Unsupervised domain adaptive segmentation algorithm based on two-level category alignment.

To enhance the model's generalization ability in unsupervised domain adaptive segmentation tasks, most approaches have primarily focused on pixel-level local features, but neglected the clue in category information. This limitation results in the segmentation network only learning global inter-domain invariant features but ignoring the category-specific inter-domain invariant features, which degenerates the segmentation performance. To address this issue, we present an Unsupervised Domain Adaptive algorithm based on two-level Category Alignment in two different spaces for semantic segmentation tasks, denoted as UDAca+. The first level is image-level category alignment based on class activation map (CAM), and the second one is pixel-level category alignment based on pseudo label. By utilizing category information, UDAca+ can effectively capture domain-invariant yet category-discriminative feature representations to improve segmentation accuracy. In addition, an adversarial learning-based strategy in mixed domain is designed to train the proposed network. Moreover, a confidence calculation method is introduced to mitigate the misleading issues of negative transfer and over-alignment caused by the noise in image-level pseudo labels. UDAca+ achieves the state-of-the-art (SOTA) performance on two synthetic-to-real adaptative tasks, and verifies its effectiveness for image segmentation.

Association of child marriage and nutritional status of mothers and their under-five children in Bangladesh: a cross-sectional study with a nationally representative sample.

BACKGROUND: Child marriage remains an important problem around the world with young mothers and their under-five children often experiencing under-nutrition. The problem is rarely studied in the Bangladeshi population. This paper was designed to identify the association between child marriage and nutritional status of mothers and their under-five children in Bangladesh. METHODS: Nationally representative secondary data was used for this study, data was extracted from the Bangladesh Demographic and Health Survey (BDHS) 2017-18. The sample consisted of 7235 mothers aged 18-49 years and their under-five children. The mothers were classified into two classes according to their age at first marriage: (i) child marriage (marriage at < 18 years) and (ii) not child marriage (marriage at ≥ 18 years). The nutritional status of mothers was measured by body mass index (BMI), and under-five children's nutritional status was measured by (i) height-for-age (z-score) (stunting), (ii) weight-for-age (z-score) (underweight), and (iii) weight-for-height (z-score) (wasting). The chi-square test and two-level logistic regression model were used for data analysis using SPSS software (IBM version 20). RESULTS: The prevalence of child marriage among Bangladeshi women was 69.0%, with the mean and median of age at the first marriage being 16.57 ± 2.83 years and 16 years, respectively. Of the mothers, 15.2% suffered from chronic energy deficiency (underweight), and 72.8% were married at < 18 years. The prevalence of stunting, underweight, and wasting among under-five children in Bangladesh was 31.0%, 22.0%, and 8.5%, respectively. Compared to women married at the age of ≥ 18 years, there was a significantly higher likelihood of chronic energy deficiency among women who married at < 18 years [Adjusted OR = 1.27, CI: 1.05-1.82; p < 0.05]. Under-five children of mothers married before the age of 18 were more likely to have stunting [Adjusted OR = 1.201, CI: 1.11-1.72; p < 0.05], wasting [Adjusted OR = 1.519, CI: 1.15-2.00; p < 0.01], and underweight [Adjusted OR = 1.150, CI: 1.09-1.82; p < 0.05] compared to children of mothers who married at age ≥ 18. CONCLUSION: The rate of child marriage among Bangladeshi women is high, and it is significantly associated with malnutrition among mothers and their under-five children. The Bangladesh government can use the findings of this study to prevent and reduce child marriage and malnutrition among mothers and their under-five children to achieve sustainable development goals by 2030.

Improved control method of the paralleled three-phase two-level inverters for common-mode voltage and circulating current suppression.

The paralleled configuration of three-phase two-level (3P2L) inverters has been put forward to increase the output power rating, operating efficiency, and system reliability. Nevertheless, this architecture brings about the serious circulating current problem, which distorts the quality of output currents, results in additional power losses, and reduces the system efficiency. Another problem is the common-mode voltage (CMV), which causes electromagnetic interference and threatens the safe operation of the system. There exists interconnection between these two issues in the paralleled 3P2L inverters. To suppress the CMV and circulating current simultaneously, an improved control method is presented. At first, the discrete model of paralleled 3P2L inverters is established, based on which the improved control method is designed to restrain the circulating current, while the parameter tuning is avoided. In addition, the zero-sequence component injection associated with the optimized configuration of carrier phase is conducted, and the CMV magnitude of each inverter is limited within one-sixth of dc-side voltage. When comparing with the traditional space vector modulation (SVM) approach, the CMV magnitude is restrained by two-thirds by the presented method. The hardware-based evaluation results have been provided to validate the presented approach.

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day's closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LE-CEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model's prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature.

MPPT efficiency enhancement of a grid connected solar PV system using Finite Control set model predictive controller.

Maximum power point tracking (MPPT) is required to get the highest possible power generated from a photovoltaic (PV) cell. Numerous researchers have proposed different MPPT strategies to be able to collect maximum generated electricity from the photovoltaic cells. In this research paper, a MPPT model predictive control strategy for a grid-connected PV system is presented. Model predictive control (MPC) was used to develop and model the AC load energy tracking efficiency for the PV systems with a power rate of 20 kW at standard test conditions. For the purpose of obtaining the power tracking performance, a DC-DC boost converter, DC-AC two level three phase inverter, and control mechanism for a grid connected AC load system was examined and presented in this paper. To approximate the actual PV array properties, the PV model is used, and the MPPT approach is suggested as a way to regulate the DC-DC boost converter and get the most power possible from the PV array when compared to P&O and model predictive control system. A three-phase, two-level VSI is employed in this study that is controlled by a model predictive control system with SVPWM. The inverter's control structure is developed using a model predictive control system (inner loop current controller) with reference frame transformation (abc to dq) coordinates by utilizing PLL. The PLL is used to obtain critical information about the grid voltage. A RL filter is then used to lower the total harmonic distortion of the output and connect the inverter's output to the grid. The MATLAB R2019a environment is used to create the system model. The overall performance of the system for conventional perturb and observer is around 97.72%, while for Finite Control Set Model Predictive Controller is 99.80%, which is better than previous similar research with faster time response and less oscillation around maximum power point.

Development of citrus peel by-product as a slice jam by using two-level factorial design.

Natural slice jam from the citrus peel is a product that enhances the economic value of the waste of citrus fruit peel. The goal of the study was to improve a recipe for a natural jam made entirely from citrus peel and shaped into a square to make it a more convenient and ready-to-eat product. A two-level factorial design was used to investigate the effects of four independent variables; X1: concentration lemon peel (Citrus limonum), X2: concentration orange peel (Citrus sinensis), X3: storage temperature, and X4: sugar. Sample processing was performed at range between 10 and 20 g of lemon and orange peel concentration and sugar (6-10 g) at a constant gelatine weight of 1.6 g. The samples were stored at 2 °C and 25 °C, and textural properties (hardness and adhesiveness) were evaluated. Results indicated that concentrations of lemon peel (10 g), orange peel (15 g), sugar (8 g), and storage temperature (13.5 °C) significantly influenced jam texture. Interaction effects, including AD, BC, and BD, were notable. Optimal conditions favoured higher orange peel concentration. Validation experiments confirmed a maximum 10 g lemon peel concentration with an error below 10%. Despite room temperature storage causing textural quality deterioration due to syneresis, this study contributes valuable insights for food industry applications of natural products, greener and more cost-effective. The factorial design approach effectively optimized citrus peel slice jam formulation, demonstrating the significance of applied sciences in addressing practical challenges.

Social determinants of chronic diseases reporting among slum dwellers in Egypt.

The high prevalence of chronic diseases in urban slums poses increasing challenges to future social and economic development for these disadvantaged areas. Assessing the health status of slum residents offers guidance for formulating appropriate policies and interventions to improve slum residents' health outcomes. This research aimed to identify the social determinants of chronic diseases reporting among slum dwellers in Egypt. A cross-sectional survey was conducted from March to December 2021 in three slum areas in Giza governorate, Egypt, including 3,500 individuals. We constructed an asset index and a welfare index to measure the economic status and living conditions of slum residents, respectively. We used these indices, along with demographic and socio-economic factors, as independent variables in the analysis. We modeled factors associated with health status using a two-level mixed logistic model to control the effects of slum areas and the potential correlation between household members. The study contributed significantly to a better understanding of the context in which slum dwellers live and the interlinkages among poor living conditions, low economic status, and health outcomes. The results showed a high rate of self-reported chronic diseases among adults aged 18 and older, reaching more than 22%, while it did not exceed 2.0% among children in the slum areas. Therefore, measuring the determinants of chronic diseases was limited to adults. The sample size was 2530 adults after excluding 970 children. The prevalence of chronic diseases among adults ranged between 16.3% in Zenin and 22.6% in Bein El Sarayat. Our findings indicated that low socio-economic status was significantly associated with reporting chronic diseases. Future policies should be dedicated to improving living conditions and providing necessary healthcare services for these vulnerable areas.

Microwave extraction and molecular imprinted polymer isolation of bergenin applied to the dendrochronological chemical study of Peltophorum dubium.

This study describes methodologies for extracting and isolating bergenin, a C-glucoside of 4-O-methylgallic acid found in some plants and it presents various in vitro and in vivo biological activities. Bergenin was previously obtained from the Pelthophorum dubim (Fabaceae) roots with a good yield. Conventional chromatographic procedures of the CHCl3 soluble fraction of the MeOH extract gave 3.62% of this glucoside. An HPLC/DAD method was also developed and validated for bergenin and its precursor, gallic acid quantifications. Microwave extractions with different solvents were tested to optimize the extraction of bergenin, varying the temperature and time. MAE (Microwave Assisted Extraction) was more efficient than conventional extraction procedures, giving a higher yield of bergenin per root mass (0.45% vs. 0.0839%). Molecularly imprinted polymer (MIP) and non-imprinted polymer (NIP) based on bergenin as the template molecule, methacrylic acid, and ethylene glycol dimethacrylate were synthesized and characterized by FTIR and SEM (Scanning Electron Microscopy). Bergenin adsorption experiments using MIP and NIP followed by molecular imprinted solid phase extraction (MISPE) showed that MIP had a higher selectivity for bergenin than NIP. A dendrochronological study using the proposed method for detection and quantification of gallic acid and bergenin in five P. dubium growth rings of a 31-year-old heartwood and in the phelloderm and barks indicated that bergenin was more abundant in the 11-14th growth rings of the heartwood and decreased from the heartwood to the barks.

The Effects of Ying Yang Bao on Nutritional Status of Children Aged 6-60 Months in Underdeveloped Rural Areas of China.

The Ying Yang Bao (YYB) intervention, a national policy in China, has been implemented for over two decades. Most previous studies have focused only on the short-term effects of YYB, while the long-term effects remain unexplored. This study was designed to evaluate the long-term effects of YYB in children aged 6-60 months. A sample of 4666 children was divided into intervention and control groups. Information on basic characteristics, physical examination, YYB consumption, etc., was obtained annually from 2018 to 2021. T-tests or chi-square tests were used to compare differences between the groups for continuous or categorical variables. Children in the intervention group showed greater incremental improvements in hemoglobin levels and physical development (p < 0.05). Prevalence of anemia, underweight, and stunting were lower in the intervention group than in the control group (all p < 0.05). Two-level regression models were constructed to assess the long-term effects of YYB. YYB reduced the risk of anemia and wasting by 37% (OR: 0.63, 95% CI: 0.52-0.75) and 49% (OR: 0.51, 95% CI: 0.39-0.67), respectively. This study indicates that YYB could significantly improve the nutritional status of children aged 6-60 months in underdeveloped rural areas of China.

A new mathematical model for evaluating surface changes in the mid-abdominal sagittal plane after two-level pedicle reduction osteotomy in patients with ankylosing spondylitis.

BACKGROUND: The purpose of this study was to create a mathematical model to precalculate the acreage change in the abdominal median sagittal plane (ac-AMSP) of patients with ankylosing spondylitis (AS) for whom two-level pedicle subtraction osteotomy (PSO) was planned. METHODS: A single-centre retrospective review of prospectively collected data was conducted among 11 adults with AS. Acreage of the abdominal median sagittal plane (a-AMSP) was performed. The distances and angles between the osteotomy apexes, anterosuperior edge of T12, xiphoid process, superior edge of the pubis, and anterosuperior corner of the sacrum were measured on preoperative thoracolumbar computed tomography. A mathematical model was created using basic trigonometric functions in accordance with the abdominal parameters. Planned osteotomized vertebra angles (POVAs) were substituted into the mathematical model, and the predictive ac-AMSP (P-AC) was obtained. A paired sample t test was performed to determine the differences between the P-AC and actual ac-AMSP (A-AC) and between the predictive acreage change rate (P-CR) and actual acreage change rate (A-CR). RESULTS: The mean age and GK were 44.4 ± 8.99 years and 102.9° ± 19.17°, respectively. No significant difference exists between A-CR and P-CR via mathematical modeling (p > 0.05). No statistically significant difference existed between POVA and actual osteotomized vertebra angles (AOVA) (p > 0.05). A statistically significant difference was observed between preoperative and postoperative measurements of LL, SVA, and GK variables (p < 0.001). CONCLUSIONS: The novel mathematical model was reliable in predicting the ac-AMSP in AS patients undergoing two-level PSO.

Avoided Level Crossing and Entangled States of Interacting Hydrogen Molecules Detected by the Quantum Superposition Microscope.

Pump-probe measurements by ultrashort THz pulses can be used to excite and follow the coherence dynamics in the time domain of single hydrogen molecules (H2) in the junction of a scanning tunneling microscope (STM). By tailoring the resonance frequency through the sample bias, we identified two spectral signatures of the interactions among multiple H2 molecules. First, the avoided level crossing featured by energy gaps ranging from 20 to 80 GHz was observed because of the level repulsion between two H2 molecules. Second, the tip can sense the signal of H2 outside the junction through the projective measurement on the H2 inside the junction, owing to the entangled states created through the interactions. A dipolar-type interaction was integrated into the tunneling two-level system model of H2, enabling accurate reproduction of the observed behaviors. Our results obtained by the quantum superposition microscope reveal the intricate quantum mechanical interplay among H2 molecules and additionally provide a 2D platform to investigate unresolved questions of amorphous materials.

Two-level QR code scheme based on region matrix image secret sharing algorithm.

Quick response (QR) codes have become increasingly popular as a medium for quickly and easily accessing information through mobile devices. However, the open-source nature of QR code encoding poses a risk of information leakage and potential attacks, especially with the growing use of QR codes in financial services and authentication applications. To mitigate the risk of information leakage due to open-source QR code encoding, this paper proposes a two-level QR code scheme based on a region matrix image secret sharing algorithm. In this scheme, the first-level public information can be directly obtained by scanning with any standard QR code scanner, while the two-level secret information can only be accessed by overlaying the shared images. To enhance the robustness of joint secret information recovery using shared images, this article designs a progressive image secret sharing algorithm based on region matrices. This algorithm meticulously processes high-priority share regions and generates multiple substitute shares. In the event of attacks on key shares, substitute shares can be employed to recover the secret information. For an increased payload capacity of each QR code, an adaptive pixel depth adjustment algorithm is devised. This algorithm ensures that the recovery of two-level secret information maintains high clarity, while not affecting the scanning functionality of each shared QR code. Experimental results validate the feasibility of this scheme, which simplifies the construction matrix, reduces matrix redundancy, and exhibits priority partitioning and higher robustness. Furthermore, QR codes embedding secret shares can safeguard the two-level information, and the recovered images exhibit exceptional clarity.

Strong Coupling Dynamics of a Quantum Emitter near a Topological Insulator Nanoparticle.

We study the spontaneous emission dynamics of a quantum emitter near a topological insulator Bi2Se3 spherical nanoparticle. Using the electromagnetic Green's tensor method, we find exceptional Purcell factors of the quantum emitter up to 1010 at distances between the emitter and the nanoparticle as large as half the nanoparticle's radius in the terahertz regime. We study the spontaneous emission evolution of a quantum emitter for various transition frequencies in the terahertz and various vacuum decay rates. For short vacuum decay times, we observe non-Markovian spontaneous emission dynamics, which correspond perfectly to values of well-established measures of non-Markovianity and possibly indicate considerable dynamical quantum speedup. The dynamics turn progressively Markovian as the vacuum decay times increase, while in this regime, the non-Markovianity measures are nullified, and the quantum speedup vanishes. For the shortest vacuum decay times, we find that the population remains trapped in the emitter, which indicates that a hybrid bound state between the quantum emitter and the continuum of electromagnetic modes as affected by the nanoparticle has been formed. This work demonstrates that a Bi2Se3 spherical nanoparticle can be a nanoscale platform for strong light-matter coupling.

Accurate classification of land use and land cover using a boundary-specific two-level learning approach augmented with auxiliary features in Google Earth Engine.

Land use land cover (LULC) classification using remote sensing images is a valuable resource in various fields such as climate change, urban development, and land degradation monitoring. The city of Madurai in India is known for its diverse geographical elements and rich heritage, which includes the cultural sport of "Jallikattu": whose main competitor, the zebusare deeply affected by the conversion of their waterbodies and pastures into concrete jungles. Hence, monitoring land degradation is vital in preserving the geography and cultural heritage of the study area, Madurai. The "Landsat 8 Operational Land Imager tier_2 collection_2 Level_2 Surface Reflectance" image was taken for this study. The LULC classification is performed based on the following classes: forest, agriculture, urban, water bodies, uncultivated land, and bare land. The objective of the study is to incorporate auxiliary features to spectral and textural features along with a simple non-iterative clustering (SNIC) segmentation algorithm and implement a boundary-specific two-level learning approach based on support vector machines (SVM) and k nearest neighbors (kNN) classification algorithms. The overall accuracy (OA) of 95.78% and 0 .94 Kappa score (K) were obtained using a boundary-specific two-level model augmented with auxiliary feature and SNIC algorithm in comparison to PB, OB, and OBS, which achieve OA (K) of 81% (0.76), 91% (0.89), and 94.42% (0.92), respectively. The results demonstrate a notable enhancement in overall classification accuracy when augmenting the features and refining classification decisions using a boundary-specific two-level learning approach.

An aneurysm-specific preconditioning technique for the acceleration of Newton-Krylov method with application in the simulation of blood flows.

In this paper, we develop an algorithm to simulate blood flows in aneurysmal arteries and focus on the construction of robust and efficient multilevel preconditioners to speed up the convergence of both linear and nonlinear solvers. The work is motivated by the observation that in the local aneurysmal region, the flow is often quite complicated with one or more vortices, but in the healthy section of the artery, the principal component of blood flows along the centerline of the artery. Based on this observation, we introduce a novel two-level additive Schwarz method with a mixed-dimensional coarse preconditioner. The key components of the preconditioner include (1) a three-dimensional coarse preconditioner covering the aneurysm; (2) a one-dimensional coarse preconditioner covering the central line of the healthy section of the artery; (3) a collection of three-dimensional overlapping subdomain preconditioners covering the fine meshes of the entire artery; (4) extension/restriction operators constructed by radial basis functions. The blood flow is modeled by the unsteady incompressible Navier-Stokes equations with resistance outflow boundary conditions discretized by a stabilized finite element method on fully unstructured meshes and the second-order backward differentiation formula in time. The resulting large nonlinear algebraic systems are solved by a Newton-Krylov algorithm accelerated by the new preconditioner in two ways: (1) the initial guess of Newton is obtained by solving a linear system defined by the coarse preconditioner; (2) the Krylov solver of the Jacobian system is preconditioned by the new preconditioner. Numerical experiments indicate that the proposed preconditioner is highly effective and robust for complex flows in a patient-specific artery with aneurysm, and it significantly reduces the numbers of linear and nonlinear iterations.

Treatment of the two-level degenerative cervical disk diseases based on algorithmic anterior approach: a multicenter prospective study.

To analyze the effectiveness of an algorithmic anterior approach to the surgical treatment of patients with two-level cervical degenerative disk disease based on the preoperative clinical and imaging parameters. The study included 244 patients with two-level cervical degenerative disk disease. Three groups of patients were evaluated at 3 neurosurgical centers between 2016-2019. The prospective group (Group I, n = 126) consisted of patients who were treated using an algorithm to decide whether they should be treated with a two-level Total Disk replacement (TDR), Anterior Cervical Discectomy and Fusion (ACDF) and hybrid technique. The control group (Group II, n = 118) consisted of patients who underwent two-level anterior decompression with TDR, ACDF and hybrid stabilization between 2005-2015. Visual Analogue Scale (VAS) neck pain, VAS upper limbs pain, Neck Disability Index (NDI), SF-36, Macnab and Nurick scales were collected. Perioperative complications were identified. At 2 years of follow up Group I had significantly better clinical outcomes based on VAS neck pain score (p = 0.02), VAS upper limbs pain (p = 0.04), NDI score (p = 0.02), SF-36 score (p = 0.01), satisfaction with surgery on the Macnab scale (p < 0.001) and outcome of surgery based on Nurick scale (p < 0.001). Complication rate was lower in Group I, 6.3% compared to 24.6% in Group II, p = 0.0001. The algorithmic anterior approach to the surgical treatment of patients with two-level cervical degenerative disk disease resulted in significant improvement of functional outcomes and a decrease in complications at a minimum 2 years of follow-up.

Pump-Probe Optical Response and Four-Wave Mixing in a Zinc-Phthalocyanine-Metal Nanoparticle Hybrid System.

We investigate theoretically the optical response of a zinc-phthalocyanine molecular quantum system near a gold spherical nanoparticle with a radius of 80 nm. The quantum system is irradiated by a strong pump and a weak probe coherent electromagnetic field. Using the density matrix methodology, we obtain analytical expressions for the absorption, dispersion, and the four-wave-mixing coefficients. The influence of the nanoparticle on the spontaneous decay rate of the quantum system, as well as on the external fields, are obtained by an electromagnetic Green's tensor method. The spectroscopic parameters of the molecule are also obtained by ab initio methods. For the studied optical spectra, we find that, below a critical distance between the molecule and the plasmonic nanoparticle, determined by the minimal value of the effective Rabi frequency, single-peaked spectra are observed. Above this critical distance, the spectra exhibit the characteristic Mollow-shaped profiles. The enhancement of the pump field detuning induces the shift of the sideband resonances away from the origin. Lastly, and most importantly, regardless of the value of the detuning, the optical response of the system is maximized for an intermediate value of the interparticle distance.

A life course perspective on determinants of discontinuance of active participation in sports activities.

Physical inactivity remains a global public health challenge today. Determining why people stop regularly participating in sports is significant to develop targeted intervention strategies for sports promotion and healthy living. As sports participation is dynamic throughout life, a life-course perspective is needed to provide a more comprehensive understanding. This study adopts a life-course perspective to explore the determinants of the change from active participation in sports to becoming inactive. Based on online retrospective survey data collected in the Netherlands, a two-level binary logistic regression model is estimated to capture the effects of socio-demographics, sports motivations, life transitions, and neighborhood characteristics on sports dropout over the lifespan. Results show that dropout from sports is age-specific, and that people are less likely to discontinue sports participation when they have health and weight loss goals. Life transitions have different effects. The cessation of living with physically active people appears to be the most important event to make people stop sporting, followed by having a baby, and then owning the first car. Compared with education-related events, work-related events are more likely to cause people to stop sporting. Moreover, the probability of sports discontinuance may increase when residents feel unsafe doing physical activities in their neighborhoods or when the neighborhood has sufficient greenspace for walking. The findings have implications for supporting sports participants to continue exercising by addressing the barriers.

Bayesian estimation for the random moderation model: effect size, coverage, power of test, and type І error.

The random moderation model (RMM) was developed based on a two-level regression model to cope with heteroscedasticity in moderation analysis, and normal-distributed-based maximum likelihood (NML) estimation was developed to estimate the RMM. To present an alternative to the NML, this article discusses the effectiveness of Bayesian estimation for the RMM, aiming to explore a more practical method using the popular software Mplus. Through a simulation study, the RMM based on Bayesian estimation was investigated and compared to maximum likelihood (ML) estimations, including the NML and the default ML estimation in Mplus. The results indicated that the Bayesian approach outperformed the two ML estimations. It showed (a) higher accuracy for estimation of the effect size of the moderation effect; (b) higher 95% credibility interval coverage of the true value of the moderation effect; and (c) well-controlled and more stable type I error rates, while powers comparable to the ML estimations were provided.

An overview of optimal designs under a given budget in cluster randomized trials with a binary outcome.

Cluster randomized trial design may raise financial concerns because the cost to recruit an additional cluster is much higher than to enroll an additional subject in subject-level randomized trials. Therefore, it is desirable to develop an optimal design. For local optimal designs, optimization means the minimum variance of the estimated treatment effect under the total budget. The local optimal design derived from the variance needs the input of an association parameter ρ in terms of a "working" correlation structure R(ρ) in the generalized estimating equation models. When the range of ρ instead of an exact value is available, the parameter space is defined as the range of ρ and the design space is defined as enrollment feasibility, for example, the number of clusters or cluster size. For any value ρ within the range, the optimal design and relative efficiency for each design in the design space is obtained. Then, for each design in the design space, the minimum relative efficiency within the parameter space is calculated. MaxiMin design is the optimal design that maximizes the minimum relative efficiency among all designs in the design space. Our contributions are threefold. First, for three common measures (risk difference, risk ratio, and odds ratio), we summarize all available local optimal designs and MaxiMin designs utilizing generalized estimating equation models when the group allocation proportion is predetermined for two-level and three-level parallel cluster randomized trials. We then propose the local optimal designs and MaxiMin designs using the same models when the group allocation proportion is undecided. Second, for partially nested designs, we develop the optimal designs for three common measures under the setting of equal number of subjects per cluster and exchangeable working correlation structure in the intervention group. Third, we create three new Statistical Analysis System (SAS) macros and update two existing SAS macros for all the optimal designs. We provide two examples to illustrate our methods.