Initial Parameters Research Articles

Optimization of FDM 3D Printer Process Parameters to Minimize Dimensional Errors with PLA Material Using Response Surface Methodology

This paper presents a response surface methodology to fit a second-order response surface model aimed to finding process parameters to minimize length error (LE) and diameter error of the cylindrical shafts (DE) in fused deposition modeling (FDM) using polylactic acid (PLA) material. The process parameters in this study included layer height (LH), which varied from 0.1 to 0.4 mm, and print speed (PS), which ranged from 20 to 60 mm/s, while other parameters were set to constant values. The results showed that optimal process parameters obtained in this study significantly lower dimensional errors than the initial parameterization recommended by UltiMaker Cura software.

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Initiation of Cassava Callus Culture and Its Prospect for Starch Production: A Systematic Mapping Study

Mass propagation of cassava on several hectares of arable land due to increasing demand for its starch is not feasible due to land availability, pests and disease invasion, and long cultivation period. Plant cell culture technology is a promising solution despite the scarcity of cassava callus culture for starch production applications. Therefore, a systematic mapping study (SMS) was performed to identify the applications of cassava tissue culture and its prospects in starch production and investigate the important parameters for cassava callus culture initiation. The SMS began with formulating research questions (RQs), conducting searches on various databases, collecting and screening related articles, and extracting and mapping the selected articles. A total of 56 of 589 articles in the initial searching phase were chosen to be used as references to answer each RQ. The extracted data indicates that cassava tissue culture was mostly used for micropropagation, while starch production from its tissue culture is still limited. Basal medium and plant growth regulators influence cassava callus culture initiation most. The findings of the SMS offer a better understanding of cassava tissue culture and the prospects of producing cassava starch.

Inhibitory control of gait initiation in humans: An electroencephalography study.

Response inhibition is a crucial component of executive control. Although mainly studied in upper limb tasks, it is fully implicated in gait initiation. Here, we assessed the influence of proactive and reactive inhibitory control during gait initiation in healthy adult participants. For this purpose, we measured kinematics and electroencephalography (EEG) activity (event-related potential [ERP] and time-frequency data) during a modified Go/NoGo gait initiation task in 23 healthy adults. The task comprised Go-certain, Go-uncertain, and NoGo conditions. Each trial included preparatory and imperative stimuli. Our results showed that go-uncertainty resulted in delayed reaction time, without any difference for the other parameters of gait initiation. Proactive inhibition, that is, Go uncertain versus Go certain conditions, influenced EEG activity as soon as the preparatory stimulus. Moreover, both proactive and reactive inhibition influenced the amplitude of the ERPs (central P1, occipito-parietal N1, and N2/P3) and theta and alpha/low beta band activities in response to the imperative-Go-uncertain versus Go-certain and NoGo versus Go-uncertain-stimuli. These findings demonstrate that the uncertainty context; induced proactive inhibition, as reflected in delayed gait initiation. Proactive and reactive inhibition elicited extended and overlapping modulations of ERP and time-frequency activities. This study shows the protracted influence of inhibitory control in gait initiation.

The effectiveness of a protocol without routine radiographs for follow-up of adolescent idiopathic scoliosis patients (CURVE): a study protocol.

Current follow-up protocols for adolescent idiopathic scoliosis (AIS) are based on consensus and consist of regular full-spine radiographs to monitor curve progression and surgical complications. Consensus exists to avoid inappropriate use of radiographs in children. It is unknown whether a standard radiologic follow-up (S-FU) approach is necessary or if a patient-empowered follow-up (PE-FU) approach can reduce the number of radiographs without treatment consequences. A nationwide multicenter pragmatic randomized preference trial was designed for 3 follow-up subgroups (pre-treatment, post-brace, post-surgery) to compare PE-FU and S-FU. 812 patients with AIS (age 10-18 years) will be included in the randomized trial or preference cohorts. Primary outcome is the proportion of radiographs with a treatment consequence for each subgroup. Secondary outcomes consist of the proportion of patients with delayed initiation of treatment due to non-routine radiographic follow-up, radiation exposure, societal costs, positive predictive value, and interrelation of clinical assessment, quality of life, and parameters for initiation of treatment during follow-up. Outcomes will be analyzed using linear mixed-effects models, adjusted for relevant baseline covariates, and are based on intention-to-treat principle. Study summary: (i) a national, multicenter pragmatic randomized trial addressing the optimal frequency of radiographic follow-up in patients with AIS; (ii) first study that includes patient-empowered follow-up; (iii) an inclusive study with 3 follow-up subgroups and few exclusion criteria representative for clinical reality; (iv) preference cohorts alongside to amplify generalizability; (v) first study conducting an economic evaluation comparing both follow-up approaches.

Multitask-Guided Deep Clustering With Boundary Adaptation.

Multitask learning uses external knowledge to improve internal clustering and single-task learning. Existing multitask learning algorithms mostly use shallow-level correlation to aid judgment, and the boundary factors on high-dimensional datasets often lead algorithms to poor performance. The initial parameters of these algorithms cause the border samples to fall into a local optimal solution. In this study, a multitask-guided deep clustering (DC) with boundary adaptation (MTDC-BA) based on a convolutional neural network autoencoder (CNN-AE) is proposed. In the first stage, dubbed multitask pretraining (M-train), we construct an autoencoder (AE) named CNN-AE using the DenseNet-like structure, which performs deep feature extraction and stores captured multitask knowledge into model parameters. In the second phase, the parameters of the M-train are shared for CNN-AE, and clustering results are obtained by deep features, which is termed as single-task fitting (S-fit). To eliminate the boundary effect, we use data augmentation and improved self-paced learning to construct the boundary adaptation. We integrate boundary adaptors into the M-train and S-fit stages appropriately. The interpretability of MTDC-BA is accomplished by data transformation. The model relies on the principle that features become important as the reconfiguration loss decreases. Experiments on a series of typical datasets confirm the performance of the proposed MTDC-BA. Compared with other traditional clustering methods, including single-task DC algorithms and the latest multitask clustering algorithms, our MTDC-BA achieves better clustering performance with higher computational efficiency. Deep features clustering results demonstrate the stability of MTDC-BA by visualization and convergence verification. Through the visualization experiment, we explain and analyze the whole model data input and the middle characteristic layer. Further understanding of the principle of MTDC-BA. Through additional experiments, we know that the proposed MTDC-BA is efficient in the use of multitask knowledge. Finally, we carry out sensitivity experiments on the hyper-parameters to verify their optimal performance.

An Analysis of Power Friction Losses in Gear Engagement with Intermediate Rolling Elements and a Free Cage

Currently, mechanical gears with cycloid engagement are increasingly used in mechanisms along with involute ones. In modern drive mechanisms, using pin gears and gears with intermediate rolling elements (IRE) is widespread, which simultaneously use cycloid gears. To a greater extent, pin gears are now being investigated, but IRE gears have their undeniable advantages. Many works are devoted to the study of cycloid toothing for certain gears, but the efficiency, especially that of IRE gears, has practically not been investigated. Therefore, the analysis of power losses in the engagement of a gear with IRE and a free cage (IREFC) is relevant. In this analysis, the authors of the work have used laws of mechanics, methods of energy flows and a secant normal. Mathematical expressions have been obtained to estimate slip speeds and power friction losses in the engagement of a gear with IREFC, and a formula has been derived to determine the efficiency of a mentioned mechanical transmission. The calculation of slip speeds and power losses at the points of contact of a rolling element with cycloid profiles of wheels for selected initial parameters of a gear with IREFC has been presented. The friction power and the overall efficiency of the entire gear engagement have also been calculated. This work shows that power friction losses at the points of contact of a rolling element with cycloid profiles of tooth wheels of a gear with IREFC are not the same. The friction power in the contact of a rolling element with a cycloid profile of a cam is an order of magnitude higher than the friction power in the contact of a rolling element with a cycloid profile of a crown.

The laboratory parameters in predicting the severity and death of COVID-19 patients: Future pandemic readiness strategies.

The range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics.

Bayesian model updating of super high‐rise building for construction simulation

SummaryA finite element model was established using SAP2000 software for the C1 tower, a super high‐rise building in the second phase of the Nanjing Financial City project, and the construction process of the tower was simulated. The C1 tower adopts a frame core tube extension arm and ring truss structure system, with 87 floors above ground and five floors underground. The roof structure has an elevation of 416.6 m. Precise measurements of inter‐story compression deformation were conducted using advanced surveying equipment. Sensitivity analysis, based on the finite difference method, identified the shear wall elastic modulus, frame column elastic modulus, steel beam elastic modulus, and shear wall unit weight as four highly influential parameters. Employing the Bayesian principle, the Markov Chain Monte Carlo (MCMC) method was applied to determine the posterior density probability function of the parameters targeted for modification. Subsequently, the Metropolis–Hastings (MH) sampling algorithm was employed to refine the C1 Tower model. This refinement significantly reduced the root mean square error between the measured and simulated vertical displacements, achieving an error reduction of approximately 10% from 6.082 to around 2.160. The modified material parameters, for the most part, adhered to a normal distribution assumption and exhibited mean values in the posterior probability density functions for the elastic modulus of Q345 steel beams, C70 frame columns, and C60 shear walls. Compared to the initial finite element parameters, the variation range was approximately 13% to 17%. These results serve as a validation of the effectiveness of the proposed method.

Design and analysis of ELM-based predefined time sliding mode adaptive controller for PMLM position control under physical constraints

Achieving accurate position tracking for robotics and industrial servo systems is an extremely challenging task, particularly when dealing with control saturation, parameter perturbation, and external disturbance. To address these challenges, a predefined time convergent sliding mode adaptive controller (PTCSMAC) has been proposed for a permanent magnet linear motor (PMLM). A novel sliding mode surface (SMS) with predefined time convergence PDTC has been constructed, which ensures that the error converges to zero within the prescribed time. The system not only meets the expected performance standards but also has a uniformly bounded motor speed. The trajectory tracking error in SMS is proven to converge to zero within the predefined time. This predefined time stability of the closed-loop system has been demonstrated by using the Lyapunov stability criterion with PDTC. The convergence time (CT) can be arbitrarily set, and the upper bound of it is not affected by the initial value and control parameters of the system. A new updated version of extreme learning machine (ELM) is introduced to approximate the uncertain part of the system based on PDTC. The ELM is also provided with the hyperbolic tangent function to estimate the saturation constraint. This is done by converting the function into a linear function concerning the unconstrained control input variable. Then, based on established stability, a novel sliding mode adaptive controller (PTCSMAC) with predefined time convergence is designed. The convergence time (CT) of the controller is unaffected by the initial conditions as well as the control parameters. The rigorous numerical simulations on the PMLM model with complex disturbances verify the strong robustness and high-precision tracking characteristic of the proposed control law.

How artificial intelligence cooperating with agent‐based modeling for urban studies: A systematic review

AbstractAs urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent‐based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.

Improved Fault Detection Method based on HSMM

This paper proposes a fault detection method for multivariate statistical process control. The proposed method combines the Forward-Backward Hidden Semi-Markov Model (HSMM) and Principal Component Analysis (PCA). A stochastic automaton was used for multi-mode detection with many observation sequences. We used agglomerative clusters to find the initial parameters of HSMM. We allocated an adaptive threshold and a fixed threshold in each mode for fault detection with PCA, including Hotelling T2 statistic and squared predictive error (Q statistic). We simulated this method on the Tennessee Eastman Process (TEP). Some faults were designed with various runs and times of occurrence. The experimental results were compared with the Mixture Bayesian PCA, Hidden Markov Model (HMM), and HSMM methods. The results are robust with an efficient detection rate. This activity recommends ways to find action plans for multi-mode process monitoring in chemical plants.

Performance Analysis of a Novel Axial Radial Flux Segmental Rotor Switched Reluctance Motor

Based on the characteristics of axial flux switched reluctance motor (SRM) and radial flux SRM, this paper proposes a new type of axial radial flux segmental rotor switched reluctance motor (ARFSRSRM). The motor has the left and right dual axial stator structures, which forms a kind of axial flux path with the segmental rotor in the middle, and a radial stator structure, which shares a kind of radial flux path with the segmental rotor on the outside of the motor. Compared with the conventional SRM, this motor combines the advantages of high output torque and torque density due to the inclusion of two kinds of flux paths, making it suitable for high-efficiency and high-torque fields such as electric vehicles. In this paper, the topology and operating principle of the new motor are first introduced. Then the initial structural parameters are derived, and the multi-objective optimization of the proposed motor is carried out using Taguchi’s algorithm. The static and dynamic performance of the motor is comprehensively analyzed using the 3-D finite element method. Finally, the experimental results obtained through the trial production of a prototype machine verify the good performance of the new motor.

A Fast Optimizing Method of Clutch–Clutch Shift Calibration Parameters

Aiming at the drawbacks of the heavy workload and lengthy calibration cycle of transmission control unit (TCU) development, a fast optimizing and test method of clutch–clutch shift calibration parameters is developed based on the “V” development process. A controlled-object simulation model of a six-speed automatic transmission (6AT) is established, and the shift calibration parameters and shift quality evaluation system are studied. Therefore, a model-in-the-loop (MIL) calibration platform for key calibration parameters and evaluation indicators in the shift process is constructed. By setting the optimization target of evaluation indicators, the initial calibration parameters’ ranges are reduced. Finally, the TCU is flashed with the optimized calibration parameters, and a hardware-in-the-loop (HIL) test system is built to verify the shift quality. The same shift quality verification test is carried out in the vehicle. The test results show that the application of the MIL simulation platform can enable the batch simulation analysis of eight key shift calibration parameters, gradually refine the initial rough interval, and ensure shift quality. The shift calibration parameters are cross-validated in multiple development links, and the dependence on vehicle calibration is greatly reduced. The development cycle is effectively shortened to less than 6 months.

Structural flexibility identification from impact test data through a subband estimation method

SummaryFlexibility is an important parameter reflecting bridge load‐carrying capacity. Dynamic testing is a fast and effective method to obtain the structural modal flexibility of small‐ and medium‐span bridges. The Deterministic‐stochastic subspace identification (DSI) algorithm is a well‐established structure identification method in the time domain. However, the estimation of damping, especially the modal scaling factor, is not always reliable due to inevitable measurement noise, which directly affects the identification accuracy of flexibility. This paper proposes a maximum likelihood estimation method in subbands (SMLE), which can be regarded as an add‐on method of the DSI algorithm because the initial parameters are obtained from the DSI algorithm. The processing of frequency band division is implemented first, and the frequency response function curve in each subband of the whole frequency range is fit separately. Then, subband cyclic iteration is proposed to improve the identification accuracy in a closely spaced mode system. The proposed SMLE method maintains the advantages of the DSI algorithm while improving the accuracy of parameter estimation and flexibility identification. Two lumped mass models are used to verify that the proposed method can effectively improve estimates to obtain a precise flexibility matrix and predict the displacement of the structure under static loading. Experimental example of a continuous girder bridge is considered to verify the availability and effectiveness of the proposed method in practice.

Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities

In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy.

Clinical and economic effectiveness of using immunotherapeutic drugs in patients with urothelial carcinoma in the healthcare system of the Russian Federation

Aim. To perform pharmacoeconomic evaluation of chemotherapy schemes GC (gemcitabine, cisplatin) and GemCarbo (gemcitabine, carboplatin) in comparison with immunotherapy drugs atezolizumab, pembrolizumab or avelumab in patients with locally advanced or metastatic urothelial carcinoma.Materials and methods. Pharmacoeconomic cost–effectiveness analysis, sensitivity analysis in context of changes of initial model parameters were performed.Results. Literature data analysis allows to make a conclusion of better clinical effectiveness and safety of immunotherapy drugs compared to chemotherapy in patients with urothelial carcinoma. Cost of medications was significantly lower for platinum-based chemotherapy (103,625.61 rubles for GC and 88,733.63 rubles for GemCarbo) compared to a course of immunotherapy (950,092.39 rubles for atezolizumab, 953,340.21 rubles for pembrolizumab, 1,328,999.43 rubles for GC + avelumab). However, the cost of treatment of complications arising during platinum-based chemotherapy was more than 20-fold higher than cost of treatment of immunotherapy complications: 578,853.02 rubles versus 15,336.78– 26,994.52 rubles). Cost–effectiveness analysis favored atezolizumab for which cost–effectiveness ratio was 53,230.69 rubles for 1 month of patient’s life. Atezolizumab had better value than standard 1st line GC chemotherapy by 10,671.80 rubles, as well as immunotherapy courses using pembrolizumab by 9,697.57 rubles and GC + avelumab by 10,824.66 rubles. The highest costs were observed for GemCarbo chemotherapy course: it is 18,522.82 rubles more expensive than atezolizumab course. Sensitivity analysis performed for the cost–effectiveness ratio showed stability of the developed model in regards to increased cost of atezolizumab course up to +18 % and decrease in overall survival with this course up to –15 %.Conclusion. Atezolizumab is a clinically effective and economically justified option for treatment of adults with locally advanced or metastatic urothelial carcinoma and PD-L1 expression ≥5 % in the healthcare system of the Russian Federation.

Cavity expansion-based interpretation of piezocone penetration test (CPTu) data in clays

A cavity expansion-based method is proposed in this paper to correlate the relevant findings with the piezocone penetration test (CPTu) data in clays. The rigorous and explicit solution for both spherical and cylindrical undrained cavity expansion is adopted based on a unified critical state constitutive model for clays and sands. The analogous model developed is comprehensively validated against two series of advanced numerical simulations and calibration chamber tests of the CPTu in fine-grained soils, along with bias analyses. The effects of the overconsolidation ratio and initial state parameter are extensively investigated for various clay types. Both theoretical and empirical correlations are then proposed for back-calculations of cone factor, undrained shear strength, overconsolidation ratio and initial state parameter of clays. Two case studies of CPTu tests are conducted in addition to examine correlations and contours of the in situ state parameter, demonstrating the applicability of the proposed theoretical approach for interpretation of the CPTu data.

Model robustness for feedback stabilization of open quantum systems

This paper builds upon and expands the findings presented in Liang et al. (2021). Our focus lies in scenarios where initial states and model parameters remain unknown. In contrast to prior research, we consider multiple measurement channels, addressing the challenge of global exponential stabilization towards a chosen target subspace. Under quantum non-demolition conditions, our analysis establishes essential tools for achieving feedback stabilization. Furthermore, we explore the utilization of a simplified filter as a viable alternative to full state estimator. While the effectiveness of this simplified filter was numerically examined in Cardona et al. (2020), our paper offers a comprehensive proof of its applicability in feedback stabilization. Furthermore, our analysis may serve as an inspiration for demonstrating the validation of the robust feedback stabilization through generic approximate filters.

Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators

This study proposes a parabolic off-axis reflective optical system design method to reduce the wave aberration of the optical system of a large aperture single star simulator and improve the optical system’s imaging quality. Firstly, we determined the design indexes of the optical system of the large aperture single star simulator by analyzing the technical indexes of the star sensitizer and the development status of the single star simulator; secondly, the initial structural parameters of the optical system were calculated based on the theory of primary aberration; then, we carried out the design optimization of the optical system, the image quality evaluation, and the tolerance analysis using Zemax software; finally, the study tested the wave aberration of the optical system by using the four-dimensional interferometer and the standard mirror together. The simulation results of the optical system are as follows: in the entire field of view, the aberration of the optical system is far less than 0.002%, the modulation transfer function (MTF) reaches the diffraction limit, and the maximum wave aberration is 0.0324 λ. The experimental results are as follows: the maximum wave aberration of the optical system is 0.0337 λ, which is less than 1/25 λ, and it meets the requirements of the index. The simulation and experimental results show that the optical system of the large aperture single star simulator designed by this method has good imaging quality and a simple system structure.