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In this study, the microstructure evolution on hot deformation was investigated and a dynamic recrystallization (DRX) kinetic model was constructed based on the hot deformation behavior of the homogenized state Mg–7Gd–3Y–1Zn–0.5Zr (VW73B) alloy within 713–773 K and 0.01–0.1 s−1. The DRX critical strain reduced when the deformation temperature (T) increased and increased when the strain rate (ε˙) increased. The variation trend of the DRX volume fraction was inversely proportional to the critical DRX strain. The constructed DRX kinetic model was modified by introducing a compensation factor; the modified model showed a mean absolute deviation of the DRX volume fraction of 1.8%. Combined with the microstructure analysis, it can be found that the Long Period Stacking Ordered (LPSO) phase is able to promote DRX during the hot deformation, but its ability to promote DRX is less pronounced than that of grain boundaries. At the block-shaped LPSO phase interface, the grain boundary arched by strain induction provided a nucleation site for DRX, enabling discontinuous dynamic recrystallization (DDRX). At the lamellar LPSO phase interface, continuous dynamic recrystallization (CDRX) occurred predominantly through the continuous migration and merging of subgrains, resulting in the formation of DRX grains. The results of this study could guide future research on the DRX of the VW73B alloys with varied applications.

ABSTRACT Aims/Introduction To investigated the association between serum asprosin and metabolic characteristics in type 2 diabetes mellitus patients with different durations. Materials and Methods A total of 436 patients with type 2 diabetes mellitus were enrolled in this study from the community health service center in southeastern Shanxi Province. All the patients were divided into two groups according to their diabetes duration: diabetes duration ≤5 years group (n = 132) and diabetes duration ≥10 years group (n = 304). Fasting blood samples were gathered and serum asprosin was tested. Pearson/Spearman correlation analysis was carried out. Results Asprosin was comparable between the two groups. Asprosin was positively correlated with systolic blood pressure (SBP), triglycerides, creatinine, serum uric acid and low‐density lipoprotein cholesterol in the diabetes duration ≤5 years group (P

ABSTRACT Aims/Introduction Serum asprosin is expected to become a screening indicator in early‐stage diabetic heart disease. The relationship between serum asprosin and left ventricular diastolic dysfunction (LVDD) was studied in elderly patients with type 2 diabetes mellitus in the community. Materials and Methods A total of 252 elderly patients with type 2 diabetes mellitus were recruited from Zhuoma Community Care Station and Chengbei West Street Community Care Service Center in Changzhi City of Shanxi Province from November 2019 to July 2021. Patients were divided into the LVDD group (n = 195) and the non‐LVDD group (n = 57). The t‐test, Mann–Whitney U test, and χ2 test were used to compare indicators between the LVDD group and the non‐LVDD group. Pearson or Spearman correlation analysis was adopted to evaluate the correlation between serum asprosin and other clinical data. Multivariate logistic regression analysis was applied to analyze the influencing factors on LVDD. Results Compared with patients without LVDD, patients with LVDD had a higher level of low‐density lipoprotein cholesterol (LDL‐C), fasting blood glucose (FPG), and asprosin, but a lower level of early diastolic movement speed (A) to diastolic movement velocity (E) (E/A). Asprosin was positively associated with waist circumference (WC), body mass index (BMI), creatinine, triglycerides (P 355.08 pg/mL [OR (95% CI) is 2.549 (1.275–5.095), P

Abstract Traditional atmospheric chemistry posits that sulfur dioxide (SO2) can be oxidized to sulfate (SO4 2–) through aqueous-phase reactions in clouds and gas-phase oxidation. Despite adequate knowledge of traditional mechanisms, several studies have highlighted the potential for SO2 oxidation within aerosol water. Given the widespread presence of tropospheric aerosols, SO4 2− production through aqueous-phase oxidation in aerosol water could have a pervasive global impact. Here, we quantify the potential contributions of aerosol aqueous pathways to global sulfate formation based on the GEOS-Chem simulations and subsequent theoretical calculations. Hydrogen peroxide (H2O2) oxidation significantly influences continental regions both horizontally and vertically. Over the past two decades, shifts in the formation pathways within typical cities reveal an intriguing trend: despite reductions in SO2 emissions, the increased atmospheric oxidation capacities, like rising H2O2 levels, prevent a steady decline in SO4 2− concentrations. Abating oxidants would facilitate the benefit of SO2 reduction and the positive feedback in sulfate mitigation.

The effects of extrusion ratio (ER) on the microstructures, static mechanical properties, and tension-compression high cycle fatigue (HCF) behavior of the as-extruded Mg–5Zn–1Mn (ZM51) alloy was investigated systematically, and the HCF mechanism was discussed. The results show that texture weakening and grain refinement caused by increasing the ER can effectively enhance the static mechanical properties and alleviate the tensile-compressive asymmetry of the alloy. The fatigue strength at 107 cycles is 124 ± 5, 138 ± 4, and 142 ± 5 MPa for ER8, ER11.5, and ER23, respectively, and the corresponding fatigue ratio is approximately 0.45, 0.49, and 0.50, which means that the as-extruded ZM51 alloy possesses outstanding fatigue resistance. The failure analysis and microstructure evolution of post-fatigued samples demonstrated that the stress amplitudes and microstructure characteristics strongly influence the HCF mechanism of the alloy. At high-stress load, close to the tensile yield strength 160–180 MPa, abundant {101‾2} residual twin bands were observed near the fracture surface. Twinning/detwinning deformation is the dominant fatigue mechanism due to the plastic deformation incompatibility between the matrix and the residual twins. At low-stress load, close to the fatigue strength 125–145 MPa, the fatigue crack initiation mechanism transits from twinning/detwinning to dislocation slip. Grain refinement and texture weakening inhibit the activation of {101‾2} twins, alleviate the twin-dislocation interaction, and promote dislocation slip to dominate the fatigue deformation, thereby enhancing the fatigue life of the alloy. This work provides new insight into the design and development for enhancing the fatigue resistance of wrought Mg alloy to ensure the long-term service safety of structural materials.

The effects of Sn in as-extruded Mg-1.2Ga alloys on the electrochemical and discharge properties were investigated by in-situ electrochemistry, half-cell and air-cell tests to provide new ideas for the development of magnesium alloy anode materials. The addition of Sn promoted grain refinement and deformation precipitation on the extrusion streamlines. Extrusion can effectively reduce the |bc| value of the alloy, improving the cathodic hydrogen control process. After the addition of Sn, more secondary phases are introduced. The redeposition and dissolution of Sn and Ga are enhanced. In conclusion, the GT12 alloy has excellent anode efficiency and average cell voltage, and its maximum discharge capacity (1292.98 mAh·g−1 at 75 mA·cm−2) is 55 mAh·g−1 higher than that of the as-cast alloy, showing great potential for development.

Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3−)). The mechanism between ε(NO3−) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3−). Here we introduce a supervised machine learning approach—the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42−, and temperature as pivotal drivers for ε(NO3−). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Aluminum thermal welding joints are widely used in the maintenance welding of heavy-haul railways due to their easy handling and high efficiency. However, due to their inherent welding characteristics, welding results in certain differences in the material’s physical properties at the welding zone compared to adjacent base materials, leading to the occurrence of short-wave irregularity under long-term wheel–rail interactive forces. In order to explore the evolution characteristics of weld irregularity, dynamic characteristics, and plastic deformation under long-term wheel–rail impact, a detailed tracking test was conducted on a normal aluminum weld, and the process from being put on the track to being damaged and replaced was evaluated. At the same time, a rigid–flexible coupling model was established for subsequent analysis, and plastic damage was analyzed using the finite element model. The results show that the service life of the weld can be divided into three different stages: the initial stage, the intermediate stage, and the damage stage. In the damage stage, a temporary separation occurred between the wheel and rail, leading to a sudden change in the wheel–rail interaction. The weight of 250 MT at the weld reached the repairment control limit. The concentration effect of equivalent plastic deformation was most serious at 2~5 mm below the rail head.

This study leveraged 2019 online data of particulate matter (PM2.5) and volatile organic compounds (VOCs) in Tianjin to analyze atmospheric pollution characteristics. PM2.5 was found to be primarily composed of water-soluble ions, with nitrates as the dominant component, while VOCs were predominantly alkanes, followed by alkenes and aromatic hydrocarbons, with notable concentrations of propane, ethane, ethylene, toluene, and benzene. The receptor model identified six major sources of PM2.5 and seven major sources of VOCs. The secondary source is the main contribution source, while motor vehicles and coal burning are important primary contribution sources in PM2.5. And, industrial processes and natural gas volatilization were considered major contributors for VOCs. A health risk assessment indicated negligible non-carcinogenic risks but potential carcinogenic risks from trace metals As and Cr, and benzene within VOCs, underscoring the necessity for focused public health measures. A risk attribution analysis attributed As and Cr in PM to coal combustion and vehicular emissions. Benzene in VOCs primarily originates from fuel evaporation, and industrial and vehicular emissions. These findings underscore the potential for reducing health risks from PM and VOCs through enhanced regulation of emissions in coal, industry, and transportation. Such strategies are vital for advancing air quality management and safeguarding public health.

Abstract Ground-level ozone affects Earth’s climate and human health. The formation of ozone is a complex process, depending on both atmospheric chemical processes and meteorological factors. In this study, machine learning coupled with a structure mining analysis was applied to investigate the ozone formation mechanism in Tianjin, China. The results showed isoprene has the greatest individual impact on local ozone generation, which suggests the biogenic emission of vegetation contribute significantly to native ozone pollution. The interaction between isoprene and nitrogen oxides is the strongest among precursors, with an obvious antagonistic effect between them. Reducing active volatile organic compounds is more effective for mitigating ozone pollution. Visualized network diagram also clearly illustrated the impacts of multiple drivers on ozone formation: isoprene, temperature and nitrogen oxides were the key drivers among all the influencing factors, other drivers (such as relative humidity) could assist the key drivers to collaboratively enhance or suppress ozone formation.

With the rapid development of underground space, the issue of safety in construction processes is becoming more and more significant. The purpose of this paper is to solve the problem of the existing underground space monitoring technology not being centralized and unified. In view of the problems related to large underground spaces in the process of constructing complex structures, with the introduction of Internet of Things technology and digital twins, we put forward an application of an intelligent safety-monitoring digital twin system in the construction of a large underground space structure, and at the same time, explore the Internet and digital integration mechanism of the twin system. The twin system uses BIM technology to establish the corresponding physical construction model, and collects multi-source heterogeneous monitoring data in real time through Internet of Things technology to achieve the exchange of information between the virtual construction model and the physical construction model. The twin system uses the multi-source heterogeneous data for real-time security analysis, and obtains the security status of the structure and feeds it back to the application service layer. The effectiveness and practicability of the twin system in large underground spaces are verified by an example project. Aiming at the safe performance of the orthogonal arch, the mapping relationship of various parameter indexes is obtained, and reasonable control measures are given. This study provides a new solution for improving the safety of construction projects and risk prevention and control, and has important theoretical and practical value for the safety management of underground space construction processes.

The thermal compression deformation behavior of homogenized Mg–5Zn–1Mn (wt.%) alloy was systematically investigated over the temperature and strain rate ranges of 285–345 °C and 0.001–1 s−1, respectively. The constitutive model and thermal processing diagrams were established. The microstructure evolution process and dynamic recrystallization (DRX) mechanisms were analyzed. The results demonstrate that the flow stress curves of the ZM51 alloy exhibit prominent DRX characteristics. The peak stress can be improved by decreasing the deformation temperature or increasing the strain rate. A finite element simulation is performed, and the Arrhenius constitutive model can accurately depict the plastic flow process of the alloy with strain correction, and the average activation energy is about 143.821 kJ/mol. Both the percentage DRX and the DRX grain size are found to be sensitive to the applied deformation conditions, including temperature, strain rate, and true strain. The formation of DRX grains promotes the weakness of the texture and the refinement of the grains. Grain boundaries nucleation (including discontinuous and continuous DRX) and particle-stimulated nucleation (PSN) are the main DRX mechanisms. Based on the processing map, ZM51 alloy is suitable for large plastic deformation by multi-pass forging. The optimum processing temperature is 345 °C, and as the degree of plastic deformation increases, the strain rate can be suitably increased. Local plastic flow and microcracking at grain boundaries are the primary instability mechanisms of the alloy.

Abstract Cadmium (Cd) contamination in paddy fields and its subsequent transfer in soil–rice systems are of particular concern. Significant discrepancies exist in the transfer process of Cd pollution sources from soil to rice. Here, we proposed a novel hybrid framework to reveal the priority of controlling Cd pollution sources in soil–rice systems, based on a high-dimensional geographical database. We further defined transfer potential (TP) to describe the ability of Cd from soil to rice (TPr = Cdr/Cds) and activated status (TPa = Cda/Cds), respectively, to reveal the priority sources of Cd pollution at the regional scale. The mining source has both high levels of TPr and TPa, which should be a controlled priority. Followed by traffic sources with a higher value of TPr, showing the risk to rice rather than the soil. The activated and enriched capacities of soil Cd are unequal in different sources that we attribute to the disparities of Cd transport in soil–rice systems. Cd contamination shows a significant spatial heterogeneity due to the difference in its transport performance. Our findings provide support for designing site-specific and pollution-targeted control priorities for suitable Cd pollution mitigation strategies at the regional scale.

Neuroblastoma (NB) is a leading cause of death in children. It usually occurs in the adrenal gland and rarely in the spinal canal. Here, we report the case of a 48-year-old male patient with abnormal thickening of the cauda equina nerve as revealed by lumbosacral magnetic resonance imaging. The patient’s main clinical manifestations were numbness and pain in both lower limbs. The patient underwent surgical treatment; however, intraoperatively, an unclear border was observed between the cauda equina nerve and the tumor; therefore, the tumor was not forcibly excised. The postoperative pathological results were reported as NB. The disease known as NB, which is extremely rare. We believe that a pathological biopsy is extremely vital for diagnosing NB, and aggressive post-operative radio-chemotherapy could potentially prolong the patient’s survival time.

Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East–West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry.

Abstract Tropospheric ozone (O3) affects Earth’s climate and human health. Volatile organic compounds (VOCs), major contributors to O3 formation, are of particular interest. Generally, the measured concentrations of VOCs (M-VOCs) and O3 show nonlinear or even opposite time serial-trend. We attributed the phenomenon to survivor bias: lack of insight of the photochemically consumed VOCs (C-VOCs) which emitted from sources to ambient and devote to forming O3, while excessive concern on the measured VOCs (M-VOCs) at observation site. Both observational and model results provide evidence that C-VOCs are the key to O3 formation. We proposed an improved model to quantify the source contributions of C-VOCs (biogenic emissions, gasoline evaporation, industry, etc.) and their impacts on the formation of O3, successfully avoiding the misidentification of dominant VOCs sources originated from the survivor bias in observational data. The survivor bias found in this study highlights that focusing of M-VOCs directly is insufficient and demonstrates the necessity of capture the sources of C-VOCs which contribute to O3 formation.

Ground-level ozone (O3) pollution has received widespread attention because its rising trend and adverse ecological impacts. However, the extremely strong photochemical reactions of its precursor volatile organic compounds (VOCs) increase the difficulty of reducing VOCs emissions to alleviate O3. Here, we carried out a one-year comprehensive observation in two representative cities, Tianjin (TJ, Northern China) and Guangzhou (GZ, Southern China). By revealing the concentration characteristics of three different types of VOCs, i.e., initial VOCs without photochemical reaction (In-VOCs), consumed VOCs (C-VOCs), and measured VOCs after the reaction (M-VOCs), we elucidated the important role of C-VOCs in the formation of O3. Although the overall trends were similar in both cities, the average concentration level of VOCs in GZ was 8.2 ppbv higher than that in TJ, and the photochemical loss of VOCs was greater by 2.2 ppbv. In addition, various drivers affecting O3 generation from C-VOCs were specifically explored, and it was found that most alkenes of TJ were key substances for rapid O3 formation compared to aromatics of GZ. Meanwhile, favorable meteorological conditions such as high temperature (T > 31 °C in TJ, and T > 33 °C in GZ), low relative humidity (56% in TJ and 45% in GZ), and stable atmospheric environment (proper pressure and gentle wind speed) also contribute to the generation of O3. More importantly, we combined chemical kinetics and receptor model to quantify the three-type VOCs source contributions and assess the potential impact of C-VOCs sources on O3 production, thus proposing environmental abatement technologies corresponding to the three types of VOCs. The differences in the comparison results of the three-type VOCs highlight the need to reduce O3 pollution from C-VOCs sources, which provides insights for future clean air policies development.

In response to the economic and social hazards posed by the COVID-19 global pandemic, many countries have adopted various information technologies to rapidly track and control the spread of the epidemic. Health Quick Response (QR) codes are emergency measures implemented by the Chinese government in the epidemic environment to balance epidemic prevention and control with recovery of economic and social development, and facilitate public mobility across regions and access to various public spaces. The use of health codes by the public is a practical necessity, but the satisfaction of their use is influenced by multiple factors such as environment, technology, and organization. In this paper, we collected data through a questionnaire to analyze the basic situation of public satisfaction with the use of health QR codes in China and its influencing factors. The results show that perceived quality and platform trust directly affect the satisfaction of health code usage, while environmental risk and platform trust indirectly affect the satisfaction of health code usage through the mediating effect of perceived quality.

Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional ‘factor-energy consumption’ to three parts of ‘factor-event-energy consumption’, which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

The precise and effective prognosis of safety risks is vital to ensure structural safety. This study proposed an intelligent method for the health monitoring of cable network structures, based on the fusion of twin simulation and sensory data. Firstly, the authors have established a framework that integrate simulation data with sensory data. The authors have established a high-fidelity twin model using genetic algorithm. The mechanical parameters of the structures were obtained based on the twin model. The key components of the structure are captured by using Bayesian probability formula and multiple mechanical parameters. The fusion mechanism of twin simulation and random forest (RF) was established to capture the key influencing factors. The coupling relationship between structural safety state and key factors was obtained, and the safety maintenance mechanism was finally formed. In view of the risk prognosis of the structure, the establishment method for the database of influencing factors and maintenance measures was formed. The authors used the Speed Skating Gymnasium of 2022 Winter Olympic Games (symmetric structure) as the case study for validating the feasibility and effectiveness of the proposed method. The theoretical method formed in this study has been applied to the symmetric structure, which provides ideas for the safety maintenance of large symmetric structures. Meanwhile, this research method also provides a reference for the health monitoring of asymmetric structures.

The construction process of cable net structure is complicated, which leads to the strong randomness of construction errors. The safety state of the cable net structure is very sensitive to construction errors. Obtaining the coupling relationship between construction errors and cable force response efficiently and accurately is critical to developing the construction technique of cable structures. This paper proposed an analysis method based on a genetic algorithm optimized back propagation neural network (GA-BPNN) to judge the influence of construction error on the cable force of single-layer orthogonal cable network structures. Taking the speed skating stadium of the 2022 Winter Olympic Games as the research object, this paper analyzed the structure form of the venue. According to the characteristics of cable network structure and GA-BPNN calculation, the principle of construction error analysis was put forward. The influence of construction errors of load-bearing cables and stable cables on cable force response was analyzed. The influence degree of different component errors on structural cable forces was obtained, and the most unfavorable key components were obtained. For the key components, the influence trend of different construction errors on cable force was analyzed, and the fitting formula was given. Driven by GA-BPNN, it can realize the analysis of structural and mechanical responses under the action of multi-type, multi-component, and multi-combination construction errors. The results show that the research method efficiently and accurately obtains the performance law of structural cable force under the influence of construction error, effectively predicts the influencing factors of the structural safety risk, and effectively avoids structural safety accidents caused by construction error. The construction errors analysis method based on GA-BPNN proposed in this paper can provide a reference for similar structural analysis and application.

As a plant organ with the largest surface area, leaves are the main place where photosynthesis and respiration take place. High-throughput phenotyping of crop leaves is of great significance for breeding, growth monitoring, and increasing crop yield. Due to the highly complex and diversified plant structures, automated leaf segmentation and phenotypic feature extraction remain to be challenging tasks. In this article, we propose a novel five-stage framework that comprises multiview stereo point cloud reconstruction, preprocessing, stems removal in canopy, leaf segmentation, and leaf phenotypic feature extraction to carry out leaf phenotyping on two types of ornamentals-Maranta arundinacea and Dieffenbachia picta. The phenotypic traits such as the leaf area, leaf length, width, and leaf inclination angle for each single leaf are calculated and compared with ground truths. The experimental results show that the average accuracy of calculated leaf area of the two species reached 96.8% and 97.8%, respectively. The average errors of both the calculated leaf length and width of Maranta arundinacea are less than 4.0%, and for Dieffenbachia picta, the average errors of calculated leaf length and width are both no higher than 4.7%. The average errors of calculated leaf inclination angle for the two plant species are 2.9° and 3.0°, respectively.

The codling moth, Cydia pomonella, is one of the major pests of pome fruit (apples and pears) and walnuts. Here, the authors sequence and analyze its genome, providing insights on olfactory and detoxification processes that may underlie its worldwide expansion.

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects’ mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.

This study is aimed at the fact that material allocation and construction progress cannot be intelligently controlled in the construction of prestressed steel structures. An intelligent planning method of a material-allocation path for prestressed steel-structure construction, based on digital twins (DTs), is proposed. Firstly, the characteristics of material allocation in the process of structural construction are analyzed, and a five-dimensional integrated DT framework for intelligent path-planning is built. Driven by the DT framework, the progress and environmental information of the construction site are collected in real time. At the same time, the field working conditions are dynamically simulated in the virtual model, so as to realize the interactive mapping between physical space and virtual space. In each construction process, by integrating the progress of each process at each construction location, and the storage and allocation of materials, a multidimensional model for the intelligent planning of material allocation is formed. The information fusion of virtual and real space is carried out using an entropy method to analyze the construction buffer time and material allocation time at each location of the construction site. On this basis, combined with the Dijkstra algorithm, the transportation time associated with the path is calculated according to the field distribution of each location. A feasibility analysis is carried out in the virtual model and imported into the field dynamic-marking system. Combined with radio frequency technology to guide material allocation on site, the intelligent planning of the material-allocation path is realized. In this study, taking the construction of the National Speed Skating Pavilion of the 2022 Beijing Winter Olympics as an example, the DT technology and Dijkstra algorithm are applied to the intelligent planning of the material-allocation path. It is fully verified that the intelligent method can effectively coordinate the relationship between schedule control and material allocation.

Building operation and maintenance (O&M) processes are tedious. Controlling such tedious processes requires extensive visualization and trustworthy decision-making strategies. Unfortunately, challenges still exist as existing technologies and practices can hardly achieve effective control of building O&M processes. This study has established a method for achieving intelligent control of building O&M processes by integrating Global Navigation Satellite System (GNSS) with Digital Twins (DTs) techniques. Specifically, GNSS could be used to capture real-time building information during building O&M processes. Such captured real-time information realizes the intelligent closed-loop control of building O&M driven by DTs. In this study, the authors have (1) captured the dynamic information required for achieving intelligent control of building O&M processes, (2) established a DT model of building O&M processes, (3) established a data management mechanism of intelligent building O&M processes, and (4) formalized an intelligent building O&M decision control platform. Finally, the authors have validated the proposed method using the 2022 Beijing Winter Olympics venue as a case study. The three-dimensional coordinates of various building information are captured based on GNSS automatic monitoring system. This realizes the precise positioning of O&M elements and feedbacks to the twin model of the venue. Through the intelligent analysis and prediction of O&M information, the characteristics of various O&M accidents are obtained. Finally, under the navigation function of GNSS, the processing measures are accurately formulated. Results indicate that the proposed GNSS–DTs-based method could help to achieve intelligent control of large building O&M processes.

Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing.

In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction of the DTs can provide an efficient management and control mechanism for the construction process. This research proposes an intelligent safety assessment method of prestressed steel structures based on DTs. In this research method, the structural safety assessment is divided into two aspects: performance analysis and maintenance. By analyzing the characteristics of the construction safety assessment, a DTs framework for construction safety assessment is built. Driven by the DTs framework, a physical space model and a virtual space model are constructed. On the basis of virtual and actual interaction, multidimensional information fusion of time and space is carried out to realize the analysis of structural safety performance. On this basis, the paper establishes a Bow-tie model for the maintenance modeling of unsafe construction events. Moreover, the theoretical method formed is applied to the construction of a symmetrical structure (wheel–spoke cable truss). The validity of the method is verified by comparing the cable force calculated by the theoretical method and measured on site. The assessment method driven by the DTs ensures the structural safety and improves the intelligence level of safety management and control of the structure construction.

The information collected on large-span prestressed cables by field sensors is susceptible to interference, which leads to inaccurate collection of structural and mechanical parameters of large-span prestressed cables, resulting in misjudgment of structural safety performance. This paper proposes an intelligent judgment method for improving the sensitivity of analyzing mechanical parameters of prestressed cables based on digital twins (DTs). The safety performance of the structure was evaluated by analyzing the mechanical parameters. First, the information during prestressed cable tensioning is dynamically sensed, thereby establishing a multidimensional model of structural analysis. The virtual model is processed by the model modification rule to improve the robustness of the simulation; thus, a DT framework for the sensitivity judgment of the mechanical parameters of the cable is built. In the twin model, the simulation data of the real structure were extracted. Probabilistic analysis was performed using the Dempster–Shafer(D–S) evidence theory to discriminate the sensitivity of mechanical parameters of each cable node under the action of external forces with high accuracy and intelligence. Sensitivity analysis provides a reliable basis for the safety performance assessment of structures. Taking the wheel–spoke-type cable truss as an example, the application of DTs and D–S evidence theory to the sensitivity determination of cable mechanical parameters under temperature fully verified that the proposed intelligent method can effectively evaluate the safety performance of the actual structure.

In this study, to address the problems of multiple dimensions, large scales, complex tension resource scheduling, and strict quality control requirements in the tensioning process of cables in prestressed steel structures, the technical characteristics of digital twins (DTs) and artificial intelligence (AI) are analyzed. An intelligent tensioning of prestressed cables method driven by the integration of DTs and AI is proposed. Based on the current research status of cable tensioning and DTs, combined with the goal of intelligent tensioning, a fusion mechanism for DTs and AI is established and their integration to drive intelligent tensioning of prestressed cables technology is analyzed. In addition, the key issues involved in the construction of an intelligent control center driven by the integration of DTs and AI are discussed. By considering the construction elements of space and time dimensions, the tensioning process is controlled at multiple levels, thereby realizing the intelligent tensioning of prestressed cables. Driven by intelligent tensioning methods, the safety performance evaluation of the intelligent tensioning process is analyzed. Combined with sensing equipment and intelligent algorithms, a high-fidelity twin model and three-dimensional integrated data model are constructed to realize closed-loop control of the intelligent tensioning safety evaluation. Through the study of digital twins and artificial intelligence fusion to drive the intelligent tensioning method for prestressed cables, this study focuses on the analysis of the intelligent evaluation of safety performance. This study provides a reference for fusion applications with DTs and AI in intelligent tensioning of prestressed cables.

Recent advancements in bone implant materials have led to the development of various alloys. In this study, the degradation behavior of the as-cast Mg-3 wt% Zn-1 wt% Ca-0.5 wt% Sr alloy in vitro was investigated using x-ray diffraction (XRD), scanning Kelvin probe force microscopy (SKPFM), and scanning electron microscopy (SEM). Our results demonstrated that the alloy microstructure was composed of α-Mg, a Ca _2 Mg _6 Zn _3 phase, and a Mg _17 Sr _2 phase. The Ca _2 Mg _6 Zn _3 phase, which had the smallest absolute potential, was shown to have cathodic protection, while the α-Mg, which had the largest absolute potential, was shown to prefer corrosion. The in vitro corrosion products of the as-cast alloy were Mg(OH) _2 , a Ca-P compound, and HA. At the beginning of the corrosion, the hydrogen evolution rate of the alloy was fast due to the thin corrosion product layer. With the extension of the corrosion time, the corrosion layer thickened and the hydrogen evolution rate slowed down and stabilized to 1.25 × 10 ^−5 mol cm ^−2 · h . Due to the high concentration of Ca and Mg ions near the second phase, HA was quickly deposited and an ion exchange channel between the solution and the alloy was formed, making it easier for the Mg, Ca, and Sr ions to enter the solution and promote the formation of HA. The hysteresis effect of Sr element was found, that is, Sr ions were released into the solution after etching for a period of time, which promoted the formation of HA and HA-containing Sr (Sr/HA).

We consider all of the transmission eigenvalues for one-dimensional media. We give some conditions under which complex eigenvalues exist. In the case when the index of refraction is constant, it is shown that all the transmission eigenvalues are real if and only if the index of refraction is an odd number or reciprocal of an odd number.