Quality Prediction Research Articles

Prediction and analysis of interior sound quality of high-speed trains based on XGBoost

This paper primarily investigates the objective quantification of subjective perceptions of sound quality in high-speed trains. The study measures and analyzes the sound quality in high-speed trains under high-speed operating conditions and calculates psychoacoustic parameters. A subjective evaluation of the interior sound quality is conducted using the grading scale method. An Extreme Gradient Boosting (XGBoost) algorithm is utilized to construct a predictive model for sound quality in high-speed trains and get high predictive accuracy for sound quality evaluation. Employing AI-based evaluation models to evaluate the sound quality in high-speed trains offers a more effective method for studying the interior sound quality of high-speed trains.

PB-Trajectron: Physics bounded neural network for generalized trajectory prediction

Vehicle trajectory prediction is a critical technology in autonomous driving systems, as the quality of prediction directly affects downstream path planning and vehicle control. Although recent studies have shown that models combining kinematic rules with data-driven methods exhibit better interpretability in trajectory prediction, these models often adopt fixed constraint strengths, limiting their generalization ability in diverse traffic scenarios. This fixed constraint strength design restricts the model’s adaptability to different traffic environments.To address this issue, we propose PB-Trajectron, a dynamic integration mechanism that enhances the model’s prediction performance and generalization capability. PB-Trajectron is a dynamic integration mechanism that combines vehicle kinematic rules with deep learning prediction models for generalized vehicle trajectory prediction. The model integrates physics-based motion models and Deep Neural Networks (DNNs) to provide reasonable physical explanations for predicting vehicle trajectories at different speeds. First, we establish two vehicle kinematic constraints applicable to different prediction scenarios, enabling the agent to switch between these constraints based on the causal relationships between vehicle motion states to avoid generating non-physical trajectory results. Second, we propose a kinematic constraint decision framework based on velocity thresholds, which demonstrates adaptive adjustment, allowing the agent to switch tasks according to real-time state conditions and adaptively adjust the contribution of different physical constraints based on actual vehicle speeds. Finally, we investigate the advantages of PB-Trajectron in Out-of-Domain(OOD) generalization.Cross-scenario experiments on the nuScenes dataset show that, compared to previous methods, PB-Trajectron reduces the final displacement error by 7.69% when the prediction step length is 4. Furthermore, out-of-domain generalization tests on the INTERACTION dataset demonstrate that PB-Trajectron achieves a 12.23% reduction in average prediction error on datasets from different countries and scenarios compared to previous methods. The proposed mechanism can better adapt to complex and diverse traffic scenarios, laying the foundation for explainable and robust autonomous driving systems.

Multiparametric ultrasound assessment of axillary lymph nodes in patients with breast cancer

The presence and extent of metastatic disease in axillary lymph nodes (ALNs) in the setting of breast cancer (BC) are important factors for staging and therapy planning. The purpose of this study was to perform a multiparametric sonographic evaluation of ALNs to better differentiate between benign and metastatic nodes. Ninety-nine patients (mean age 54.1 y) with 103 BCs were included in this study, and 103 ALNs were examined sonographically. B-mode parameters, such as size in two dimensions, shape, cortical thickness and capsule outline, were obtained, followed by vascularity assessment via colour Doppler and microflow imaging and stiffness evaluation via shear wave elastography. Postoperative histopathological evaluation was the reference standard. In the statistical analysis, logistic regression and ROC analyses were conducted to search for feature patterns of both types of ALNs to evaluate the prediction qualities of the analysed variables and their combinations. For a cortex larger than 3 mm, without a circumscribed margin of the LN capsule and SWE (E max > 26 kPa), the AUC was 0.823. Multiparametric assessment, which combined conventional US, quantitative SWE and vascularity analysis, was superior to the single-parameter approach in the evaluation of ALNs.

Enhancing VPPA welding quality prediction: A hybrid model integrating prior physical knowledge and CNN analysis

In response to the inconsistency between the features obtained by deep learning models and the quality features reflected by the physical laws of the welding process, this study proposes a solution by integrating a physical prior information model with a CNN model. Initially, the physical laws of the welding process are utilized to annotate the arc, weld pool, and weld seam features relevant to quality, which are then acquired through image processing algorithms, thereby converting the physical laws into a prior information model. Subsequently, this prior information model guides the CNN model for quality recognition, and the CNN model's attention to features is explained through visualization methods to elucidate the relationship between features and quality recognition. Experimental results demonstrate that under the guidance of the prior information model, the CNN model not only automatically focuses on features relevant to quality but also achieves a differential feature attention strategy, thereby improving the recognition accuracy of different outcomes. This research provides a new perspective for deep learning in the field of welding quality recognition.

Dynamic Spatio-Temporal Modeling for Enhanced Air Quality Prediction: Implications for Information Management and Public Health Decision Support Systems

Air quality forecasting has significant implications for environmental monitoring, public health, and information management systems. This study proposes three advanced deep learning models: Graph Neural Networks with Dynamic Spatio-Temporal Attention (GNN-DSTA), Multi-Resolution Convolutional Recurrent Neural Networks (MRC-RNN), and Variational Autoencoders with Spatio-Temporal Latent Embeddings (VAE-STLE). These models address the challenges of capturing complex spatio-temporal dependencies in environments with sparse data samples. The GNN-DSTA model introduces a temporal attention mechanism that dynamically captures evolving spatial-temporal dependencies. MRC-RNN combines CNN's spatial pattern recognition with RNN's temporal modeling across multiple spatial resolutions. VAE-STLE provides a probabilistic framework for robust and interpretable forecasting. Experimental results demonstrate significant improvements in prediction accuracy: GNN-DSTA reduces RMSE by 15-20%, MRC-RNN improves accuracy by 12-15%, and VAE-STLE shows a 10-12% improvement with enhanced uncertainty estimation. These models advance AQI predictions through dynamic attention mechanisms, multi-resolution analysis, and probabilistic forecasting. Furthermore, this study explores the implications of these advanced predictions for information management and public health decision support systems. We discuss the integration of real-time data, scalability considerations for large-scale deployments, user interface design for effective communication of predictions, and ethical considerations in using AI-driven models for public health decision-making. The proposed approach not only enhances the accuracy and reliability of AQI predictions but also provides a framework for developing more effective and responsive public health interventions and environmental policies.

Diffuse reflectance spectroscopy and RGB-imaging: a comparative study of non-invasive haemoglobin assessment

Non-invasive assessment of haemoglobin (Hb) level in blood is a hot spot in the point-of-care biomedical diagnostics. Several optical methods are suggested as a solution, some of them being approved for clinical use. Still, there is no consensus on the accuracy of optical techniques, the quality of Hb assessment on different tissue sites, and on the ability of combined use of several optical techniques to improve the quality of Hb level prediction. In this work we examined the capabilities of two optical techniques—diffuse reflectance spectroscopy and RGB-imaging of the skin and fingernails areas—in detecting low blood Hb level. The test sample consisted of 240 adult volunteers with 70 volunteers exhibiting Hb level lower than 120 g/L. We show that using simple descriptors of the diffuse reflectance spectrum of the forearm skin and fingernails is applicable for predicting low blood Hb concentration (ROC-AUC = 0.84 ± 0.08), while RGB-imaging shows similar performance when applied to the fingernail areas (ROC-AUC = 0.83 ± 0.07), which can be considered perspective for clinical use and screening properties. We also report that while the joint use of predictions from two optical methods slightly improves the accuracy of non-invasive Hb level assessment (ROC-AUC = 0.86 ± 0.07), the effect is not as high as one might expect from combining predictions of truly independent modalities, indicating the limit of the accuracy one can expect with multimodal optical approach. We review this case and propose possible solutions towards more sensitive non-invasive optical determination of hemoglobin.

Quality prediction for rocket body structure manufacturing process based on improved ELM

In the current multi-variety and small-batch production mode, this paper proposes a quality prediction method for rocket body structure manufacturing process based on improved extreme learning machine (ELM). Aiming at the non-optimal problems caused by random selection of input layer weights and hidden layer bias of ELM, particle swarm optimization (PSO) is combined with gravitational search algorithm (GSA) to form a hybrid algorithm to optimize the random parameters of ELM. Taking a weld in a rocket body structure as an example, the weld width prediction model based on improved ELM is established and verified. The results show that compared with the prediction model based on the standard ELM and the prediction model based on PSO-ELM, the prediction model based on improved ELM has higher prediction accuracy, and its prediction deviation does not exceed 2.5%, which can effectively predict the weld width.

A parallel and multi-scale probabilistic temporal convolutional neural networks for forecasting the key monitoring parameters of gas turbine

As a crucial equipment in the power industry, gas turbines need effective condition monitoring techniques to maintain safe and reliable operations. Fast yet accurate long-term forecasting of the key monitoring parameters of gas turbine is vital for achieving the overall effective condition monitoring. This however poses challenges in terms of both efficacy and efficiency for conventional time series models like recurrent neural networks (RNN), since they run in a sequential manner. To this end, this paper introduces a novel parallel probabilistic time series prediction model. Particularly, the proposed model leverages the parallelism of temporal convolutional neural network (TCN) and exploits the power of latent space in the Bayesian framework. Besides, a multi-scale feature extraction strategy based on dense connections and a lagged observation strategy are presented to improve the model performance. In the comparative study against state-of-the art competitors on four classical system identification benchmarks, the proposed model significantly reduces the training time, while consistently achieving highly accurate predictions and providing appropriate uncertainty quantification. Thereafter, the proposed model is adopted to build a systematic workflow for the fast yet accurate long-term forecasting of the temperature of blade channel of gas turbine. The comprehensive results again highlight the superiority of the proposed model in terms of both the prediction quality and the training efficiency.

Increasing the prediction quality of artificial neural network–based optimisation approaches for the sheet moulding compound compression moulding process by means of targeted expansion of training data

The resulting fibre orientation when pressing Sheet Moulding Compound (SMC) is essentially dependent on the position and geometry of the resin mat in the mould. It is desirable to adapt the fibre orientation as best as possible to best withstand the applied load. The relationship between the position and geometry of the resin mat and the deflection of the final part under a specific load can be mapped using a numerical simulation; nevertheless, an optimisation is not feasible based on the simulation in an economically reasonable time frame due to the required computing time. For this reason, a metamodel based on an ensemble of artificial neural networks (ANN) was created on the basis of the simulation data, and an optimisation based on these ANN was set up. However, the investigations show that the minimal deflections found on the basis of the metamodel through optimisation have large deviations compared to the simulation. This leads to the hypothesis that the applied optimisation finds weak points of the metamodel instead of actual minima. From this hypothesis, an extension approach of the data set on which the metamodel is based by the weak points found is derived and implemented. This is done in an iterative process. The results show that the goal of increasing the prediction quality of the optimisation approach is achieved by the developed approach. The root mean square error (RMSE) could be reduced from the original deflection of 25 to 7 mm, which corresponds to a reduction of 72%. It is shown that the presented approach is successful in increasing prediction quality; nevertheless, further measures are needed to reach decent prediction quality.

Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake

Accurate prediction of water quality that is essential for effective lake management requires a comprehensive understanding of influential factors in the ’land-river-lake’ continuum. We develop a novel deep learning model employing a Long Short-Term Memory (LSTM) model for predicting water quality, and apply it for a large plateau lake, Lake Dianchi. The model comprises two stages: the first establishes a correlation between land and rivers by predicting nutrient loads, incorporating socioeconomic and meteorological features. The second establishes a correlation between feeding rivers and the lake by predicting nutrient concentrations, taking water quality, meteorological conditions, riverine import, and agricultural Non-Point Source (NPS) loads into consideration. Compared to single-stage deep learning models developed using classical methods, the two-stage model reduces Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), indicating enhanced prediction accuracy. Shapley Additive Explanation (SHAP) analysis is further employed to identify key contributing features. Precipitation emerges as the primary feature in the first stage, while in the second stage, disparities in the most contributing features for two zones of the lake are revealed due to dam isolation and water diversion projects. In the main zone, the primary predicting features are temperature and agricultural NPS features, while in the dam-isolated zone, Secchi depth is the key predictor, highlighting spatial–temporal variations in driving features. Our model contributes a novel deep learning framework and valuable insights for water quality management in large lakes, emphasizing the importance of considering the dynamic interplay of features or factors in the ’land-river-lake’ system.

Effect of ESG rating disagreement on stock price informativeness: Empirical evidence from China's capital market

We assess the level of environmental, social, and governance (ESG) rating disagreement among Chinese listed companies using ESG data from eight ESG rating providers and examine the impact of ESG rating disagreement on stock price informativeness. Our results indicate a significant and positive correlation between ESG rating disagreement and stock price synchronicity. This suggests that different ESG ratings impede the integration of company-specific details into stock prices, thereby reducing the stock price's informativeness. The opacity of corporate information and agency costs can both be used to interpret this result. We also explore the transmission channels through which ESG rating disagreement affects stock price informativeness. Our findings reveal that such disagreement lowers the quality of analysts' predictions and impedes investors' access to information, affecting the informativeness of stock prices. Furthermore, we observe that ESG rating disagreement has a greater impact on stock price synchronicity for companies with high ESG ratings and those operating in highly competitive industries. Increased media coverage would mitigate the effect of ESG rating disagreement on stock price synchronicity. Overall, our findings contribute to the literature on ESG rating disagreement and demonstrate the critical role of ESG rating disagreement in the informativeness of stock prices.

Proactive scheduling for mmWave Wireless LANs

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.

Sleep as a Predictor of Health-Related Quality of Life among Economically Disadvantaged Black Older Adults.

Sleep disturbances may partially account for the health-related quality of life (HRQoL) disparities experienced by Black older adults when compared to non-Hispanic White (NHW) adults. The present study examined the role of self-reported sleep duration and the belief that one is not getting enough sleep on physical and mental HRQoL among Black older adults. Participants were 281 community-dwelling, economically disadvantaged Black older adults between 60 and 97 years of age (Mean=69.01, SD=6.97) who lived in a large city in the Southeastern United States. The present study uses baseline data from a larger intervention study aimed at promoting social connection and food security among older adults. For this study (and as part of the larger intervention), participants completed an assessment battery that included (1) a demographic data and health questionnaire that included self-reported sleep duration and a belief that one is not getting enough sleep questions; (2) the CDC (Centers for Disease Control and Prevention) Health-Related Quality of Life-14 Healthy Days Core Module; and (3) the World Health Organization Quality of Life-Brief Form. Descriptive results show that most participants slept less than 7 hours and felt like they did not get enough sleep. Results from 2 hierarchical regressions also showed that believing one is not getting enough sleep predicts lower self-reported mental and physical HRQoL. While sleep deprivation has a serious impact on quality of life for Black older adults, sleep disturbances in this population are understudied. Interventions to improve sleep duration and quality among Black older adults may help reduce disparities in quality of life between Black older adults and NHW adults.

Metabolite profiles of green leaves and coffee beans as predictors of coffee sensory quality in Robusta (Coffea canephora) germplasm from the Democratic Republic of the Congo

Research on the chemical composition of coffee beans and its correlation with sensory quality is advancing, but the metabolites in coffee leaves have often been overlooked. This study investigated the metabolite profiles of roasted coffee, green beans, and coffee leaves from 39 C. canephora genotypes through LC-HRMS with an untargeted metabolomics approach. Our results showed that metabolite profiles of roasted coffee, green beans, and coffee leaves can be discriminated based on the coffee's sensory quality. The highest predictive power of coffee sensory quality was achieved with the metabolite profiles of the coffee leaves. Genotypes with varying genetic backgrounds could only be discriminated by the metabolite profiles of the coffee leaves. Metabolite marker compounds predictive of sensory quality were a putative quercetin derivate in green beans and likely physagulin E and argophyllin in coffee leaves. Estimated levels of caffeoylquinic acids, dicaffeoylquinic acids, and caffeoylquinic acid lactones were higher in roasted coffee with a lower sensory quality. These differences were not apparent in their respective green beans and coffee leaves. Overall, our study revealed the promising potential of the metabolite profile of coffee leaves as a predictive tool for coffee sensory quality and the genetic background of C. canephora.

Machine learning discrimination and prediction of different quality grades of sauce-flavor baijiu based on biomarker and key flavor compounds screening

The quality grade of base Baijiu directly determines the final quality of sauce-flavor Baijiu. However, traditional methods for assessing these grades often rely on subjective experience, lacking objectivity and accuracy. This study used GC-FID, combined with quantitative descriptive analysis (QDA) and odor activity value (OAV), to identify 27 key flavor compounds, including acetic acid, propionic acid, ethyl oleate, and isoamyl alcohol etc., as crucial contributors to quality grade differences. Sixteen bacterial biomarkers, including Komagataeibacter and Acetobacter etc., and 7 fungal biomarkers, including Aspergillus and Monascus etc., were identified as key microorganisms influencing these differences. Additionally, reducing sugar content in Jiupei significantly impacted base Baijiu quality. Finally, 11 machine learning classification models and 9 prediction models were evaluated, leading to the selection of the optimal model for accurate quality grade classification and prediction. This study provides a foundation for improving the evaluation system of sauce-flavor Baijiu and ensuring consistent quality.

A procedure for assessing of machine health index data prediction quality

The paper discusses the challenge of evaluating the prognosis quality of machine health index (HI) data. Many existing solutions in machine health forecasting involve visual assessing of the quality of predictions to roughly gauge the similarity between predicted and actual samples, lacking precise measures or decisions. In this paper, we introduce a universal procedure with multiple variants and criteria. The overarching concept involves comparing predicted data with true HI time series, but each procedure variant has a specific pattern determined through statistical analysis. Additionally, a statistically established threshold is employed to classify the result as either a reliable or non-reliable prognosis. The criteria include both simple measures (MSE, MAPE) and more advanced ones (Space quantiles-inclusion factor, Kupiec’s POF, and TUFF statistics). Depending on the criterion chosen, the pattern and decision-making process vary. To illustrate effectiveness, we apply the proposed procedure to HI data sourced from the literature, covering both warning (linear degradation) and critical (exponential degradation) stages. While the method yields a binary output, there is potential for extension to a multi-class classification. Furthermore, experienced users can use the quality measure expressed in percentage for more in-depth analysis.

Emotional intelligence: a novel predictor of quality of life in patients with systemic sclerosis.

Systemic sclerosis (SSc), a chronic systemic autoimmune disease, affects skin and internal organs compromising organ function and leading to significant morbidity and poor health-related quality of life (HrQoL). This cross-sectional study investigated whether HrQoL is influenced by trait emotional intelligence (TEI). Sixty patients with SSc (Female: 86.67%) completed the socio-demographic characteristics form, TEI Questionnaire Short-Form (TEIQue-SF), and Short-Form Health Survey (SF-36). Sixty healthy controls were also completed the TEIQue-SF. A series of multiple linear regression analyses with correlation matrix was used to analyze SF-36 domains as dependent variables with TEI domains (well-being, self-control, emotionality, sociability) as independent variables. The average age of participants was 57.3 ± 12.9 years with a mean disease duration of 7.7 ± 6.7 years. Patients differed from controls in the sociability domain of TEI. TEI global was found to affect the physical and mental component summaries (p < .001), and all 8 dimensions of the HrQoL (p < .001). Age, disease duration, and gastrointestinal manifestations were negatively associated with various components of SF-36. TEI was positively associated with all dimensions of HrQoL. Understanding the relationship between TEI and HrQoL dimensions is important for the support and empowerment of SSc patients, as well as the establishment and implementation of appropriate psychotherapeutic interventions.

Monitoring and warning for ammonia nitrogen pollution of urban river based on neural network algorithms.

Ammonia nitrogen (AN) pollution frequently occurs in urban rivers with the continuous acceleration of industrialization. Monitoring AN pollution levels and tracing its complex sources often require large-scale testing, which are time-consuming and costly. Due to the lack of reliable data samples, there were few studies investigating the feasibility of water quality prediction of AN concentration with a high fluctuation and non-stationary change through data-driven models. In this study, four deep-learning models based on neural network algorithms including artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were employed to predict AN concentration through some easily monitored indicators such as pH, dissolved oxygen, and conductivity, in a real AN-polluted river. The results showed that the GRU model achieved optimal prediction performance with a mean absolute error (MAE) of 0.349 and coefficient of determination (R2) of 0.792. Furthermore, it was found that data preprocessing by the VMD technique improved the prediction accuracy of the GRU model, resulting in an R2 value of 0.822. The prediction model effectively detected and warned against abnormal AN pollution (> 2mg/L), with a Recall rate of 93.6% and Precision rate of 72.4%. This data-driven method enables reliable monitoring of AN concentration with high-frequency fluctuations and has potential applications for urban river pollution management.

TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment.

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this article, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios.

Air Quality Prediction and Control Systems Using Machine Learning and Adaptive Neuro-Fuzzy Inference System

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.