Model Structure Research Articles

Validity, test-retest reliability, and responsiveness of the Indonesian version of FACIT-COST measure for subjective financial toxicity.

Financial toxicity describes the impairment of financial wellbeing in patients due to the burden of cancer diagnosis and care. The COST: A Functional Assessment of Chronic Illness Therapy Measure of Financial Toxicity (FACIT-COST) is the most widely used cancer-specific measure of subjective financial toxicity, having been validated in multiple languages, but not in Indonesian. This study aimed to validate the Indonesian version of FACIT-COST in a breast cancer sample. A single-center prospective cohort study was performed in Indonesia. Female breast cancer patients aged ≥ 18 undergoing treatment at baseline were invited to participate and followed for up to six months. The survey included the official Indonesian version of FACIT-COST (v2) which was administered to the patients by interviewers. Clinical information (e.g., metastasis status, disease duration) was provided based on medical records. The following measurement properties of FACIT-COST were tested: distributional characteristics, structural validity (principal component [PCA] and confirmatory factor analyses [CFA]), internal consistency reliability (Cronbach's alpha and McDonald's omega), known-groups validity (Mann-Whitney U or Kruskal-Wallis H test), test-retest reliability, and responsiveness to change. Overall, 300 female patients participated at baseline. No patients reported the best or worst possible FACIT-COST total scores. The PCA proposed a two-factor model structure for the instrument, which was confirmed by the CFA (RMSEA = 0.042, SRMR = 0.049, CFI = 0.99, TLI = 0.99). The internal consistency reliability of the two factors was considered adequate (Cronbach's alpha = 0.774-0.882, McDonald's omega = 0.786-0.888). The FACIT-COST total score significantly discriminated across the following known-groups: age, education, residential setting, income, employment, metastasis status, number of symptoms, and financial coping strategies. The FACIT-COST demonstrated excellent test-retest reliability (intraclass correlation coefficient = 0.96) and satisfactory responsiveness to change (standardized response mean and effect size ranges=|0.39| to |0.92|). This is the first study to validate the FACIT-COST in patients with breast cancer and to present the measurement properties of the Indonesian version of FACIT-COST. The Indonesian FACIT-COST demonstrates acceptable psychometric performance and shows potential as a valid measure of subjective financial toxicity. The instrument may serve as a valuable tool for informing health policies that focus on providing resource support to improve cancer care in Indonesia.

Students Can Identify Quality Teachers, but can They Distinguish Between Dimensions of Quality Teaching? a Comparative Analysis of the Structure Behind the Tripod Survey

ABSTRACT Student perception surveys are prominent and pervasive tools for teacher appraisal and feedback across international contexts, buoyed by the strong relationship between higher student ratings of teachers and additional measures of teacher effectiveness. Yet, there is disagreement and conflicting evidence regarding the underlying structure of student perception survey instruments, including whether students distinguish multiple dimensions of teaching or form more general impressions (or unidimensional conceptions) of teaching. This study examined the structure of the Tripod student survey in Australian schools using a competing model analysis of different survey structures and examination of discriminant validity to identify the best model fit. Findings challenge the purported Tripod model structure and raise larger questions regarding elements of teaching that students distinguish via student perception surveys. Implications for the use of and continued research on student perception surveys, including those originating in other countries, are discussed.

Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.

A vertical take-off and landing (VTOL) unmanned aircraft vehicle trajectory control model with air-launched missiles

Purpose In today’s technology, the significance of unmanned aerial vehicles is steadily increasing. Many unmanned aerial vehicles design, especially those used for military purposes, have achieved autonomy from human operators. Undoubtedly, one of the most crucial features of these aircraft is their vertical take-off and landing (VTOL) capability. Inspired by quadrotor methodology, this paper aims to conduct, a modeling of an aircraft with VTOL capabilities. Design/methodology/approach The impact of releasing air-launched missiles, considered as the useful payload carried during the flight of the aircraft, has been taken into account in this modeling. The release of air-launched missiles disrupts both the symmetric structure of the system and alters the mass and inertia parameters. Simulations were conducted to investigate scenarios involving the simultaneous release of all air-launched missiles and their release at different times. Findings The investigation focused on determining how quickly an aircraft, aiming to consecutively hit targets, can return to its desired trajectory. The time interval between the consecutive releases of two air-launched missiles has been identified. Originality/value It is crucial for a VTOL-capable aircraft to possess a unique modeling structure to examine its capability of releasing air-launched missiles in various scenarios. This entails understanding not only the aircraft’s VTOL functionality but also its ability to effectively release missiles in different operational conditions.

Remote physiological signal recovery with efficient spatio-temporal modeling

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart. This measurement mode has advantages of contactless measurement, simple operation, low cost, etc. In recent years, several deep learning-based rPPG measurement methods have been proposed. However, the features learned by deep learning models are vulnerable to motion and illumination artefacts, and are unable to fully exploit the intrinsic temporal characteristics of the rPPG. This paper presents an efficient spatiotemporal modeling-based rPPG recovery method for physiological signal measurements. First, two modules are utilized in the rPPG task: 1) 3D central difference convolution for temporal context modeling with enhanced representation and generalization capacity, and 2) Huber loss for robust intensity-level rPPG recovery. Second, a dual branch structure for both motion and appearance modeling and a soft attention mask are adapted to take full advantage of the central difference convolution. Third, a multi-task setting for joint cardiac and respiratory signals measurements is introduced to benefit from the internal relevance between two physiological signals. Last, extensive experiments performed on three public databases show that the proposed method outperforms prior state-of-the-art methods with the Pearson’s correlation coefficient higher than 0.96 on all three datasets. The generalization ability of the proposed method is also evaluated by cross-database and video compression experiments. The effectiveness and necessity of each module are confirmed by ablation studies.

Herwig 7.3 release note

A new release of the Monte Carlo event generator Herwig (version 7.3) has been launched. This iteration encompasses several enhancements over its predecessor, version 7.2. Noteworthy upgrades include: the implementation of a process-independent electroweak angular-ordered parton shower integrated with QCD and QED radiation; a new recoil scheme for initial-state radiation improving the behaviour of the angular-ordered parton shower; the incorporation of the heavy quark effective theory to refine the hadronization and decay of excited heavy mesons and heavy baryons; a dynamic strategy to regulate the kinematic threshold of cluster splittings within the cluster hadronization model; several improvements to the structure of the cluster hadronization model allowing for refined models; the possibility to extract event-by-event hadronization corrections in a well-defined way; the possibility of using the string model, with a dedicated tune. Additionally, a new tuning of the parton shower and hadronization parameters has been executed. This article discusses the novel features introduced in version 7.3.0.

Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake

AbstractWe propose a novel kinematic rupture modeling procedure for synthesizing broadband ground motions derived from the frequency-wavenumber integration algorithm. This procedure addresses two key issues in characterizing the rupture processes relevant to broadband seismic radiation: an accurate Green's function and a well-constrained kinematic source model. For the first issue, we derive the theoretical Green's function based on an improved dynamic stiffness matrix approach that effectively handles wave propagation in a 1D crustal velocity structure across a broad frequency band. For the second issue, we generate the hybrid source model that combines asperity slip and random slip over the fault plane to effectively implement constraints on the radiated energy during the whole rupture process. The accuracy and effectiveness of the proposed methodology are verified by comparing with the surface acceleration traces and Fourier spectra calculated by spectral element method. With the hybrid source model and crustal velocity structure applicable to the target area, the broadband (0–10 Hz) ground motion of the 2022 MS6.9 Menyuan earthquake is synthesized. The amplitude, duration, and frequency content of the synthetic motions are systematically compared with those of the available observed records and ground motion attenuation relationships, as well as the spatial distribution characteristics of the near-field ground motions from the earthquake scenarios are presented. In conclusion, the case study of the Menyuan MS6.9 earthquake demonstrates that the presented modeling procedure can estimate broadband ground motions rapidly and reliably from a physics-based kinematic rupture perspective.

A cloud 15kV-HDPE insulator leakage current classification based improved particle swarm optimization and LSTM-CNN deep learning approach

Real-time insulator leakage current classification is crucial in preventing the pollution flashover phenomenon and providing appropriate maintenance schedules in high-voltage transmission towers. However, current methodologies only utilize traditional artificial neural networks, which have limitations when performing big data analysis. This research developed a novel cloud 15kV-HDPE insulator leakage current classified framework, utilizing a long short-term memory convolutional neural network (LSTM-CNN). The hybrid model structure is optimized through hyperparameter fine-tuning based on improved particle swarm optimization (IPSO), which reduces human effort and considerable time compared with PSO and random search (RS) techniques. The IPSO-LSTM-CNN model can productively identify correlations between selected weather features and target leakage current levels of 15kV-HDPE insulators. LSTM efficiently captures long-term patterns in sequential data, while CNN layers competently extract high-level dependency in time-invariant information. Four 15kV-HDPE insulators’ datasets, collected in high-voltage transmission lines in the coastal area of Taiwan for more than one year, are deployed for analyzing and comparing classified performance. Other conventional models are developed to evaluate and compare classified performance with the proposed IPSO-LSTM-CNN approach, which acquires the most significant enhancement of 48.08 % loss, 45.91 % validating loss, 52.57 % MAE, 35.47 % validating MAE, 47.34 % MSE, 27.02 % validating MSE, 9.15 % PRE, 3.40 % validating PRE, 4.76 % REC, and 6.17 % validating REC. The experiment outcomes demonstrate that the developed IPSO-LSTM-CNN model acquires improved robustness and accuracy in the leakage current classified capability of 15kV-HDPE insulators.

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application.

A real-world battery state of charge prediction method based on a lightweight mixer architecture

This paper proposes a lightweight model based on the improved Mixer architecture—TrendFlow-Mixer, designed for efficient prediction of the state of charge (SOC) of lithium-ion batteries. SOC accuracy is crucial for the safe operation, usage, and maintenance of power batteries. When predicting the SOC of real-world batteries, the algorithm's generalization, robustness, and computational efficiency across different battery types must be considered. To this end, TrendFlow-Mixer introduces targeted improvements to battery characteristics, significantly alleviating the Mixer architecture's lag in SOC prediction. The model's core structure integrates the Fast Fourier Transform and arrangement transformation convolutional structures. This allows for efficient extraction of domain-specific spatial features and enables cross-channel correlation interactions. This reduces the model's parameter size while enhancing its adaptability to new domains. The model quickly captures data trend changes and identifies data flow patterns, improving computational efficiency by 70 % compared to Transformer-based architectures. TrendFlow-Mixer employs a dual-prediction head, combining rapid feature change analysis with a nonlinear prediction output to generate SOC predictions. This design facilitates fine-tuning of the model's structure and parameters while enhancing overall flexibility. Experimental results show that in a laboratory dynamic condition dataset, the model achieves a minimum mean absolute error (MAE) of 0.11 %, with the maximum prediction error under extreme temperatures controlled within 4.66 %. In validation using real-world data from the BMW i3, the mean absolute percentage error remains around 1 %, with a minimum MAE of 0.465 %. The results demonstrate that TrendFlow-Mixer possesses high computational efficiency, excellent generalization, and adaptability, effectively predicting SOC trends.

Physics-informed deep convolutional network for combined sea ice concentration and velocity prediction

Accurate prediction of short-term fluctuations in Arctic sea ice is important for safe use of Arctic shipping routes. While deep-learning models can improve the accuracy of sea-ice predictions, the accuracy and predictability of short-term sea-ice conditions are limited in most purely data-driven deep-learning models that consider a single aspect of sea ice, without considering the physical variation law between several sea ice factors. We propose dual-task prediction models for sea ice concentration (SIC) and sea ice velocity (SIV), and incorporate a loss function that simultaneously addresses dynamic constraints of SIC and SIV into each model, based on dynamics terms in the sea ice control equation. Comparative experiments are performed to determine which model structure best performs at predicting SIC and SIV. We report a dual-task branching structure to be more suitable for predicting SIC and SIV, and a post-decoder branch network structure to best predict SIC and SIV. Adding a sea ice dynamics equation to the loss function improves the model fit with dynamics law, improving the predictability and prediction accuracy of SIC and SIV.

Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.

Note on higher-group structure in 6d self-dual gauge theory

We analyze higher-group structure of a 6d model coupled with a self-dual 2-form gauge field. This model is defined from 6d axion-electrodynamics with a 1-form Chern-Weil(CW) symmetry gauged dynamically. The gauging leads to a Green-Schwarz-West-Sagnotti(GSWS) term, which gives rise to an anomaly through a GSWS transformation acting on the 2-form gauge field. We cancel this anomaly by gauging a 3-form CW symmetry in 6d axion-electrodynamics. We find out the global symmetries in the resultant model and derive the gauge invariant action in the presence of the background gauge fields. It is argued that a discrete 1-form symmetry is anomalous because turning on the associated background gauge field causes quantum inconsistency due to an operator-valued ambiguity. Higher-group structure in this model that is manifested as a Green-Schwarz-like transformation for CW background gauge fields is discussed.

Dual-grained Lightweight Strategy.

Removing redundant parameters and computations before the model training has attracted a great interest as it can effectively reduce the storage space of the model, speed up the training and inference of the model, and save energy consumption during the running of the model. In addition, the simplification of deep neural network models can enable high-performance network models to be deployed to resource-constrained edge devices, thus promoting the development of the intelligent world. However, current pruning at initialization methods exhibit poor performance at extreme sparsity. In order to improve the performance of the model under extreme sparsity, this paper proposes a dual-grained lightweight strategy-TEDEPR. This is the first time that TEDEPR has used tensor theory in the pruning at initialization method to optimize the structure of a sparse sub-network model and improve its performance. Specifically, firstly, at the coarse-grained level, we represent the weight matrix or weight tensor of the model as a low-rank tensor decomposition form and use multi-step chain operations to enhance the feature extraction capability of the base module to construct a low-rank compact network model. Secondly, unimportant weights are pruned at a fine-grained level based on the trainability of the weights in the low-rank model before the training of the model, resulting in the final compressed model. To evaluate the superiority of TEDEPR, we conducted extensive experiments on MNIST, UCF11, CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet datasets with LeNet, LSTM, VGGNet, ResNet and Transformer architectures, and compared with state-of-the-art methods. The experimental results show that TEDEPR has higher accuracy, faster training and inference, and less storage space than other pruning at initialization methods under extreme sparsity.

Mixed-mode coupling in the red clum. I. Standard single star models

The investigation of global, resonant oscillation modes in red giant stars offers valuable insights into their internal structures. In this study, we investigate in detail the information we can recover on the structural properties of core-helium burning (CHeB) stars by examining how the coupling between gravity- and pressure-mode cavities depends on several stellar properties, including mass, chemical composition, and evolutionary state. Using the structure of models computed with the stellar evolution code we calculate the coupling coefficient implementing analytical expressions, which are appropriate for the strong coupling regime and the structure of the evanescent region in CHeB stars. Our analysis reveals a notable anti-correlation between the coupling coefficient and both the mass and metallicity of stars in the regime $M in agreement with data. We attribute this correlation primarily to variations in the density contrast between the stellar envelope and core. The strongest coupling is expected thus for red-horizontal branch stars, partially stripped stars, and stars in the higher-mass range exhibiting solar-like oscillations ($M While our investigation emphasises some limitations of current analytical expressions, it also presents promising avenues. The frequency dependence of the coupling coefficient emerges as a potential tool for reconstructing the detailed stratification of the evanescent region.

Self-attention network-based state of charge estimation for lithium-ion batteries with gapped temperature data

The accurate estimation of the state of charge (SOC), a critical indicator of the energy stored in lithium-ion batteries, is essential for ensuring reliable and safe battery management. The influence of temperature on the battery characteristics substantially affects the SOC estimation accuracy. Owing to the broad operational temperature range of batteries, it is vital to address the various temperature conditions. This study proposes a model structure for data-driven SOC estimation to enhance accuracy under diverse temperature conditions. The model leverages the analysis of the SOC characteristics derived from the measured data. The proposed structure incorporates parallel-connected self-attention and long-short-term memory modules, thus providing an innovative approach for effectively capturing intricate features in SOC estimation. This study primarily focused on evaluating the capability of the proposed model to achieve satisfactory SOC estimation for untrained temperature conditions when trained with gapped temperature data, thus emphasizing its practicality. To assess the feasibility of the proposed method, experiments were performed under a broad range of fixed and varying temperature conditions, including seasonal and daily changes. The experimental results demonstrated that the root-mean-square errors of the estimated SOC were 0.4101% and 1.5611% at fixed and time-varying temperatures, respectively, including the subzero ranges. These results highlight the robustness of the proposed model under various temperature conditions and its applicability to real-world battery operational temperatures.

A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

ABSTRACTA novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.

Dynamics of the plasma ejection structure in laboratory modeling of young star jets at plasma focus facilities

The use of “plasma focus” type facilities, such as PF-3 (Kurchatov Institute), makes it possible to carry out well-controlled and diagnosable laboratory experiments to study laboratory jets with scale parameters close to the jets of young stars. In this paper, we present the results of numerical modeling of plasma outburst propagation in PF-3. A self-consistent configuration was chosen as the initial conditions, which correctly takes into account the internal structure of the jet. This allowed us to obtain a detailed structure of the interaction between the magnetized emission and the ambient gas. Due to the scalability of such a structure, one should expect such a structure from the head shock waves of jets of young stars.

A semi-empirical model for online real-time control of NOX generation in diesel engines

Diesel NOX emission control is the main control content of diesel engines. Real-time high-precision NOX emission prediction model is the basic guarantee for (1) Diesel combustion management, (2) SCR after-treatment control, (3) OBD system. Existing models that rely exclusively on physical processes or data-driven artificial intelligence models fail to meet real-time requirements, while MAP look-up table-based models are poorly adapted and require extensive experiments for calibration. In this study, a new semi-empirical model for NOX emission prediction was developed, and the variables of the model were identified as exhaust temperature, engine speed, injected fuel mass flow rate, air intake mass flow rate, and exhaust oxygen mass flow rate, taking into account the mechanism of NOX generation in diesel engines as well as the requirement of real-time and direct measurability of the variables for on-line control. The influence of each variable on NOX was analyzed, the model structure of the NOX prediction model was determined, and the parameters of the prediction model were identified by least squares. The model is applied to real operating vehicles for validation and compared with a previous semiempirical model taken from the literature, and the results show that the overall accuracy of the proposed model is improved. Finally, the effect of variable uncertainty on the proposed model was assessed and it was found that the relative uncertainty in predicting NOX emissions ranged between 0 and 8% for sensor accuracies of [Formula: see text] and [Formula: see text] for exhaust temperature and intake air mass flow, respectively, suggesting that the sensor accuracy has a significant effect on the uncertainty of the model.

Bi-channel dynamic topic model for quality monitoring considering initial and additional online customer reviews

E-commerce provides a vast space for user-generated content, including user reviews from the initial use of products or services to subsequent usage. Existing methods mainly focus on unmodified reviews and ignore customer perception after more experience with products. To address this, we propose a Markov chain-based bi-channel dynamic topic model (BDTM), which extends the sequential structure of the dynamic topic model and incorporates initial customer reviews and additional reviews to reflect topic shifts caused by customer experience perception. Then, a control chart based on word mover’s distance (WMD), called a BDTM-WMD (B-W) chart, is proposed to monitor topic shifts under BDTM. An alternative multiscale dynamic topic model (M-K) chart is constructed for comparison. Using a simulation approach, we find that compared with the existing sequential reverse joint sentiment-topic (SRJST) chart and joint sentiment topic-rating meets review (JSTRMR) chart, the proposed B-W chart is more sensitive to small shifts. Case study 1, using real data, shows the advantages of the proposed BDTM, B-W chart and M-K chart under both initial and additional reviews. Case study 2 shows that with additional reviews, the proposed B-W chart triggers out-of-control signals earlier than those by the existing joint sentiment topic-rating meets review (JSTRMR) chart.