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To meet the requirements of future aircraft for power systems, the turbine inlet temperatures of aero engines are gradually increasing. Ceramic matrix composite (CMC), with its higher thermal limit, has become the preferred material for the turbine blades of variable cycle engines (VCEs). However, the impact of CMC turbine blades on the performance of a VCE is still unknown. In this research project, the comprehensive cooling-efficiency characteristics of CMC are determined through a fluid–solid coupling calculation; a cooling calculation model for turbine blades is established, and cooling airflow solution and control technology (CSCT) for an air system is developed. Additionally, a VCE simulation model is established to analyze the influence of CMC turbine blades on the cooling airflow of the air system and the overall performance of the engine. The results show that, for the design condition, the CMC turbine blade can reduce the cooling airflow of the air system by approximately 10%, and the net thrust is increased by 6.07–7.98%. For the off-design conditions, with the CSCT, the specific fuel consumption can be reduced by 3.06–5.73% while ensuring that the engine net thrust remains unchanged. A comprehensive analysis of the performance for both the design point and off-design points indicates that the use of CMC for high-pressure turbine (HPT) guide vanes and rotor blades yields significant performance benefits, while the performance improvement from the use of CMC for low-pressure turbine (LPT) rotor blades is minimal. [ABSTRACT FROM AUTHOR]

Copyright of Reviews of Geophysics & Planetary Physics is the property of Editorial Office of Reviews of Geophysics & Planetary Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources. [ABSTRACT FROM AUTHOR]

Accurately assessing the erodibility of geomaterials is of great significance for the design of earthen structures and the prevention of the associated failure induced by seepage force. Recently, the un-resolved Computational Fluid Dynamics–Discrete Element Method (CFD–DEM) has been widely used to investigate internal erosion. However, due to the use of wall boundary and the fact that the fixed CFD domain cannot be changed with the soil sample's volume contraction during the erosion test, a larger porosity at the boundary of the CFD domain is commonly formed, resulting in sidewall preferential flow (i.e., relatively more fine particles migrate along the boundary of the DEM domain) and thereby overestimating the soil erodibility. In this study, a new method based on particle boundary is developed to tackle this problem. The newly proposed particle boundary can prevent its particles from erosion via inter-particle bonding and transfer stress from servo walls to the simulated sample. An optimal particle boundary thickness is determined by considering sample contraction and computational efficiency. The performance of the new method was compared with the conventional method and also verified using experimental results. The results show that the newly proposed method has significantly improved the uniformity of fluid velocity distribution. Furthermore, the cumulative eroded mass of fine particles in the new model is approximately 15% lower than in the conventional model. It is convincingly demonstrated that the new method can simulate internal erosion better and give a more accurate assessment of geomaterial erodibility. [ABSTRACT FROM AUTHOR]

Nonlinear state-space models (SSMs) are widely used to model actual industrial processes. System identification is an important method to reduce the uncertainty of the simulation model. In recent years, system identification has been greatly improved with the rise of machine learning. However, there are a few reviews on the latest identification methods based on machine learning. Therefore, this paper focuses on the latest development of identification methods for nonlinear SSM in recent years. In particular, this paper comprehensively compares the identification methods based on traditional methods and machine learning. In addition, according to the type of uncertainty, we divided the paper into the parameter's identification and the identification of unknown parts of the model. Compared with the classification of other reviews, our classification method is clearer. Briefly, this paper organizes the review according to the classification of uncertainty. Each type is extended from offline identification to online identification. Specifically, interval identification and point estimation methods are reviewed for offline parameter identification. For online parameter identification, point estimation methods are reviewed. In the case that the model is partially unknown or black-box, the modeling methods and identification methods are mainly reviewed. In addition to the traditional methods, this paper focuses on the latest progress in the application of machine learning in system recognition. Finally, at the end of the paper, this paper summarizes the existing methods and points out the key problems that still need to be solved. [ABSTRACT FROM AUTHOR]

A majority of the planets in the Solar System are characterized by intrinsic magnetic fields that co-rotate with these celestial bodies. With ongoing advances in human deep-space exploration, magnetic field models have been established for all known Solar System bodies with intrinsic magnetic fields. Planetary magnetic field modeling serves as a fundamental basis for a diverse range of research objectives, including investigations of planetary internal dynamical processes, examinations of planetary evolutionary histories, determinations of the interactions between planetary magnetic fields and the solar wind, and estimations of the shielding effects of planetary magnetic fields against high-energy charged particles. Furthermore, the modeling of planetary magnetic fields represents an interdisciplinary research endeavor, encompassing aspects such as the theories of magnetic field modeling, separation of internal and external magnetic field sources, parameter inversion methods, and equation optimization. In this paper, we provide a comprehensive review of the progress in magnetic field modeling for the Earth, other planets in the Solar System, and also the Moon and Ganymede, one of Jupiter’s moon. We describe the characteristics of the intrinsic magnetic fields of planets and present details of the most accurate global magnetic field models for Solar System planets. Starting with mathematical modeling methods for planetary magnetic fields, we outline the development of magnetic field modeling techniques since the era of Gauss and identify the current hot topics in this field. The review concludes with a forward-looking perspective on the trends in magnetic field modeling research and the potential contributions of China’s future deep-space exploration to the study of planetary magnetic fields.

Marine renewable energy has broad application prospects. Compared with the single energy utilization device, the floating multi-energy combined power generation device can better realize the optimal allocation of resources and reduce the development cost of energy. In order to fully understand the development prospects of multi-energy combined power generation devices, this paper starts with the multi-energy combined power generation integration scheme, analyzes the differences between different types of combined power generation devices, and summarizes their technical characteristics. Next, the research progress of each module analysis method and multi-module coupling dynamics analysis method of the floating multi-energy combined power generation device is clarified and summarized in such aspects as theoretical research and simulation. Finally, the applicable scenarios of the various methods are analyzed and the development trends, problems, and challenges of floating multi-energy combined power generation devices are discussed, aiming to provide references for the subsequent construction of economically feasible floating multi-energy combined power generation devices.

Copper canisters are commonly employed to contain HLW for the long-term, making it crucial to understand how corrosion affects the canister. This study conducted a comparative analysis of two widely used calculation methods for modeling canister corrosion within a 3-D DGR domain. The first method, termed transport-limited corrosion, assumes an immediate sulfide-copper reaction and has been traditionally used due to its conservative nature. The second method, known as the potential-limited corrosion, considers coupled redox reactions at the canister surface and computes corrosion rates through anodic current density. From the results, we found that the edge of the canister geometry and the omission of electrochemical kinetics impose critical limitations with the transport-limited corrosion method. These limitations include the singularity problem, excessive sensitivity to the curvature of the canister's edge, and an inability to evaluate the distribution of corrosion rate over the canister surface as a function of the sulfide concentration. On the other hand, the potential-limited corrosion method avoided the limitations found in the other method. Since the factors relating to these limitations are critical to the design and optimization of the copper disposal canister, careful consideration when selecting appropriate calculation methods for corrosion will be required.

In the article, a state analysis of the organic carbon-containing additives use in fire fighting has been carried out. Negative environmental effects when using fluorine-containing short- and long-chain surfactants, which can act as both a fire extinguishing agent and its decomposition product, have been noted. As an important direction for further evolution in the field of fire extinguishing agents, the use of oxysilanes and gel systems based on liquid glass as environmentally friendly compounds has been noted. The prospects and environmental friendliness of the use of acoustic effects in extinguishing fires have been noted. The addition of small amounts of inorganic and organic compounds is regarded as an inexpensive and effective method to increase the fire fighting properties of water. In this work, the ecological characteristics of a number of organic compounds used in fire fighting as thickeners of aqueous solutions and the reduction of their surface tension were studied. Alginic acid has been shown to be the most environmentally friendly water additive among the investigated organic carbon-containing compounds used in fire fighting. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The proposed generalized electric spring (G-ES) topology effectively reduces the redundancy caused by the parallel structure of multiple electric springs in a microgrid system. A modeling method for the G-ES is urgently needed to accurately determine the G-ES parameters and to evaluate its transient operation characteristics. The correspondence between Hamilton's principle in mechanics and electricity is introduced, and the feasibility of Hamilton's principle under the smart load topology is verified. The modeling method of the G-ES under Hamilton's principle is discussed, followed by the application of repetitive control strategies on the G-ES. The filter parameters are designed using the normalization method and a simplified parameter design using repetitive controllers was applied. This article introduces the impedance analysis method into the generalized electric spring to analyze its stability under weak network conditions. This work also derives the stability judgment criteria for generalized electric springs. Finally, the feasibility of modeling the G-ES based on the Hamilton's principle was verified using MATLAB and a DSP hardware system. In addition, the consistency between the G-ES and a single intelligent load was verified under the Hamilton's modeling principle. The G-ES stability and dynamic performance under this modeling method meet industry requirements. [ABSTRACT FROM AUTHOR]

To meet the requirements of future aircraft for power systems, the turbine inlet temperatures of aero engines are gradually increasing. Ceramic matrix composite (CMC), with its higher thermal limit, has become the preferred material for the turbine blades of variable cycle engines (VCEs). However, the impact of CMC turbine blades on the performance of a VCE is still unknown. In this research project, the comprehensive cooling-efficiency characteristics of CMC are determined through a fluid–solid coupling calculation; a cooling calculation model for turbine blades is established, and cooling airflow solution and control technology (CSCT) for an air system is developed. Additionally, a VCE simulation model is established to analyze the influence of CMC turbine blades on the cooling airflow of the air system and the overall performance of the engine. The results show that, for the design condition, the CMC turbine blade can reduce the cooling airflow of the air system by approximately 10%, and the net thrust is increased by 6.07–7.98%. For the off-design conditions, with the CSCT, the specific fuel consumption can be reduced by 3.06–5.73% while ensuring that the engine net thrust remains unchanged. A comprehensive analysis of the performance for both the design point and off-design points indicates that the use of CMC for high-pressure turbine (HPT) guide vanes and rotor blades yields significant performance benefits, while the performance improvement from the use of CMC for low-pressure turbine (LPT) rotor blades is minimal.

In many applications of fuzzy modeling, not only high accuracy but also high interpretability is needed. It is usually difficult to determine the number of fuzzy rules. This paper seeks to address these issues and decrease the computational cost. Considering that pyramid fuzzy systems (PFSs) have been developed to improve the accuracy while simplifying the structure of fuzzy systems. On this basis, a hierarchical cluster-based optimization algorithm of hierarchical pyramid fuzzy system (HPFS) modeling is proposed, and a T-S-type PFS is considered as its subsystem. First, a new clustering method based on the K-means algorithm is applied to partition the input universe in the learning process of the initial structure. Unlike a traditional fuzzy system with a predefined rule number, the number of fuzzy rules (i.e., the cluster size) can be defined automatically. Secondly, a novel hierarchical genetic algorithm (HGA) that produces a tree-like chromosome structure, i.e., a natural structure identical to HPFS that combines several low-dimensional T-S-type PFSs, is proposed. To verify and validate the effectiveness of the proposed method, four experiments are performed, and detailed comparisons with other methods are given. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The rabbit model of steroid-induced osteonecrosis of femoral head is the most commonly used animal model of femoral head necrosis. The pathological changes of the femoral head are close to clinical practice, however, the conditions, methods and evaluation standards of animal models reported in and outside China are not uniform, which leads to the low scientific value of animal models and is difficult to popularize. This study aimed to clarify the influence of different mold-making conditions on the establishment of steroid-induced osteonecrosis of femoral head rabbit model and analyze the appropriate conditions for the successful model establishment. METHODS: We searched the CNKI, WanFang, VIP, CBM, WoS, PubMed and EMbsae databases for the literature on the modeling of steroid-induced osteonecrosis of femoral head rabbits up to April 1, 2022, completed the screening of the literature according to the inclusion and exclusion criteria and literature quality evaluation, and extracted the outcome index data in the literature. RevMan Stata and ADDIS statistical software were used to conduct a metaanalysis of the included data. RESULTS: (1) A total of 82 articles with 1 366 rabbits were included in the study. The steroid-induced osteonecrosis of femoral head modeling methods were divided into three types: steroid-alone method, steroid combined lipopolysaccharide method and steroid combined serum method. Among these, 33 articles used steroid-alone method; 20 articles used steroid combined lipopolysaccharide method; 29 articles used steroid combined serum method. (2) Meta-analysis results showed that the three modeling methods significantly increased the rate of empty bone lacunae in the femoral head of steroid-induced osteonecrosis of femoral head rabbits (P < 0.001), and significantly decreased the ratio of the trabecular bone area in the femoral head of steroid-induced osteonecrosis of femoral head rabbits (P < 0.001). The order of empty bone lacunae rate of each modeling method was: steroid combined with lipopolysaccharide method > steroid-alone method > steroid combined with serum method > normal group, and the order of trabecular bone area rate of each modeling method was: normal group > steroid combined with serum method > steroid-alone method > steroid combined with lipopolysaccharide method. (3) The results of subgroup analysis suggested that the rate of empty bone lacunae in the rabbit model induced by steroid alone might be related to the rabbit variety and the type of steroid used for modeling (difference between groups P < 0.05), in which the combined effect amount of New Zealand white rabbits was higher than that of Chinese white rabbits (P < 0.05) and Japanese white rabbits, and the combined effect amount of dexamethasone was higher than that of other steroids. The rate of empty bone lacunae induced by steroid combined with lipopolysaccharide was related to the administration mode of lipopolysaccharide and the type of steroid (P < 0.05), among which the combined effect of methylprednisolone sodium succinate was significantly higher than that of other steroids (P < 0.05), and the combined effect of prednisolone was significantly lower than that of other steroids (P < 0.05). The combined effect of lipopolysaccharide 100 μg/kg × twice was significantly lower than 10 μg/kg × twice and 50 μg/kg × twice (P < 0.05). The rate of empty bone lacunae in the model induced by steroid combined with serum was related to serum dose and steroid type (P < 0.05), among which the combined effect amount of dexamethasone sodium phosphate was significantly higher than other steroid types (P < 0.05), and the combined effect amount of dexamethasone was significantly lower than other steroid types (P < 0.05); the combined effect amount of serum “10 mL/kg+6 mL/kg” combined dose was lower than other serum doses (P < 0.05). CONCLUSION: (1) With the rate of empty bone lacunae and the ratio of trabecular bone area as the judgment standard for the successful establishment of the model, the three modeling methods can successfully construct the rabbit steroid-induced osteonecrosis of femoral head model, of which the steroid combined with lipopolysaccharide method is the best. (2) New Zealand white rabbits and dexamethasone are recommended when selecting the steroid-alone method. Methylprednisolone sodium succinate and low-dose lipopolysaccharide are recommended when selecting the steroid combined with lipopolysaccharide method. Dexamethasone sodium phosphate is recommended when selecting the steroid combined with serum modeling method. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sarcopenia is a progressive, generalized skeletal muscle disease that is closely related to the occurrence of osteoarthritis, fractures, limb disability and death in the elderly. Establishing animal models of sarcopenia is essential to understand the pathophysiology of sarcopenia and to identify effective treatment strategies. OBJECTIVE: To review the evaluation criteria of mouse models of sarcopenia and the modeling methods of mouse models of sarcopenia, and to analyze and compare the advantages and disadvantages of various modeling methods, in order to provide reference for the research and diagnosis and treatment of sarcopenia. METHODS: “Sarcopenia, skeletal muscle aging, mouse model, animal model” in Chinese and English were used as Chinese and English search terms, respectively. The search formula was “(sarcopenia OR skeletal muscle aging) AND (mouse model OR animal model).” CNKI, WanFang and PubMed were searched for related articles published from January 2010 and October 2022. A total of 59 articles were finally included for analysis. RESULTS AND CONCLUSION: There is a faster modeling time in SAMP8 mice and the type of muscle atrophy is consistent with that of patients with sarcopenia. Therefore, it is an ideal model. Although the surgical method can successfully induce muscle atrophy, it requires precise surgical operation, which is difficult and time-consuming. Hindlimb suspension modeling in mice is similar to that of the elderly and can be regarded as an effective model of senile sarcopenia. Although reagent injection molding is simple to perform, both the dose and number of days of administration of reagents are not clear and need to be further research direction. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: the article is devoted to the problem of studying convicted women’s plans for the future and using a modeling method to shape a personal future of female convicts in conditions of imprisonment. Purpose: to contribute to the construction of a positive personal future of female convicts on the basis of the use of the modeling method. Methods: theoretical analysis of literature, modeling methods, conversation, and classification. Results: to analyze convicted women’s plans for the personal future, we developed the following criteria: by time duration (long-term, medium-term, current), by degree of parameter determination (deterministic and probabilistic), by planned actions (reactive, inactive, preactive). During individual conversations, three groups of life plans of first-time convicted women of various ages for their personal future were identified: 1) longterm, deterministic and positive personal plans for the future; 2) medium-term or current, probable, inactive; 3) there is no model of future life. Drawing up a detailed plan, modeling the prospects of life is the basic method of a person’s influence on their future. This makes it possible to more effectively build a correctional process for a more successful rehabilitation of the personality and its adaptation in society after release. Conclusion: plans for the future are all kinds of long-term goals (personal or professional), considered as a real benchmark of action. The personal future model is an accurate description of the life planning process, its sketch or reference point. In this study, the use of the modeling method in drawing up a personal future is a practice-oriented embodiment of life plans of convicted women, which allows them to see their future life as a whole process, as well as to isolate all components in their interrelation. Based on this, it is necessary to carry out specially organized work to study life plans of convicts, include them in various correctional programs in order to revise their views, realize the value of life, and model a positive personal future.

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.

Copyright of Mining & Metallurgy (10057854) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a single-event transient model based on the effective space charge for MOSFETs is proposed. The physical process of deposited and moving charges is analyzed in detail. The influence of deposited charges on the electric field in the depletion region is investigated. The electric field decreases in a short time period due to the neutralization of the space charge. After that, the electric field increases first and then decreases when the deposited charge is moved out. The movement of the deposited charge in the body mainly occurs through ambipolar diffusion because of its high-density electrons and holes. The derivation of the variation in electric field in the depletion region is modeled in the physical process according to the analysis. In combination with the ambipolar diffusion model of excessive charge in the body, a physics-based model is built to describe the current pulse in the drain terminal. The proposed model takes into account the influence of multiple factors, like linear-energy transfer (LET), drain bias, and the doping concentration of the well. The model results are validated with the simulation results from TCAD. Through calculation, the root-mean-square error (RMSE) between the simulation and model is less than 3.7 × 10−4, which means that the model matches well with the TCAD results. Moreover, a CMOS inverter is simulated using TCAD and SPICE to validate the applicability of the proposed model in a circuit-level simulation. The proposed model captures the variation in net voltage in the inverter. The simulation result obviously shows the current plateau effect, while the relative error of the pulse width is 23.5%, much better than that in the classic model. In comparison with the classic model, the proposed model provides an RMSE of 7.59 × 10−5 for the output current curve and an RMSE of 0.158 for the output voltage curve, which are significantly better than those of the classic model. In the meantime, the proposed model does not produce extra simulation time compared with the classic double exponential model. So, the model has potential for application to flow estimation of the soft error rate (SER) at the circuit level to improve the accuracy of the results. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the grey models, the GM(1,1) model is an important type of prediction model. The traditional grey GM(1,1) model has good prediction results in the case the original data show exponential variations at a slow rate. However, in practical problems, although showing exponential variations or approximately exponential variations, original data vary very fast sometimes. In these cases, the traditional grey GM(1,1) model tends to have poor prediction accuracy, mainly because the data fails to meet the laws presented by the traditional model. Therefore, the paper makes improvements in the following two aspects: first, the paper transforms the traditional accumulated generating sequence of original data; second, the paper extends the traditional grey model's structure, i.e., building a grey EGM(1,1,Σe^(ck)) model. The paper offers the parameter optimization method of the grey EGM(1,1,Σe^(ck)) model. Using the novel modeling method proposed, the paper builds the grey EGM(1,1,Σe^(ck)) models for China's total electricity consumption and China's GDP per capita, respectively, in the final section. Results show that the models built with the proposed modeling method have high simulation precision and prediction precision. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stress cardiomyopathy (SC) is an acute cardiomyopathy caused by intense mental stimulation or physical stress. Currently, few interventions are effective in reducing mortality, improving prognosis, or preventing recurrence in acute or chronic stages of SC. Therefore, it is particularly important to establish a reliable and effective SC animal model to study the pathogenesis and prevention of SC. In this paper, the preparation methods and evaluation indexes of SC models at home and abroad were reviewed, including the operation details of catecholamine hormone injection method, binding method, binding combined with electroshock method, binding combined with dehydration method, vagus nerve electrical stimulation and other modeling methods, as well as evaluation indexes such as open field test, cardiac ultrasound, electrocardiogram and ELISA, striving to provide useful reference for model selection and establishment of SC research.

BACKGROUND: Adolescent idiopathic scoliosis has a high prevalence and complex condition in China and has a significant risk to the physical and mental health of adolescents. The establishment of finite element models based on patient medical images can effectively analyze spinal stresses, simulate the recovery effects of different treatments, and provide a scientific basis for the selection of patient treatment modalities. However, there are many different ways to build finite element models and no unified standard has been formed yet; the quality of models varies and the validation methods need to be improved. OBJECTIVE: The current status of modeling studies in adolescent idiopathic scoliosis in recent years is reviewed in terms of software selection, modeling of key structures (vertebral body, disc, ligament, and trunk models), and material allocation. The research progress of model validation is outlined from the perspectives of geometry, comparison of existing literature, and experimental validation. The deficiencies and development directions of finite element models are summarized to provide references for related studies. METHODS: The first author searched the Web of Science and China National Knowledge Infrastructure databases in December 2021 for relevant literature published from January 2010 to the present, using “adolescent idiopathic scoliosis, finite element, biomechanics” as Chinese and English search terms. The highly cited authoritative articles were traced, and finally 83 articles were included for analysis. RESULTS AND CONCLUSION: (1) Finite element analysis method can reconstruct the three-dimensional model of spine, simulate the physiological state of spine through biomechanical analysis, and provide theoretical basis and scheme reference for conservative treatment and surgical treatment of adolescent idiopathic scoliosis. (2) The establishment method of finite element model in China is different from that outside China. Domestic researchers use full spinal CT to reconstruct the vertebrae through reverse engineering and establish intervertebral discs, ligaments, articular cartilage endplate, up and down structure according to the model of vertebrae. However, the foreign researchers use multiple plane X-ray devices such as the EOS imaging system, which is lack of description of the model establishment process. (3) Although the specific software selection in the process of model building is different, the software classification includes medical image processing software, three-dimensional modeling software and finite element analysis software. (4) The finite element material assignment of vertebrae and intervertebral discs has formed a unified standard, but the material assignment of ligament model is also different due to different unit settings, and the assignment of ligament will significantly affect the results of the model. (5) The verification methods of finite element models mainly include geometric verification compared with original medical images, mechanical verification compared with existing literature results and clinical trial verification of finite element analysis results. [ABSTRACT FROM AUTHOR]

Proton Exchange Membrane Fuel cell (PEMFC) model is helpful to understand the physical and chemical properties of the PEMFC and predict the performance of the PEMFC under different working conditions. Moreover, it can also provide essential references for evaluating new designs, new materials, and new control strategies. This paper specifically discusses PEMFC modeling at different scales, including single cell scale, stack scale and system scale. In addition, the main modeling approaches of the PEMFC are introduced in-depth, including mechanism approach, empirical approach, semi-empirical approach, equivalent circuit approach, and data-driven approach. The relationship between modeling approaches and different modeling scales is also discussed. At the same time, according to the different characteristics of the model, the modeling research in the last decade is summarized. On this basis, the development trend of the PEMFC modeling research is further analyzed by the statistical analysis method. • Latest developments in the modeling of the PEMFC are reviewed. • The correlation of modeling between models at different scales is described. • Detailed summary and comparison of existing models by statistical analysis. • The potential research areas and unsolved problems of modeling are discussed. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present research, a method to model the pose error of the machine tool feed system is proposed based on the principle of minimum potential energy. Firstly, the force-moment equilibrium equation of the feed system is established by mechanical analysis. Secondly, the force of the single slider is analyzed based on Hertzian contact theory, and the force state of the slider under the action of the guideway error is obtained. The elastic displacements of the single slider in the horizontal and vertical directions are solved based on the principle of the minimal potential energy. Subsequently, the pose error of the feeding system is obtained by using the plane fitting principle. Finally, the correctness of the proposed method is verified through experiments, and the experimental results are in good agreement with the theoretical results.

This article uses BIM technology to simulate HVAC in a commercial building. And use fractional differential equations to verify the set temperature, humidity, and other related parameters. The experiment takes an air-conditioned room model as the research object and uses the thermodynamic characteristics to establish a dynamic mathematical model of differential equations. Research shows that the multiple electric heat pump equipment aggregation groups discussed in this article are a good resource for user-side demand response. The air-side flow rate disturbance of the building HVAC has the greatest impact on the heat transfer of the surface cooler. As the air velocity increases, the rate of increase in heat exchange will decrease.

Abstract A modeling method of CNC machine tools’ spatial geometric error is proposed based on two-dimensional angle error in this paper. Different from traditional modeling method, this method takes into account the effect of additional angle errors because the machine tool’s angle errors in the system of the lower guide rail will be passed on to slip board structure to the upper guide rail system. Firstly, after analyzing the principle of two-dimensional angle error, an optimized error compensation model of CNC machine tools’ spatial geometric error contains two-dimensional angle error is established through the traditional homogeneous coordinate transformation matrix method (HTM). Further, by applying the experiment, the compensation effect of the model is verified through comparing the body diagonal errors of CNC machine tools before and after compensation. Experiment results show that the body-diagonal accuracy after compensation based on the optimized model is 87.73% higher than that before compensation, and 54.82% higher than that of the traditional model after compensation. Article Highlights The highlight of the study reported here is threefold: Firstly, the principle of two-dimensional angle error is analyzed. Secondly, an optimized spatial geometric error compensation model of CNC machine tool is established based on the principle of two-dimensional angle error. Thirdly, the compensation effect of the optimized compensation model is verified by comparing the bodies diagonal precision of the machine tool.

The image of the city as one of the most important elements of modern post-industrial reality is an essential element in the world picture of any individual. The research aims to study the peculiarities of perception and verbal representation of the image of Kaliningrad by the students of the Immanuel Kant Baltic Federal University. The objectives of the research determined the choice of a comprehensive methodology, which included the method of unfini­shed sentences, as well as the methods of interpretation and modelling. As a result of this re­search, four interrelated constituents of the image of Kaliningrad were determined — external (architectural and landscape), cultural and historical, anthropological, social and household. The verbal representation of their structural and content characteristics was illustrated using the results of a survey based on the method of unfinished sentences, which was developed and con­ducted by the author.

Background: There is an immense debate about the factors that could limit the adoption of agroforestry systems. However, one of the most important is the generation of scientific information that supports the viability and benefits of the proposed techniques. Statistical analysis: This work used the Latent Dirichlet Allocation (LDA) modeling method to identify and interpret scientific information on topics in relation to existing categories in a set of documents. It also used the HJ-Biplot method to determine the relationship between the analyzed topics, taking into consideration the years under study. Results: A review of the literature was conducted in this study and a total of 9794 abstracts of scientific articles published between 1993 and 2022 were obtained. The United States, India, Brazil, the United Kingdom, and Germany were the five countries that published the largest number of studies about agroforestry, particularly soil organic carbon, which was the most studied case. The five more frequently studied topics were: soil organic carbon, adoption of agroforestry practices, biodiversity, climatic change global policies, and carbon and climatic change. Conclusion: the LDA and HJ-Biplot statistical methods are useful tools for determining topicality in text analysis in agroforestry and related topics. [ABSTRACT FROM AUTHOR]

This study aims to investigate the seismic behavior of concrete-filled steel tubular (CFST)-laced piers; after verifying the model through engineering tests, the simplified finite element models (S-FEM) and refined ones (R-FEM) with CFST-laced piers are developed in this manuscript, respectively. Through comparison, it is found that the S-FEM can effectively improve analyzing efficiency while meeting the requirements of engineering analysis accuracy. In addition, the seismic response of assembled flange-connected CFST-laced piers bridge was studied based on the S-FEM, and different structural parameters, including pier height, axial compression ratios, steel ratios of CFST columns, steel lacing tube arrangement, and longitudinal slope, are considered to optimize the bridge design scheme. Results indicate that the parameters of 0.1 axial pressure ratios and 1:30 longitudinal slope show superior seismic performance. Meanwhile, the peak axial force and peak bending moment of CFST column limbs occur at the pier bottom, and the flanges, which are subject to larger bending moments, are generally located at the two connection positions above the pier bottom. [ABSTRACT FROM AUTHOR]

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In addition to the waste of resources and economic losses, environmental damage by gas flaring is widespread and significant. Since flaring the associated gas gives no added value in exchange for its pollution and greenhouse gas (GHG) emissions, it could be identified as a top priority for mitigation. Iran is the third gas flaring country after Russia and Iraq among those facing this issue, and is responsible for 12.1% of the world's gas flaring. While the necessity of developing a method for the precise estimation of flaring GHG emissions is clear, especially for evaluating the result of countries' efforts to meet their nationally determined contribution target, there are huge uncertainties and discrepancies in the values of emission factors among various data sources due to the lack of actual measurements of the volume and diversity of the composition of flare gas. This study aimed to fill the gap in providing authentic data on Iran's gas flaring GHG and air pollutant emissions by developing a model based on satellite data on flare volumes, gas compositions, and combustion equations. Our results revealed that based on 2021 data on flaring volume, Iranian gas flares are emitting approximately 50 million metric tons of CO2 equivalent to the atmosphere annually, which could be reduced to 43 by only enhancing the flares' efficiency. It accounted for 5.5%–6% of the total GHG emissions of the country. Integr Environ Assess Manag 2023;19:735–748. © 2022 SETAC. Key Points: Iranian gas flares are emitting some 50 million metric tons of CO2 equivalent to the atmosphere annually, accounting for 5.5%–6% of the country's total GHG emissions.Slight increases in the flare's destruction efficiency can cause a huge decrease in its value of global warming potential.The gas flare's emission factors suggested by USEPA would be reliable only if a combustion efficiency of 98% and a destruction efficiency of 99% were considered, while emission factors suggested by Ecoinvent seem to be too optimistic to be true.By taking advantage of fair access to financial opportunities and international cooperation envisaged through the Convention (COP21), the Paris Agreement could be a historic opportunity for the complete elimination of gas flaring from Iran's upstream and downstream oil industries. [ABSTRACT FROM AUTHOR]

The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R2c, RMSEc, R2v, RMSEv, and RPD are 0.9487, 0.1663 kg·m−2, 0.7335, 0.3600 kg·m−2, and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology. [ABSTRACT FROM AUTHOR]

Various emerging IT technologies are widely used in the service industry. Thus, an increasing number of new service models have also emerged, including the Internet of Services (IoS). The IoS supports network-based service collaboration and transactions among various service participants from different domains and different organizations, and it is expected to deliver the maximum service value to all stakeholders. To describe the cross-domain, cross-organization, and cross-value chain characteristics of the IoS from a value perspective and support subsequent analysis of the value network and optimization of the IoS, this paper proposes a semi-automatic modeling method for a IoS-oriented value network based on external public data. We first propose an intelligent domain entity recognition algorithm based on multidimensional web data to help value network modelers realize effective and efficient recognition of service participants. Then, based on external news data, an intelligent domain relationship extraction algorithm that combines the Bert + BiLSTM + CRF model with the LightGBM model is proposed to effectively and efficiently identify the value exchange relationships among service participants, thereby forming an IoS-oriented value network model (IVN). Finally, to extend the cross-domain semantics of the IVN and support analysis of the IVN, we present a domain-specific value chain extraction algorithm based on typical patterns to complete the cross-domain semantic annotation of the IVN. The effectiveness and efficiency of the proposed methods and algorithms are validated through experimental analysis and a case study, which can be of great help in IVN modeling. [ABSTRACT FROM AUTHOR]

Stress cardiomyopathy (SC) is an acute cardiomyopathy caused by intense mental stimulation or physical stress. Currently, few interventions are effective in reducing mortality, improving prognosis, or preventing recurrence in acute or chronic stages of SC. Therefore, it is particularly important to establish a reliable and effective SC animal model to study the pathogenesis and prevention of SC. In this paper, the preparation methods and evaluation indexes of SC models at home and abroad were reviewed, including the operation details of catecholamine hormone injection method, binding method, binding combined with electroshock method, binding combined with dehydration method, vagus nerve electrical stimulation and other modeling methods, as well as evaluation indexes such as open field test, cardiac ultrasound, electrocardiogram and ELISA, striving to provide useful reference for model selection and establishment of SC research. [ABSTRACT FROM AUTHOR]

Swarm robots are composed of a certain number of physical robots. Through interaction and cooperation, individuals in this kind of system can sprout robust, scalable and flexible swarm intelligence behavior. Behavior modeling of swarm robots is one of the basic methods for scientific analysis of such systems, and it is also an important basis for its capability evaluation. The research and analysis of behavior model of swarm robots is helpful to understand its behavior mechanism. Firstly, this paper summarizes the main characteristics of swarm robots, and lists and summarizes several typical spatial cooperation behaviors of swarm robots. On this basis, the spatial cooperation behavior of swarm robots is classified, and the modeling methods of spatial cooperation behavior of cluster robots are summarized and expounded, including graph theory, bionic model, dynamic model and learning model. Various modeling methods are analyzed and compared from the multi-dimensional aspects of method overview, application examples and applicable scenes. Finally, the problems of spatial cooperative behavior modeling of swarm robots to be further studied are put forward, and possible research directions in the future are prospected, so as to better support the evaluation and optimization design of swarm robots. The purpose of this paper is to enable relevant researchers to comprehensively and systematically understand the spatial cooperative behavior modeling method of swarm robots.

—The global domain fluid-thermal characteristics study is challenging to perform due to the complicated ventilation arrangement and enormous volume for the large capacity electric machine. This paper uses the world's largest capacity non-superconducting low-speed induction motor, 6.5 MW, as the research object and proposes a global domain fluid-thermal characteristics calculation method. Firstly, the global domain fluid field model of the inner air path is established and solved. Then, the motor is divided into five subdomains according to the ventilation structure. The flow and pressure obtained from fluid field solutions are used as coupling parameters to couple each subdomain model, and an iterative solution is carried out based on the boundary condition continuity of the adjacent subdomains. The temperature distribution of each subdomain is obtained when the temperature difference is less than 0.1%. Finally, an experimental platform is built to verify the results. The maximum relative error between calculated and measured values is 1.67% for temperature and 4.92% for fluid velocity, which verifies the developed model's rationality and the proposed method's validity.