Results Of Model Research Articles

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

Cities and Urbanization: Balancing the Environmental and Socioeconomic Dimensions of Sustainability

AbstractWith an increasingly urbanized world, there is an urgent need to examine how cities may evolve and achieve sustainability. This paper systematically looks at the Greater Bay Area (GBA) and the Poyang Lake Region (PLR) in China to examine the spatial processes for insights into cities and urbanization, balancing the environmental and socio‐economic dimensions. A total of 226 805 cells are analyzed to unveil the relationship between sustainability changes in 2015–2019 period and urban form indicators, considering sociodemographic variables, geographical features, and city size as control variables. Two tree‐based machine learning models (Random Forest and XGBoost) are developed. This study provides evidence that a monocentric urban form and a high share of small activity clusters are not good for sustainability. For each urban form indicator, there is a non‐linear relationship with sustainability. The results of the machine learning models reconfirm the sustainability benefits of having a strong second activity cluster comparable to the largest one. When planning cities, some forms of land use buffering are desirable. There is also support for developing relatively large activity nodes and promoting compactness in urban form. Beyond urban form characteristics, the levels of urbanization, economic development, and population are still highly relevant.

Increasing abdominal aortic aneurysm curvature visibility using 3D dual probe bistatic ultrasound imaging combined with probe translation

High frame rate ultrasound (US) imaging techniques in 3D are promising tools for capturing abdominal aortic aneurysms (AAAs) over time, however, with the limited number of channel-to-element connections current footprints are small, which limits the field of view. Moreover, the maximal steering angle of the ultrasound beams in transmit and the maximal receptance angle in receive are insufficient for capturing the curvy shape of the AAA. Therefore, an approach is needed towards large arrays. In this study, high frame rate bistatic 3D US data (17 Hz) were acquired with two synchronized matrix arrays positioned at different locations (multi-aperture imaging) using a translation stage to simulate what a larger array with limited channel-to-element connections can potentially achieve. Acquisitions were performed along an AAA shaped phantom with different probe tilting angles (0 up to ± 30°). The performance of different multi-aperture configurations was quantified using the generalized contrast-to-noise ratio of the wall and lumen (gCNR). Furthermore, a parametric model of the multi-aperture system was used to estimate in which AAA wall regions the contrast is expected to be high. This was evaluated for AAAs with increasing diameters and curvature. With an eight-aperture 0° probe angle configuration a 69 % increase in field of view was measured in the longitudinal direction compared to the field of view of a single aperture configuration. When increasing the number of apertures from two to eight, the gCNR improved for the upper wall and lower wall by 35 % and 13 % (monostatic) and by 36 % and 13 % (bistatic). Contrast improvements up to 22 % (upper wall) and 12 % (lower wall) are achieved with tilted probe configurations compared to non-tilted configurations. Moreover, with bistatic imaging with tilted probe configurations gCNR improvements up to 4 % (upper wall) and 7 % (lower wall) are achieved compared to monostatic imaging. Furthermore, imaging with a larger inter-probe distance improved the gCNR for a ± 15° probe angle configuration. The gCNR has an expected pattern over time, where the contrast is lower when there is more wall motion (systole) and higher when motion is reduced (diastole). Furthermore, a higher frame rate (45 Hz) yields a lower gCNR, because fewer compound angles are used. The results of the parametric model suggest that a flat array is suitable for imaging AAA shapes with limited curvature, but that it is not suitable for imaging larger AAA shapes with more curvature. According to the model, tilted multi-aperture configurations combined with bistatic imaging can achieve a larger region with high contrast compared to non-tilted configurations. The findings of the model are in agreement with experimental findings. To conclude, this study demonstrates the vast improvements in field of view and AAA wall visibility that a large, sparsely populated 3D array can potentially achieve when imaging AAAs compared to single or dual aperture imaging. In the future, larger arrays, less thermal noise, more steering, and more channel-to-element connections combined with carefully chosen orientations of (sub-) apertures will likely advance 3D imaging of AAAs.

Wind resistance performance analysis of metal roof system of the long-span integrated photovoltaic building

Wind resistance is an important factor in the operation of Building Integrated Photovoltaic (BIPV) systems, especially for long-span roofs, where lifting of the roof can result in significant economic losses. This paper proposes a periodic boundary numerical simulation method for long-span metal roof systems to address the problem of meshes and contact pairs in the numerical simulation of wind resistance. In order to calibrate the BIPV metal roof's material parameters, indoor tensile test was conducted, and the results of the global metal roof model were compared with those of the single metal roof model with periodic boundaries for verification of periodic boundary efficiency and accuracy. The wind-resistant capacity of laboratory and long-span sizes for the BIPV metal roof system are compared, and a conversion formula between them is proposed to enable the rapid calculation from the laboratory to long-span roof system. The results show that the periodic boundary is suitable for the analysis of wind resistance of the long-span BIPV metal roof. Wind resistance of the long-span BIPV metal roof system is 1.77 kPa, whereas that of the laboratory size is 4.50 kPa. According to the proposed formula, the wind-resistant capacity of the long-span metal roof can be converted from the laboratory test, which can provide technical support for the design of BIPV systems.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.

Determinants of Rural Development in European Union Countries and Türkiye

There are many variables that can affect rural development. The aim of the study is to find out what variables affect rural development in Türkiye and European Union countries. The data period used in the study was, annual data for the period 2002-2020. According to the results of the random effects model, Inflation, Rural Population, and Rural Population Increasing variables from 8 independent variables affect Rural Development. According to the results of the Generalized Estimation Equality Mass Average Test based on the Random Effects Model, population growth in Rural Areas, Population in Rural Areas and Inflation variables were statistically significant. In the study, the Granger causality test was performed to measure the causality links of the variables. It was found that there is a bidirectional causality relationship between the exports of Agricultural Raw Material and the employment rate of young people aged 15 -24 in rural areas. Results with unidirectional causality or no causal relationship were found among the remaining variables.

Impacts of rangeland ecological compensation on livelihood resilience of herdsmen: an empirical investigation in Qinghai Province, China

The increasing use of the payment for ecosystem services (PES) represents a prominent institutional shock triggered by external interventions on livelihoods of natural resource managers. To reconcile the needs for both conservation and development, a deep understanding on the influence mechanisms of PES on farmer livelihood resilience is paramount, particularly in dynamic and uncertain contexts. Taking the Rangeland Ecological Protection Subsidy and Reward scheme that has been implemented in pastoral China since 2011 as a case study, the paper empirically investigates the impacts of PES on livelihood resilience of livestock farmers in Qinghai Province. Based on a comprehensive evaluation of herder livelihood resilience from three dimensions—buffering, self-organizing and learning capacities, multiple linear regression was applied to examine the PES impacts. Resilience enhancement was mainly found in buffering capacity for herders with a medium to large herd size. Results of univariate parallel mediation models further reveal that household income growth and diversification and livestock upsizing were three key intermediary processes that mediated the enhancement and together contributed 28.83% of the total effect. The findings help narrow a knowledge gap in understanding whether, how and for whom PES contributes to improve farmer livelihood resilience. To achieve “win-win” outcomes, we stress that greater attention should be given to tailoring design and implementation of PES programs to align with the specificity of local contexts.

(Multi-field) natural inflation and gravitational waves

We provide a detailed study of natural inflation with a periodic non-minimal coupling, which is a well-motivated inflationary model that admits an explicit UV completion. We demonstrate that this construction can satisfy the most recent observational constraints from Planck and the BICEP/Keck collaborations. We also compute the corresponding relic gravitational wave background due to tensor perturbations and show that future space-borne interferometers, such as DECIGO, BBO and ALIA, may be able to detect it. Next, we extend this analysis and establish the validity of these results in a multi-field model featuring an additional R 2 term in the action, which allows us to interpolate between natural and scalaron (a.k.a. Starobinsky) inflation. We investigate the conditions under which the aforementioned future interferometers will have the capability to differentiate between pure natural inflation and natural-scalaron inflation. The latter analysis could open the door to distinguishing between single-field and multi-field inflation through gravitational wave observations in more general contexts.

Galaxy morphology classification based on Convolutional vision Transformer (CvT)

Context. The classification of galaxy morphology is among the most active fields in astronomical research today. With the development of artificial intelligence technology, deep learning is a useful tool in the classification of the morphology of galaxies and significant progress has been made in this domain. However, there is still some room for improvement in terms of classification accuracy, automation, and related issues. Aims. Convolutional vision Transformer (CvT) is an improved version of the Vision Transformer (ViT) model. It improves the performance of the ViT model by introducing a convolutional neural network (CNN). This study explores the performance of the CvT model in the area of galaxy morphology classification. Methods. In this work, the CvT model was applied, for the first time, in a five-class classification task of galaxy morphology. We added different types and degrees of noise to the original galaxy images to verify that the CvT model achieves good classification performance, even in galaxy images with low signal-to-noise ratios (S/Ns). Then, we also validated the classification performance of the CvT model for galaxy images at different redshifts based on the low-redshift dataset GZ2 and the high-redshift dataset Galaxy Zoo CANDELS. In addition, we visualized and analyzed the classification results of the CvT model based on the t-distributed stochastic neighborhood -embedding (t-SNE) algorithm. Results. We find that (1) compared with other five-class classification models of galaxy morphology based on CNN models, the average accuracy, precision, recall, and F1_score evaluation metrics of the CvT classification model are all higher than 98%, which is an improvement of at least 1% compared with those based on CNNs; (2) the classification visualization results show that different categories of galaxies are separated from each other in multi-dimensional space. Conclusions. The application of the CvT model to the classification study of galaxy morphology is a novel undertaking that carries important implications for future studies.

Comprehensive analysis of the seismic wave fields generated by offshore pile driving: A case study at the BARD Offshore 1 offshore wind farm.

Offshore pile driving not only generates high sound pressure levels, but also induces ground vibrations and particle motions that have the potential to affect fish and invertebrates living near or in the seabed. In particular, the seismic wave field in the form of interface waves is thought to be responsible for causing these particle motions and ground vibrations. However, the magnitude and spatial extent of the seismic wave field resulting from pile driving has not been clearly established. To fill this knowledge gap, this paper analyzes and illustrates in detail the seismic wave field at a construction site of the BARD Offshore 1 wind farm. For this purpose, the measured data from the construction site are compared to the results of a seismo-acoustic model. The measured and modeled data in combination provides a potential benchmark case for subsequent studies and other authors. The computed seismic wave field is investigated in terms of wave generation, mode composition, and propagation range of individual modes. The different seismic wave forms and their contribution to the particle motions in the seabed vicinity are discussed. The results indicate that, for the considered case, interface waves dominate the particle motion at the seafloor level up to a distance of 200 m.

Will environmental concern contribute to the transition of household cleaner cooking?

Access to clean cooking is a vital sustainable development goal, yet achieving it poses challenges within the current trend. We should now investigate whether the increasing environmental concern would drive a shift towards cleaner cooking or potentially lead to the abandonment of pro-environmental behaviors. Based on China's national micro survey data from 2012 to 2018, the results of multiple econometric models are: (1) Residents' environmental concern about environmental issues positively contributes to households use of non-solid clean cooking fuels. (2) Rising income levels and urbanization promote a smoother transition towards cleaner cooking. (3) Higher education and Internet access can reinforce the role of environmental concern. (4) The transition to cleaner cooking fuels has a net reduction in household expenditure, as it slightly increases fuel costs but significantly reduces health expenditures. The importance of environmental concern in household energy decision-making is highlighted, and suggestions are provided to promote the cleaner transition of household cooking energy.

Mediating Role of Cultural Influence Between Green Value Consciousness and Purchase Intention Towards Herbal Skin Care Products

Purpose: This study examines the mediating role of cultural influences between green value consciousness and purchase intention towards herbal skin care products.
 Research Methodology: Descriptive and hypothesis testing research design is used through a survey method using a designed questionnaire; primary data was collected from 300 consumers of herbal skin care products residing in Delhi/NCR. The study used regression estimates to measure the impacts and measurement model for Confirmatory factor analysis (CFA), followed by testing research hypotheses based on the results of the structural model. 
 Findings: It reported that there is a positive significant relationship between green value consciousness and purchase intention, between green value consciousness and the role of Cultural influence, and between the role of Cultural influence and purchase intention. Based on the results, it is revealed that Cultural influence partially mediates between green value consciousness and purchase intention.
 Practical implications - The brand managers should use the green value of products and Cultural influence for massive awareness, promotion, and branding to make a brand of herbal skin care products.
 Originality/value – This study contributes to the facts and theoretical discussion on the cultural influence on green value consciousness and buying intention.

Employing deep learning in non‐parametric inverse visualization of elastic–plastic mechanisms in dual‐phase steels

AbstractEnhancing the interpretability of machine learning methods for predicting material properties is a key, yet complex topic in materials science. This study proposes an interpretable convolutional neural network (CNN) to establish the relationship between the microstructural evolution and mechanical properties of non‐uniform and nonlinear multisystem dual‐phase steel materials and achieve an inverse analysis of the elastic‐plastic mechanism. This study demonstrates that the developed CNN model achieves an accuracy of 94% in predicting the stress‐strain curves of dual‐phase steel microstructures with different compositions and processes, with the mean absolute error not exceeding 50 MPa, representing merely 5.26% of the average tensile strength of dual‐phase steels in the dataset. The reverse visualization results of the CNN model indicate that, during tensile deformation, the grain boundaries maintain deformation coordination within the grains by impeding dislocation slip. This results in a significant stress concentration at the grain boundaries, with stresses at the boundaries being higher than those borne by the martensitic phase and minimal stresses in the ferrite phase. Moreover, compared with traditional crystal plasticity models, the CNN model exhibits a substantial improvement in computational efficiency. This method provides a generic plan for improving the interpretability of machine learning methods for predicting material properties and can be easily applied to other alloy systems.

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Examining climate change awareness and climate-friendly activities of urban residents: A case study in Košice

As the impact of climate change begins to be felt all around the world, the vulnerability of urban areas due to their population density, high concentration of economic activities and the altered characteristics of the physical environment is becoming increasingly apparent. As a result, cities will play a crucial role in tackling climate change by fostering mitigation activities and managing and coordinating the adaptation process. Research has shown that engagement and awareness among citizens is vital in the promotion of effective adaptation policies, but studies have also drawn attention to the significance of the so-called “awareness-action gap,” the absence of a direct link between awareness and action. This study contributes to the ongoing debate over the awareness-action nexus by discussing the factors that can affect the adaptation and mitigation activities of urban citizens using the Slovak city of Košice as a case study. Data was collected using a survey conducted with local residents as part of the preparations for the city’s first adaptation strategy on climate change. The paper uses a two-step analysis to investigate the socio-economic, psychological and contextual factors associated with climate change awareness and climate-friendly activities. The results of the standard linear regression model indicate that self-assessed vulnerability is a determining factor in both the level of climate change awareness and the likelihood of individuals to take action. In the second step, we find that the factors which influence engagement in climate-friendly activities differ depending on the costs involved in implementing the activities. The exploratory analysis conducted using a machine learning algorithm suggests that the awareness-action nexus may be non-linear in nature. The main findings of this study imply that communication strategies should place a primary emphasis on individuals’ vulnerability to the negative impacts of climate change. The paper contributes to the existing research by providing a case study of a generally under-researched geographical area of Central Europe. In addition, the analysis provides a specific context of urban area and implications for local policymakers, something that has been lacking the in literature looking primarily at data at the country level.

The relationship between teachers’ and principals’ use of results from nationwide achievement tests: the mediating role of teacher attitudes and data use culture

In many countries, educational policy increasingly expects teachers to use results from nationwide achievement tests for data-based decision-making. Theoretical models suggest that teachers’ data use is not only related to their attitudes and the organizational culture of data use in their schools but also to the use of data by their principals. However, the relationship between teachers’ and principals’ data use has rarely been tested, and existing studies only provide limited evidence. This study examined these relationships using data from two large-scale samples (Nmathematics teachers =796, NGerman teachers =693). Results of structural equation models show that data use by teachers is significantly associated with data use by principals. Moreover, data use culture use fully mediates this relationship, but teacher attitudes do not. We replicate our results for two samples, providing evidence for the generalizability. Thus, supporting principals to develop a positive data use culture seems promising for future professional development.

A direct Monte Carlo approach for the modeling of neutrals at the plasma edge and its self-consistent coupling with the 2D fluid plasma edge turbulence model HESEL

This paper presents a novel coupling of a kinetic description of neutrals with a fluid description of a fusion plasma. The code, plasma interacting super-atoms and molecules (PISAM), employs a grid-free Cartesian geometry and a direct simulation Monte Carlo approach to solve the kinetic equations of deuterium atoms and molecules. The grid-free geometry and the parallel nature of the neutral dynamics, in the absence of neutral–neutral interactions, allow for an unlimited and work-efficient parallelization of PISAM that always ensures a balanced workload. The highly optimized Python implementation obtains good performance while securing easy accessibility to new users. The coupling of PISAM with the edge turbulence model HESEL is outlined with emphasis on the technical aspects of coupling Message Passing Interface-parallelized Python and C++ codes. Furthermore, the paper presents and analyzes simulation results from running the coupled HESEL-PISAM model. These results demonstrate the impact of radial neutral transport and plasma–neutral dynamics perpendicular to the magnetic field. Specifically, they illustrate how the inward flow of neutral kinetic energy and the inhibition of radial electric shear, resulting from poloidal momentum transfer between atoms and ions, can affect the energy containment time. By comparing the results of the HESEL-PISAM model with those obtained from coupling HESEL with a diffusive-fluid-neutral model, the capabilities of diffusion models in predicting neutral transport in the plasma edge and scrape-off layer are elucidated.

Estimation and modeling of the biomass and carbon storage in the stump and root of Populus deltoides

Carbon storage in plant biomass is the simplest and most economically viable way to reduce atmospheric carbon dioxide carbon dioxide concentration. The objective of this study was to estimate and model the biomass and carbon storage of the stump and root in Populus deltoides trees in afforestation, Guilan Province of Iran. For this purpose, 93 trees with 25 years old were selected from different diameter classes and after cutting and removing the sections, an excavator was used to unearth the stumps and roots of the trees from the soil. In order to estimate the biomass and carbon storage of tree stumps and roots, samples of these components were isolated and weighed, and after drying in the oven, the dry weight of the samples was measured. The carbon content of stumps and roots of the trees were calculated after burning the dried samples in an electric furnace. The results showed that the biomass of stumps and roots was 7.99 and 65.5 kg per tree, respectively, and the carbon content of stumps and roots was estimated to be 3.92 and 32.32 kg per tree, respectively. The results of allometric equations showed that the models for estimating biomass and carbon storage of stumps and roots using the tree stump diameter variable had a high coefficient of determination, but the model using the stump height had no acceptable coefficient of determination. The evaluation results of different models also showed that the power model was more accurate than other models. In general, the results showed that the carbon stored in stumps and roots was 1.568 and 12.928 tons per hectare, respectively.

Physics-Informed Neural Networks for Modeling Water Flows in a River Channel

The impacts incurred by floods regularly affect the planet&#x0027;s population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained from conceptual or data-driven methods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on Physics-Informed Neural Networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and parameter identification of differential equations that govern the underlying dynamics. This work investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. <i>Impact Statement</i>&#x2014;The research seeks to contribute to the hydrological modeling area with a surrogate model based on Physics-Informed Neural Networks to water flow in a watershed. In practice, these models use conceptual or data-driven methods. Conceptual models to reach the precision provided by the methodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system behavior. We introduce PINNs as a viable solution for training neural networks with a few training data and estimating the PDE parameters that govern the underlying dynamics. In addition, we present a novel strategy for training PINNs inspired by Bayesian inference for parameter estimation problems. The research aims to bring forward opportunities to the nontrivial task of hydrological modeling and tools for balancing learning from both physics and data. Consequently, to develop concepts for real-time applications.

A Mendelian Randomization Study: Roles of Gut Microbiota in Sepsis - Who is the Angle?

Gut microbiota (GM) is a crucial underlying player during sepsis pathogenesis. However, the causal relationship is unclear and remains to be determined. A two-sample Mendelian randomization study was implemented. The statistical data about sepsis together with GM summarized from genome-wide association studies were evaluated. Instrumental variables were defined as single-nucleotide polymorphisms with prominent correlations with exposure. The inverse-variance-weighted test was employed as a major approach of Mendelian randomization analysis to estimate of causal relationships. The inverse-variance-weighted analysis results demonstrated that at different taxa levels, Actinobacteria and Bifidobacteriaceae influence sepsis. Actinobacteria had negative relationships to sepsis risk at the phylum (β = -0.34, SE = 0.10, p = 0.0008) and class (β = -0.23, SE = 0.07, p = 0.0011) levels in outcome coded ieu-b-69. Actinobacteria at the phylum level (β = -0.22, SE = 0.10, p = 0.027) was also negatively associated with sepsis in outcome coded ieu-b-4980. Bifidobacteriaceae at the order (β = -0.20, SE = 0.06, p = 0.0021), family (β = -0.20, SE = 0.06, p = 0.0021), and genus (β = -0.20, SE = 0.06, p = 0.0007) levels were all negatively correlated with the risk of sepsis in outcome coded ieu-b-69. The results of the Wald ratio model showed that Tyzzerella genus (OR (95%CI) = 0.6902[0.4907,0.9708], p = 0.0331) and Gastranaerophilales order (OR (95%CI) = 0.5907[0.3516,0.9926], p = 0.0468) were negatively connected with sepsis. This study implied at different taxa levels Actinobacteria and Bifidobacteriaceae, Tyzzerella genus, and Gastranaerophilales order have a causal relationship with sepsis, indicating that they are protective factors for the incidence of sepsis.