Parametric modeling and analysis of intake phases for side-ported Wankel rotary engines.

This research presents a novel port parametric modeling technique using three-dimensional computational fluid dynamics for the design and optimization of intake and exhaust phases in side-ported Wankel rotary engines (WREs). Definitions for the port phases encompass parameters such as port start opening, port full opening, port start closing, and port full closing timings. The four port phase control arcs are obtained by translating and rotating the rotor flank to satisfy the high control accuracy. Further, the shape of the port is further smoothed and varied by four auxiliary circular arcs. Moreover, the influence of port full closing timing and the size of auxiliary circular arcs (R1, and R3) on the intake characteristics is studied. The results show that the novel method can flexibly and effectively control the phases and shapes. The early port full closing timing reduces fluid backflow and improves volumetric efficiency (VE) but increases intake loss (IL). The small size of R1 facilitates to increase the VE and reduce IL. A larger or smaller size of R3 is not conducive to reducing IL, and the smaller size of R3 improves the VE. The novel generation method proposed in this paper provides a theoretical basis to optimize the design of various sizes of side-ported WREs and guidance for practical manufacturing.

An Improved Model of Single-Event Transients Based on Effective Space Charge for Metal-Oxide-Semiconductor Field-Effect Transistor.

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.

Heat Transfer Enhancement of Controllable Aspect Ratio Fractal Channel.

Thermal management technology is a major challenge in high-end equipment. The demand for high-efficiency heat sinks has increased. In this study, a controllable aspect ratio (AR) fractal channel (CARFC) heat sink is proposed to enhance thermal performance. First, a parameterized modeling method for the CARFC is constructed. Fractal networks are constructed using control points and bifurcation points. The geometric size of each level channel is determined by considering the AR of each level channel. A mathematical relationship is established between the two parts. Under constant heat flow boundary, the effect of aspect ratio on the fractal channel performance is studied by numerical simulation. The influence of the inlet AR on the performance of the fractal channels is studied. Then, the impact of the AR of each level channel on the performance of the CARFC is studied. The results show that the AR of the inlet has an obvious effect on the performance of the fractal channel. The CARFC results show that the AR of each level channel influences the thermal performance of the heat sink, especially the aspect ratio k0 and k1. Compared with only changing the aspect ratio of the inlet, the CARFC has better performance; the peak temperature and temperature difference are reduced by 9.62% and 26.57%, respectively. The CARFC requires less coolant to meet the same thermal demand, which is of great significance in the development of lightweight equipment.

Comorbidity network analysis using graphical models for electronic health records.

Importance: The comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality. Objective: The main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation. Method: This cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients. Results: Out of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between "poisoning by psychotropic agents" and "accidental poisoning by tranquilizers" (logOR 8.16), and the most connected diagnosis was "disorders of fluid, electrolyte, and acid-base balance" (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, "diagnoses of mitral and aortic valve" and "other rheumatic heart disease" (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, "disorders of fluid, electrolyte, and acid-base balance" was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes. Conclusion and relevance: Our graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.

Analysis and multiple index evaluation of SPO distribution of bone traction needles for pelvic fracture reduction.

For robot-assisted pelvic fracture reduction, at least two bone needles need to be inserted into the ilium of the affected pelvis, and the robot clamping device is connected with the bone needles. The biomechanical properties of the pelvic musculoskeletal tissues are different with the different Spatial Position and Orientation (SPO) of the bone needles. In order to determine the optimal SPO of bone needle pairs, the constraints between the bone needles and the pelvis are analyzed, and the SPO vectors of 150 groups bone needles are obtained by the KNN-hierarchical clustering method; a batch modeling method of bone needles with different SPO is proposed. 150 finite element models of damaged pelvic musculoskeletal tissue with different SPO of bone needles are established and simulated. The stress and strain distribution homogenization of musculoskeletal tissue with bone needles as evaluation index, the simulation results of 150 models are evaluated. Results show that, the anterior superior iliac spine and the anterior inferior iliac spine are suitable regions to place bone needles in the pelvis, and the optimal distribution of the needle combination is found in this region. The overall stress and strain distribution of the damaged pelvic musculoskeletal tissue under the large reduction force is the best.

Modeling ultrasonic wave fields scattered by flaws using a quasi-Monte Carlo method: Theoretical method and experimental verification.

The modeling and visualization of wave fields scattered by flaws can be helpful in terms of guiding the testing and evaluation of flaws using an ultrasonic nondestructive method. In this work, the ultrasonic scattering of wave fields from flaws with different shapes is modeled using a quasi-Monte Carlo (QMC) method and measured through experiments for verification. The incident wave fields generated by a transducer can be modeled using the Rayleigh integral expression and calculated using the QMC method. When the size of the flaw is much larger than the wavelength, the incident wave over the lit portion of flaw can be treated as the source for the scattering of wave fields, and these wave fields can also be modeled using the proposed QMC method. In this paper, water is treated as the material and an embedded solid component is considered as the flaw. Numerical examples and results are presented for flaws with different shapes and sizes, and the properties of these scattering wave fields are analyzed and discussed. Experiments are performed to measure the scattering wave fields using a needle transducer, and it is shown that the results agree with the simulations, thus verifying the proposed modeling method. The work presented here can assist in understanding the wave-flaw interaction and can help in optimizing ultrasonic nondestructive testing.

The importance of avoiding time-dependent bias when testing the prognostic value of an intervening event - Two acute cellular rejection examples in intestinal transplantation.

In testing the prognostic value of the occurrence of an intervening event (clinical event that occurs posttransplant), 3 proper statistical methodologies for testing its prognostic value exist (time-dependent covariate, landmark, and semi-Markov modeling methods). However, time-dependent bias has appeared in many clinical reports, whereby the intervening event is statistically treated as a baseline variable (as if it occurred at transplant). Using a single-center cohort of 445 intestinal transplant cases to test the prognostic value of first acute cellular rejection (ACR) and severe (grade of) ACR on the hazard rate of developing graft loss, we demonstrate how the inclusion of such time-dependent bias can lead to severe underestimation of the true hazard ratio (HR). The (statistically more powerful) time-dependent covariate method in Cox's multivariable model yielded significantly unfavorable effects of first ACR (P < .0001; HR = 2.492) and severe ACR (P < .0001; HR = 4.531). In contrast, when using the time-dependent biased approach, multivariable analysis yielded an incorrect conclusion for the prognostic value of first ACR (P = .31, HR = 0.877, 35.2% of 2.492) and a much smaller estimated effect of severe ACR (P = .0008; HR = 1.589; 35.1% of 4.531). In conclusion, this study demonstrates the importance of avoiding time-dependent bias when testing the prognostic value of an intervening event.

[Experimental study of improved sclerotherapy injection in rat model of vertebral arteriocervical spondylopathy].

OBJECTIVE: To improve the rat model of cervical spondylosis of vertebral artery type (CSA) induced by injecting sclerosing agent. To evaluate the efficacy of injecting sclerosing agent to induce CSA. METHODS: Forty Health SPF SD rats(20 males and 20 females), were randomly divided into two groups:the model group (20) and the blank group (20). All the animals were followed up for 4 weeks for the observation of general situation, transcranial Doppler(TCD) detection of blood flow velocity, pulsatility index and resistive index of the vertebral artery, measurement of mental distress by open-field test. RESULTS: One to two days after establish the animal model, rats in the model group appeared apathetic with decreased autonomic activities, trembling, squinting, increased eye excrement, etc., and no rats died during the experiment. The mean blood flow velocity of the model group was lower than that of the blank group (P<0.05), and the pulsatilit index and resistive index of the model group were higher than that of the blank group (P<0.05). The mental distress of the model group was significantly higher than that of the blank group. CONCLUSION: The modified injection of sclerosing agent is a practical method to establish the rat model of CSA, with high success rate, high stability, low mortality and simple operation.

Research progress and future study on physicochemical, nutritional, and structural characteristics of canola and rapeseed feedstocks and co-products from bio-oil processing and nutrient modeling evaluation methods.

This article aims to review research progress and provide future study on physicochemical, nutritional, and molecular structural characteristics of canola and rapeseed feedstocks and co-products from bio-oil processing and nutrient modeling evaluation methods. The review includes Canola oil seed production, utilization and features; Rapeseed oil seed production and canola oil seed import in China; Bio-processing, co-products and conventional evaluation methods; Modeling methods for evaluation of truly absorbed protein supply from canola feedstock and co-products. The article provides our current research in feedstocks and co-products from bio-oil processing which include Characterization of chemical and nutrient profiles and ruminal degradation and intestinal digestion; Revealing intrinsic molecular structures and relationship between the molecular structure spectra features and nutrient supply from feedstocks and co-products using advanced vibrational molecular spectroscopy technique. The study focused on advanced vibrational molecular spectroscopy which can be used as a fast tool to study molecular structure features of feedstock and co-products from bio-oil processing. The article also provides future in depth study areas. This review provides an insight as how to use advanced vibrational molecular spectroscopy for in-depth analysis of the relationship between molecular structure spectral feature and nutrition delivery from canola feedstocks and co-products from bio-oil processing.

Flood risk assessment by using an interpretative structural modeling based Bayesian network approach (ISM-BN): An urban-level analysis of Shenzhen, China.

With increasingly uncertain environmental conditions under global change, it is rather important for water security management to evaluate the flood risk, which is influenced by the compound effect of severe weather events and strong anthropogenic activities. In this paper, a risk assessment model in the framework of Bayesian network (BN) was proposed through incorporating with the Interpretative Structural Modeling method (ISM), which would produce an integrated ISM-BN model for reliable flood assessments. The ISM is employed to identify the relations among multiple risk factors, and then helps to configure the BN structure to conduct a risk inference. The established model was further demonstrated in Shenzhen city of China to perform an urban-level risk analysis of the flood disaster, and the Enhanced Water Index (EWI) was introduced to derive model parameters for training and verification. The obtained results of risk assessment lead to an accuracy of 76% with the Area Under ROC Curve (AUC) of 0.82, and spatial distribution of risk levels also showed a satisfactory performance. In addition, it was found that the maximum daily rainfall among ten risk factors play a key part in flood occurrence, while the elevation and storm frequency are also sensitive indicators for the study area. Besides, the spatial flood risk map generated under various design rainfall scenarios would contribute to identifying potential areas that are worth paying particular attention. Thus, the developed assessment model would be a useful tool for supporting flood risk governance to achieve reliable urban water security.

Estimation of greenhouse gas emissions from Iran's gas flaring by using satellite data and combustion equations.

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.

Experimental study of improved sclerotherapy injection in rat model of vertebral arteriocervical spondylopathy / 中国骨伤

OBJECTIVE@#To improve the rat model of cervical spondylosis of vertebral artery type (CSA) induced by injecting sclerosing agent. To evaluate the efficacy of injecting sclerosing agent to induce CSA.@*METHODS@#Forty Health SPF SD rats(20 males and 20 females), were randomly divided into two groups:the model group (20) and the blank group (20). All the animals were followed up for 4 weeks for the observation of general situation, transcranial Doppler(TCD) detection of blood flow velocity, pulsatility index and resistive index of the vertebral artery, measurement of mental distress by open-field test.@*RESULTS@#One to two days after establish the animal model, rats in the model group appeared apathetic with decreased autonomic activities, trembling, squinting, increased eye excrement, etc., and no rats died during the experiment. The mean blood flow velocity of the model group was lower than that of the blank group (P<0.05), and the pulsatilit index and resistive index of the model group were higher than that of the blank group (P<0.05). The mental distress of the model group was significantly higher than that of the blank group.@*CONCLUSION@#The modified injection of sclerosing agent is a practical method to establish the rat model of CSA, with high success rate, high stability, low mortality and simple operation.

Research progress and application of rodents model construction of peri-implantitis / 口腔医学

@#With the increasing popularity of dental implants, prevalence of peri-implantitishas also been increasing in recent years. However, a deeper understanding of the pathogenesis of peri-implantitis is still lacking. Animal models are a good bridge for studying the pathogenesis of clinical diseases. Animals such as mini-pigs, canines, non-human primates and rodents are used to construct animal models of peri-implantitis. Among them, rodents are easy to obtain and feed, and have a wide range of applications for research. In this review, we summarize the construction of rodent modelswithperi-implantitis as well as the research progress and applications.

Kinetic Energy-Based Indicators to Compare Different Load Models of a Mobile Crane.

This paper presents a proposal for the use of energy indicators to evaluate the load modelling methods on the dynamics of a mobile crane. Three different variants of mathematical models of a load carried were examined and compared: as a lumped mass on one hook-sling, as a sphere on one hook-sling, and as a box on four hook-slings. The formalism of joint coordinates and homogeneous transformation matrices were applied to define the kinematics of the system. The equations of motion were derived using the Lagrange equations of the second kind. These equations were supplemented by the Lagrange multipliers and constraint equations formulated for the cut-joints and drives. The energy indicators were proposed to evaluate the behavior of the crane and the carried load. The authors proved that modeling a load in the form of a lumped mass is a great simplification in the analysis of crane dynamics.

Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy.

Background: Hypertensive disorders in pregnancy (HDP) are diseases that coexist with pregnancy and hypertension. The pathogenesis of this disease is complex, and different physiological and pathological states can develop different subtypes of HDP. Objective: To investigate the predictive effects of different variable selection and modeling methods on four HDP subtypes: gestational hypertension, early-onset preeclampsia, late-onset preeclampsia, and chronic hypertension complicated with preeclampsia. Methods: This research was a retrospective study of pregnant women who attended antenatal care and labored at Beijing Maternity Hospital, Beijing Haidian District Maternal and Child Health Hospital, and Peking University People's Hospital. We extracted maternal demographic data and clinical characteristics for risk factor analysis and included gestational week as a parameter in this study. Finally, we developed a dynamic prediction model for HDP subtypes by nonlinear regression, support vector machine, stepwise regression, and Lasso regression methods. Results: The AUCs of the Lasso regression dynamic prediction model for each subtype were 0.910, 0.962, 0.859, and 0.955, respectively. The AUC of the Lasso regression dynamic prediction model was higher than those of the other three prediction models. The accuracy of the Lasso regression dynamic prediction model was above 85%, and the highest was close to 92%. For the four subgroups, the Lasso regression dynamic prediction model had the best comprehensive performance in clinical application. The placental growth factor was tested significant (P < 0.05) only in the stepwise regression dynamic prediction model for early-onset preeclampsia. Conclusion: The Lasso regression dynamic prediction model could accurately predict the risk of four HDP subtypes, which provided the appropriate guidance and basis for targeted prevention of adverse outcomes and improved clinical care.

The proposal of a modeling methodology for an industrial internet information model.

With the large distributed, autonomous, diverse, and dynamic information sources generated in the Industrial Internet area, the information model becomes the critical technology for heterogeneous data interoperability. By establishing unified architecture, mutually agreed communication protocols and standardizing syntax and semantics, the potential of complex data can be released. However, most of the existing information models are isolated in the professional fields, and the interoperability and scope of standards are very limited. In this article, we design a uniform information model for the Industrial Internet, and present a general modeling method which aims to build a standardized organizational framework of information. Specifically, the Industrial Internet information model is first defined, where the seven key elements and value evaluation are devised for information extraction. Then, an optimization approach combining entropy and semantic distance theories is proposed that determines the information organization granularity. Next, as the cross-layer interaction of complex information is very tricky in a tree structure and its modeling cost is extremely high in a mesh topology, the underground root structure is invented for model representation. Finally, the modeling methodology is applied to the ordinary and precision machine tools demonstrating 18.75% and 18.18% modeling cost reduction, respectively, and these two information models are further implemented in a digital machining workshop to verify the effectiveness of the proposed modeling method.

Kinetic study on methylene blue removal from aqueous solution using UV/chlorine process and its combination with other advanced oxidation processes.

This study investigated the kinetic degradation of methylene blue (MB) by a UV/chlorine process and its combination with other advanced oxidation processes. The ∙OH and reactive chlorine species (RCS: Cl∙, ClO∙, etc.) were the primary reactive species, which accounted for 56.7% and 37.6% of MB degradation at pH 7, respectively. The second-order rate constant of Cl∙ towards MB was calculated to be 2.8 × 109 M-1 s-1. When the pH increased from 3 to 7, kMB by ∙OH increased from 0.15 to 0.21 min-1 before being reduced to 0.11 min-1 at pH 11. kMB by RCS continuously reduced from 0.16 to 0.13 min-1 when the pH was increased to 11. Humic acid (HA), Br-, and Cl- inhibited the degradation with kMB in the order: kMB (in HA) < kMB (in Br-) < kMB (in Cl-). HCO3- increased kMB from 0.37 to 0.48 min-1. The experimental and modeling methods fit well, indicating the effectiveness of using Kintecus® in predicting concentrations of free radicals in complex water matrices. TOC removal was achieved at 60% after 30 min in a control process and it was strongly inhibited by the presence of HA, with 22% removal achieved at 5 mgc L-1 HA. UV/chlorine/electrochemical oxidation (UV/chlorine/EO) significantly improves kMB from 0.37 to 0.94 min-1 at a high current (240 mA), while UV/chlorine/H2O2 decreased kMB at a low concentration of 0.01 mM H2O2 (kMB decreased by 6.1%). The results indicate that the energy cost for UV irradiation was the main cost in MB treatment in both UV/chlorine and UV/persulfate (UV/PS) processes, accounting for 91% and 84%, respectively.

Experiment and numerical investigation of inhalable particles and indoor environment with ventilation system.

After the outbreak of COVID-19, the indoor environment has become particularly important in closed spaces, being a common concern in environmental science and public health, and of great significance for the building environment. To improve the indoor air quality and control the spread of viruses, the analysis of inhalable particles in indoor environments is critical. In this research, we study standards focused on inhalable particles and indoor environmental quality, as well as analyzing the movement and diffusion of indoor particles. Based on our analysis, we conduct an experimental study to determine the distribution of indoor inhalable particles of different sizes before and after diffusion under the conditions of underfloor air distribution. Furthermore, the mathematical modeling method is adopted to simulate the indoor flow field, particle trajectories, and pollutant dispersion process. The k-ε two-equation model is applied as the turbulence model in the numerical simulation, while the Lagrangian discrete phase model is adopted to trace the motion of particles and analyze the distribution characteristics of indoor particles. The results demonstrate that fine particles (i.e., those with size less than 0.5 µm) have a significant impact on the indoor particle concentration, while coarse particles (i.e., with size above 2.5 µm) have a greater influence on the total mass concentration of indoor particles. Small-sized particles can easily follow the airflow and diffuse to upper parts of the room. Overall, the effects of indoor particles on indoor air quality, including the potential threat of aerosol transmission of respiratory infectious diseases, are non-negligible. Application of the presented research can contribute to improving the health-related aspects of the building environment.

A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy.

The ultimate goal of the study is to present a strategy to improve the accuracy of near-infrared spectroscopy detection of Shuanghuanglian oral liquid in glass bottles without damaging the primary packaging. we adopted the multi-position spectral modeling (MPSM) method to correct the spectral variation caused by the difference of bottle and measuring position, so as to improve the measurement accuracy and find the best site combination for measuring Shuanghuanglian oral liquid. Baicalin, total flavonoids and soluble solid contents were considered as the quality indicators of the oral liquid, and partial least squares (PLS) models were employed for the single-position and multi-position spectra, respectively. The root mean square error of the validation set (RMSEP) of the optimum multi-position models are 0.7412 mg/mL for baicalin, 1.1259 mg/mL for total flavonoids and 0.9491% for soluble solids contents. Compared with the traditional single-position spectral modeling method (SPSM method), MPSM method improved the prediction accuracy of baicalin, total flavonoids and soluble solid contents by 26.84%, 31.97% and 58.14% respectively. The results showed that the MPSM method can improve the measurement accuracy of bottled oral liquid and is an effective method to eliminate the uncertainty of measurement conditions.

MEMe: A Mutually Enhanced Modeling Method for Efficient and Effective Human Pose Estimation.

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.