Joint maximum likelihood estimation (JMLE) is developed for diagnostic classification models (DCMs). JMLE has been barely used in Psychometrics because JMLE parameter estimators typically lack statistical consistency. The JMLE procedure presented here resolves the consistency issue by incorporating an external, statistically consistent estimator of examinees’ proficiency class membership into the joint likelihood function, which subsequently allows for the construction of item parameter estimators that also have the consistency property. Consistency of the JMLE parameter estimators is established within the framework of general DCMs: The JMLE parameter estimators are derived for the Loglinear Cognitive Diagnosis Model (LCDM). Two consistency theorems are proven for the LCDM. Using the framework of general DCMs makes the results and proofs also applicable to DCMs that can be expressed as submodels of the LCDM. Simulation studies are reported for evaluating the performance of JMLE when used with tests of varying length and different numbers of attributes. As a practical application, JMLE is also used with “real world” educational data collected with a language proficiency test.

The estimation of workspace for parallel kinematic machines (PKMs) typically relies on geometric considerations, which is suitable for PKMs operating under light load conditions. However, when subjected to heavy load, PKMs may experience significant deformation in certain postures, potentially compromising their stiffness. Additionally, heavy load conditions can impact motor loading performance, leading to inadequate motor loading in specific postures. Consequently, in addition to geometric constraints, the workspace of PKMs under heavy load is also constrained by mechanism deformation and motor loading performance.

This paper aims at developing a new heavy load 6-PSS PKM for multi-degree of freedom forming process. Additionally, it proposes a new method for estimating the workspace, which takes into account both mechanism deformation and motor loading performance. Initially, the geometric workspace of the machine is predicted based on its geometric configuration. Subsequently, the workspace is predicted while considering the effects of mechanism deformation and motor loading performance separately. Finally, the workspace is synthesized by simultaneously accounting for both mechanism deformation and motor loading performance, and a new index of workspace utilization rate is proposed. The results indicate that the synthesized workspace of the machine diminishes as the load magnitude and load arm increase. Specifically, under a heavy load magnitude of 6000 kN and a load arm of 200 mm, the utilization rate of the synthesized workspace is only 9.9% of the geometric workspace.

Accurately converting satellite instantaneous evapotranspiration (λETi) over time to daily evapotranspiration (λETd) is crucial for estimating regional evapotranspiration from remote sensing satellites, which plays an important role in effective water resource management. In this study, four upscaling methods based on the principle of energy balance, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were validated based on the measured data of the Bowen ratio energy balance system (BREB) in maize fields in northwestern (NW) and northeastern (NE) China (semi-arid and semi-humid continental climate regions) from 2021 to 2023. Results indicated that Eva-f and R-Eva-f methods were superior to Kc-ET0 and Direct-rc methods in both climatic regions and performed better between 10:00 and 11:00, with mean absolute errors (MAE) and coefficient of efficiency (ɛ) reaching <10 W/m2 and > 0.91, respectively. Comprehensive evaluation of the optimal upscaling time using global performance indicators (GPI) showed that the Eva-f method had the highest GPI of 0.59 at 12:00 for the NW, while the R-Eva-f method had the highest GPI of 1.18 at 11:00 for the NE. As a result, the Eva-f approach is recommended as the best way for upscaling evapotranspiration in NW, with 12:00 being the ideal upscaling time. The R-Eva-f method is the optimum upscaling method for the Northeast area, with an ideal upscaling time of 11:00. The comprehensive results of this study could be useful for converting λETi to λETd.

The practical implementation of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained with the data of the restricted nonlinear (RNL) model is used to predict the normal velocity on a detection plane at $y^+=10$ in a turbulent channel flow, and the predicted velocity is used as wall blowing and suction for drag reduction. An active control test is carried out by using the well-trained CNN in direct numerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall shear stresses, respectively. Furthermore, we explore the online control of wall turbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its observation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchronously in a RNL flow field. The control strategy discovered by RL has similar drag reduction rates with those obtained previously by the established method. Also, the training cost decreases by over thirty times at $Re_{\tau }=950$ compared with the DNS-RL model. The present results provide a perspective that combining the RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical. Also, this approach can be easily extended to flows at higher Reynolds numbers.

We construct an autoregressive moving average (ARMA) model consisting of the history and random effects for the streamwise velocity fluctuation in boundary-layer turbulence. The distance to the wall and the boundary-layer thickness determine the time step and the order of the ARMA model, respectively. Based on the autocorrelation's analytical expression of the ARMA model, we obtain a global analytical expression for the second-order structure function, which asymptotically captures the inertial, dynamic and large-scale ranges. Specifically, the exponential autocorrelation of the ARMA model arises from the autoregressive coefficients and is modified to logarithmic behaviour by the moving-average coefficients. The asymptotic expressions enable us to determine model coefficients by existing parameters, such as the Kolmogorov and the Townsend–Perry constants. A consequent double-log expression for the characteristic length scale is derived and is justified by direct numerical simulation data with $Re_\tau \approx 5200$ and field-measured neutral atmospheric surface layer data with $Re_\tau \sim O(10^6)$ from the Qingtu Lake Observation Array site. This relation is robust because it applies to $Re_\tau$ from $O(10^4)$ to $O(10^6)$, and even when the statistics of natural ASL deviate from those of canonical boundary-layer turbulence, e.g. in the case of imbalance in energy production and dissipation, and when the Townsend–Perry constant deviates from traditional values.

A novel wideband nonuniform metasurface antenna with stable gain is demonstrated. The nonuniform metasurface is composed of square patches and rings and is excited by a slot antenna. Based on characteristic mode analysis, two characteristic modes with same current direction are selected to achieve stable radiation performance in a wide frequency range. The wideband operation is achieved by assembling the resonant modes of the metasurface and slot antenna. The measured results show that the −10 dB impedance bandwidth of the proposed antenna is from 4.3 to 8.4 GHz (64.57%), and the 2 dB gain bandwidth is from 4.3 to 6.2 GHz (36.2%) with a peak gain value of 9.42 dBi. Moreover, broadside radiation performance is achieved.

This paper examines the issue of derivative pricing within the framework of a fractional stochastic volatility model. We present a deterministic partial differential equation system to derive an approximate expression for the derivative price. The proposed approach allows for the stochastic volatility to be expressed as a composition of deterministic functions of time and a fractional Ornstein–Uhlenbeck process. We apply this method to the European option pricing under the fractional Stein–Stein volatility model, demonstrating its feasibility and reliability through numerical simulations. Our numerical simulations also illustrate the impact of the parameters in the fractional stochastic volatility model on the option price.

Childhood maltreatment is an established risk factor for psychopathology. However, it remains unclear how childhood traumatic events relate to mental health problems and how the brain is involved. This study examined the serial mediation effect of brain morphological alterations and emotion-/reward-related functions on linking the relationship from maltreatment to depression. We recruited 156 healthy adolescents and young adults and an additional sample of 31 adolescents with major depressive disorder for assessment of childhood maltreatment, depressive symptoms, cognitive reappraisal and anticipatory/consummatory pleasure. Structural MRI data were acquired to identify maltreatment-related cortical and subcortical morphological differences. The mediation models suggested that emotional maltreatment of abuse and neglect, was respectively associated with increased gray matter volume in the ventral striatum and greater thickness in the middle cingulate cortex. These structural alterations were further related to reduced anticipatory pleasure and disrupted cognitive reappraisal, which contributed to more severe depressive symptoms among healthy individuals. The above mediating effects were not replicated in our clinical group partly due to the small sample size. Preventative interventions can target emotional and reward systems to foster resilience and reduce the likelihood of future psychiatric disorders among individuals with a history of maltreatment.

Todorokite is a common Mn oxide (with a tunnel structure) in the Earth surface environment, and can be obtained by hydrothermal treatment or refluxing process from precursor buserite with a layered structure. Several chemical reaction conditions for the phase transformation from Na-buserite to todorokite at atmospheric pressure were investigated, including temperature, pH, crystallinity of precursor Na-buserite, the amount of the interlayer Mg2+ of the Mg-buserite and clay minerals. The results showed that the conversion rate and crystallinity of todorokite decreased with falling temperature, and Mg-buserite could not be completely transformed to todorokite at lower temperatures (40°C). The poorly crystalline Na-buserite could be converted into todorokite more easily than highly crystalline Na-buserite. Todorokite can be prepared at pH 5–9, but the rate of conversion and crystallinity of todorokite did vary with pH in the order: neutral ≈ alkali > acidic. The conversion rate of todorokite decreased with decreasing interlayer Mg2+ content of the Mg-buserite. The presence of montmorillonite or goethite slowed the formation reaction of todorokite in the refluxing process, and the reaction time was prolonged when the amounts of those minerals were increased.

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

The association between sarcopenia and kidney function remains poorly investigated. We aimed to evaluate the associations between sarcopenia status and kidney function (rapid kidney function decline and chronic kidney disease (CKD)) in middle-aged and older Chinese population. A total of 9375 participants from the China Health and Retirement Longitudinal Study 2011 were included in the cross-sectional analyses. A total of 5864 participants with eGFRcr-cys ≥ 60 ml/min per 1·73 m2 at baseline were included in the longitudinal analyses and were followed up in 2015. Sarcopenia status was defined according to the Asian Working Group for Sarcopenia 2019 criteria. In the cross-sectional analyses, possible sarcopenia and sarcopenia were significantly associated with an increased risk of CKD. During the 4 years of follow-up, 359 (6·12 %) participants experienced rapid decline in kidney function and 126 (2·15 %) participants developed CKD. After multivariable adjustment of baseline eGFRcr-cys level and other risk factors, possible sarcopenia (OR, 1·33; 95 % CI 1·01, 2·12) and sarcopenia (OR, 1·49; 95 % CI 1·05, 2·12) were associated with an increased risk of primary outcome (composite of rapid decline in kidney function (annualised decline in eGFRcr-cys ≥ 5 ml/min per 1·73 m2) and progression to CKD (eGFRcr-cys < 60 ml/min per 1·73 m2). Individuals with low muscle mass or low muscle strength alone also had an increased risk of rapid decline in kidney function and progression to CKD.