of the stadium renovation construction process and reduce the incidence of accidents in the renovation project, this study proposed a safety evaluation model for the stadium renovation construction based on the combination weighting extension model. First, according to the 5M1E theory, 27 influencing factors were selected, a safety evaluation index system for stadium reconstruction construction was constructed, and the safety evaluation grade of the index was quantified. Second, based on the analytic hierarchy process (AHP) and improved entropy weight method, the combination weight of the index was determined, and a safety evaluation model was constructed using the matter–element extension theory. Finally, the established evaluation model was applied to the example of stadium renovation, and the construction safety level of the renovation project was obtained. The research results showed that the model has strong operability, and the evaluation results are reasonable and reliable, providing a new concept for the safety control of stadium reconstruction construction.

Spatio-Temporal Analysis of Urban Emergency Response Resilience During Public Health Crises: A Case Study of Wuhan

In recent years, public health emergencies have severely disrupted city functions and endangered residents’ health and lives, enhancing the emergency response capacity, a crucial aspect of building resilient cities. Based on the Wuhan Statistical Yearbook and local economic and social big data, this study constructed a resilience assessment system that covers resistance, adaptability, and resilience. Evaluate the resilience level of each region and analyze its spatiotemporal characteristics using the entropy weight method, Theil index, and natural breakpoint method. The central area exhibited higher resilience levels, while peripheral areas showed lower resilience, owing to location advantage and developmental disparities. The obstacle degree model revealed that scientific and technological innovation, social rescue capabilities, and population size are the primary weak links in building urban emergency response capacity. Based on these findings, this study aims to provide policy recommendations and improvement measures for multiple stakeholders, guide the construction of resilient cities, enhance the ability to respond to public health crises and recovery speed, and ensure urban functions and residents’ well-being.

Study on the Coupling Coordination Degree and Driving Mechanism of “Production-Living-Ecological” Space in Ecologically Fragile Areas: A Case Study of the Turpan–Hami Basin

One of the key conditions for achieving superior regional growth is ensuring the harmonious development of both the layout and functions of territorial space. Territorial space, which includes production space, living space, and ecological space, serves as a critical system and venue for economic, cultural, and social activities in a region. The harmonized growth of production–living–ecological space (PLES) is essential for attaining sustainable development goals. Research on PLES offers a fresh perspective on promoting sustainable development of the spatial domain and the sustainable use of resources. However, studies on PLE functions in ecologically fragile areas are lacking. Therefore, in this study, which adopts a PLES perspective, land-use data are used to classify land according to the dominant functions of production, ecology, and living. Integration of point-of-interest (POI) data with socio-economic data was established to spatially describe indicators at the grid level and create a scoring system for PLES indicators in ecologically fragile areas. Finally, the entropy weight method, holistic assessment methods, coupling coordination degree model (CCDM), and geodetector were employed to explore the coupling coordination relationships and factors influencing PLESs in the Turpan–Hami Basin from 2010 to 2020. The results indicate that the Turpan–Hami Basin consists predominantly of potential ecological space, mainly in the central and northern regions, which are characterized by the Gobi Desert and bare rock landforms. Over the past decade, the PLES framework has seen a notable rise in the allocation of residential and ecological areas, whereas the portion dedicated to production spaces has diminished. The overall coupling coordination degree (CCD) of PLES in the Turpan–Hami Basin is at a coordinated level and gradually increasing. The most significant impact on the degree of PLES coupling coordination is exerted by population and natural factors. The research findings provide theoretical support for the sustainable utilization of resources in the Turpan–Hami Basin and other ecologically fragile areas while also offering scientific evidence to promote the coupling coordination of PLES, thereby contributing to high-quality regional development.

Intermittent Preventive Treatment of Malaria in Pregnancy and the Impact on Neonates in African Countries as Assessed by Entropy Weight and TOPSIS Methods.

Background/Objectives: In regions of Africa with a high prevalence of malaria, pregnant women in their first or second trimester should be administered intermittent preventive treatment in pregnancy (IPTp). However, infants may contract malaria despite the IPTp therapy that their mothers have received. The objective of the present investigation was to assess the symptoms and various treatments for neonatal malaria. Methods: Entropy weight and TOPSIS were used to achieve the study goal. The TOPSIS multi-attribute decision-making system was used to assess newborn malaria symptoms and select the optimal treatment, even for mothers receiving IPTp medication during pregnancy. The entropy weight approach calculated TOPSIS attribute weights. The present research used UNICEF data for 14 African nations in 2023. Results: The results indicated that neonates whose mothers received IPTp therapy ultimately contracted malaria, with diarrhea being the primary symptom. It is important to note that health providers administer a combination of zinc and oral rehydration solution (ORS) to infants as the most effective treatment for malaria symptoms, thereby abandoning the first-line treatment for malaria, artemisinin-based combination therapy (ACT). Conclusions: The most effective treatment for neonatal malaria is a combination of zinc and ORS, although less than half of children in Africa have access to ORS. Therefore, the findings of this study may encourage African countries to prioritize co-pack therapy in their procurement and supply, healthcare provider training, and expenditures. This therapy will also help alleviate the symptoms of malaria in neonates.