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Ferroelectric materials showing piezoelectricity, pyroelectricity and other functional properties have been found a variety of applications in electrical and electronic devices. These properties highly rely on polarization states in multi-scale structures, including ferroelectric domains mainly in mesoscopic scale, domain walls in microscopic scale and so on. However, it is still lack of effective method to characterize multi-scale polarization states simultaneously in ferroelectric materials. Here, we proposed a data-driven cross-scale polarization state recognition method based on scanning convergent beam electron diffraction (SCBED) to characterize the complicated polarization states in a PbZr 0.4 Ti 0.6 O 3 ceramic. This method employed a deep learning model to interpret the extensive dataset of CBED patterns generated during the scanning process and further validated by atomic resolution transmission electron microscope (ARTEM). The data-driven SCBED method provided a novel strategy for characterizing and interpreting the complicated cross-scale structure frame in ferroelectric materials. [ABSTRACT FROM AUTHOR]

The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a data-driven surrounding rock classification (SRC) model integrating machine learning algorithms. Initially, the box-counting method was introduced to calculate the fractal dimension of discontinuity from the excavation face image. Subsequently, crucial parameters affecting surrounding rock quality were analyzed and selected, including rock strength, the fractal dimension of discontinuity, the discontinuity condition, the in-situ stress condition, the groundwater condition, and excavation orientation. This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. Evaluation indicators, including 5-fold cross-validation, precision, recall, F1-score, micro-F1-score, macro-F1-score, accuracy, and the receiver operating characteristic curve, demonstrated the GBDT-BO model's superior robustness in learning and generalization compared to other models. Furthermore, four additional excavation face cases validated the intelligent SRC approach's practicality. Finally, the synthetic minority over-sampling technique was employed to balance the training set. Subsequent retraining and evaluation confirmed that the imbalanced dataset does not adversely affect SRC model performance. The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support. [ABSTRACT FROM AUTHOR]

The manufacturing industry must maintain high-quality standards while meeting customer demands for customization, reduced carbon footprint, and competitive pricing. To address these challenges, companies are constantly improving their production processes using quality management tools. A crucial aspect of this improvement is the root cause analysis of manufacturing defects. In recent years, there has been a shift from traditional knowledge-driven approaches to data-driven approaches. However, there is a gap in the literature regarding a systematic overview of both methodological types, their overlaps, and the challenges they pose. To fill this gap, this study conducts a scoping literature review of root cause analysis in manufacturing, focusing on both data-driven and knowledge-driven approaches. For this, articles from IEEE Xplore, Scopus, and Web of Science are examined. This review finds that data-driven approaches have become dominant in recent years, with explainable artificial intelligence emerging as a particularly strong approach. Additionally, hybrid variants of root cause analysis, which combine expert knowledge and data-driven approaches, are also prevalent, leveraging the strengths of both worlds. Major challenges identified include dependence on expert knowledge, data availability, and management issues, as well as methodological difficulties. This article also evaluates the potential of artificial intelligence and hybrid approaches for the future, highlighting their promises in advancing root cause analysis in manufacturing. [ABSTRACT FROM AUTHOR]

Background: The blood supply chain (BSC) is crucial for providing safe and sufficient blood, but it faces numerous challenges and needs to be robust and resilient. This study provides a comprehensive model for managing and optimizing the BSC in real-world scenarios, including emergency and routine circumstances and with consideration of health equity concepts. Method: Classic time-series models are applied to predict future supply chain circumstances, addressing uncertainty in blood demand and the need for timely supply. A structured framework and medical preferences are prioritized to optimize distribution, minimize blood shortages, minimize wastage due to expiry, and maximize blood freshness. Genetic algorithms (GA) and particle swarm optimization (PSO) are used to solve mathematical models quickly and efficiently, ensuring reliable operation. Result: The model's outcomes can effectively meet the daily needs of the BSC and assist decision-makers managing blood inventory and distribution, improving robustness and resilience. Conclusions: Utilizing weights allows for the effective management of each objective function to convert the model into a single-objective mixed-integer linear programming (SO-MILP) based on unique conditions, enabling the system to self-adjust for optimal performance, boosting the sustainability of the blood supply chain, and promoting the principle of health equity under diverse real-world settings. [ABSTRACT FROM AUTHOR]

ABSTRACT We are surrounded by overwhelming big data, which brings substantial advances but meanwhile poses many challenges. A very large portion of big data contains geospatial information and hence geospatial big data, which is crucial for decision making if being utilized strategically. Among others, volumes in size and high dimensions are two major challenges that prevent strategic decision making from geospatial big data. Interactive map‐based and geovisualization enabled web applications are intuitive and useful to construct knowledge. More importantly, such interactive web map applications are powerful to intuitively reveal insights from high‐dimensional geospatial big data for actionable decision making. We propose an interactive and data‐driven web mapping framework, named dciWebMapper, for visualizing and sensing high‐dimensional geospatial (big) data in an interactive and scalable manner. To demonstrate the wide applicability and usefulness of our framework, we have applied our dciWebMapper framework to three real‐world case studies and implemented three corresponding web map applications: iLit4GEE‐AI, iWURanking, and iTRELISmap. We expect and hope the three web maps demonstrated in different domains, from literature big data analysis to world university ranking to scholar mapping, will provide a good start and inspire researchers and practitioners in various domains to apply our dciWebMapper to solve and/or aid in solving impactful problems. [ABSTRACT FROM AUTHOR]

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies. [ABSTRACT FROM AUTHOR]

A new avenue by transforming the conventional electrochemical domain into an electro‐chemo‐mechanical domain is proposed to achieve less hysteretic fast‐charging based on the three pictures: i) electro‐chemo‐mechanics, ii) phase transition kinetics, and iii) ionic kinetics. Each concept is demonstrated leading to the electrochemical improvement using data‐driven computation and experimental analyses, and its novel framework is generalized based on alkali‐ion chalcogenide models. The mechanical strain lowers and delays the electrochemical yield point, which gives rise to extending the elastic region and enhancing the phase and ionic kinetics. This is experimentally verified with less hysteresis upon (dis)charging for all models. Implementing the electro‐chemo‐mechanical domain is central in alkali‐ion chalcogenide batteries and broadens its application in other rechargeable batteries, ultimately playing a critical role in providing practical fast‐charging solutions. [ABSTRACT FROM AUTHOR]

Glass transition is one of the most crucial physical properties for polymerical materials. As a typical complex polymerical material, the glass transition phenomenon in asphalt binder is directly related to their temperature-related properties. To investigate the glass transition characteristics, this study delves into the glass transition temperature of asphalt binder based on molecular dynamics simulations. It is found that the calculation range for the glass transition temperature sits between 100 and 400 K. The evolution of asphalt binder structure is influenced by different cooling rates, where lower cooling rates allow sufficient microstructural rearrangement, resulting in a smaller volume at the lower temperature. Model size is closely associated with the glass transition region. As the size increases, the transition region significantly expands. Increasing the model size also reduces volume fluctuations after isothermal relaxation, providing more stable volume changes. It is observed that higher cooling rates with a model size over 100 Å can well reproduce the glass transition process of asphalt binders. This work provides atomic-scale insights for the glass transition phenomenon in asphalt binder, which could be beneficial for the design of high-performance asphalt binder. [ABSTRACT FROM AUTHOR]

Objective: Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones to aid planning and enable effective treatments. Materials and methods: Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor's estimates. Results: The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49–67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes (p = 0.169) unlike the vendor's estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown. Conclusions: We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achieving complete treatments, and reduce tumor recurrence. Clinical relevance statement: Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence. Key Points: Reliable tools to predict the extent of ablation treatments are currently lacking. Our novel patient-specific deep-learning algorithm was shown to predict ablation zones with higher accuracy and less bias compared to the currently used estimates provided by applicator vendor. Our method for ablation prediction allows for real-time clinical deployment, with potential for improved treatment planning and reduced tumor recurrence. [ABSTRACT FROM AUTHOR]

Due to the multiple physics involved and their mutual and complex interactions, nuclear engineers and researchers are constantly working on developing highly accurate Multi-Physics models, focusing in particular on the core coupling of Neutronics and Thermal-Hydraulics. Nevertheless, the development of accurate and stable models remains a challenging task despite the advancements in computational hardware and software. This work investigates the possibility of combining the available mathematical model with data collected on physical systems, with a two-fold goal: improvement of the performance of the former from the computational point of view without sacrificing accuracy and performing model bias correction with the knowledge coming in real-time from the system. In particular, two Data-Driven Reduced Order Modelling techniques, the Generalised Empirical Interpolation Method and the Parametrised-Background Data-Weak formulation, are applied to literature benchmark nuclear case studies, as they were observed to be quite well suited for the chosen cases. The main goal of this work is to assess the possibility of using external data to perform model bias correction: starting from a purposefully less accurate, but computationally cheaper base model, then using high-fidelity data to update and correct the model efficiently. Indeed, the numerical results obtained in this paper are promising, confirming the feasibility of this approach to develop computationally cheap and accurate multi-physics models; furthermore, investigation of Data-Driven Reduced Order Modelling approach to nuclear industrial cases, in the context of model bias correction, is foreseen. • State estimation using Data-Driven Reduced Order Modelling. • Solution of low-fidelity Multi-Physics models embedding Reduced Order Modelling approaches. • Update/correction of Multi-Physics models using higher-fidelity data. • Enhancing Multi-Physics modelling of nuclear reactors with data from different fidelity levels. [ABSTRACT FROM AUTHOR]

Due to the important strategic position of Pumped Storage Power Plants (PSPP) in global energy upgrading, conducting in-depth research on the various operating conditions of pump turbine units is important for their safe and stable operation. This study sought to clarify the gas–liquid phase motion and the nonlinear chaotic characteristics of the process of exhaust and pressurization in pump mode; with the simplified objective model proposed here, a visualization of the process is achieved using data-driven methods, and the nonlinear characteristics of gas–liquid phase motion during the process are theoretically demonstrated. A method that combines data-driven and chaotic analysis is proposed to qualitatively and quantitatively analyze the force and torque time- series signals of the runner under different exhaust rates. The results indicate that the chaotic characteristics of the force signals and torque signals of the runner are not in a single linear relationship with the exhaust rates. Therefore, this research also provides guidance on exhaust rates with the aim of informing actual engineering practice, the purpose of which is to reduce the vibration amplitude caused by repetitive torque and improve the stability of the unit operations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Proton exchange membrane fuel cell (PEMFC) has become a hotspot due to its high efficiency, compact structure, and good dynamic operation efficiency. However, problems such as poor reliability and short lifespan create bottlenecks in its large‐scale applications. There has been a large amount of research on fault diagnosis and health management techniques dedicated to addressing the lifespan issues of PEMFC systems. This article provides an in‐depth analysis on the fault mechanism of PEMFC and systematically sorts out the types, causes, and impacts of faults. On this basis, the research progress of PEMFC fault diagnosis technology is summarized, and the measurement characterization methods for fuel cell status monitoring and fault detection are summarized. The literatures of model‐based and data‐driven fault identification methods are summarized and compared. The relevant mitigation measures for PEMFC faults are discussed. Finally, based on the challenges in the current research of fault diagnosis, people mainly conduct research on fault model, online diagnostic technology, and improving diagnostic mechanisms. Overall, this article can provide useful summary and guidance for future research. [ABSTRACT FROM AUTHOR]

Treatment based on syndrome differentiation is the core diagnostic and therapeutic thinking of traditional Chinese medicine (TCM), which is the key to determine clinical efficacy. Nowadays, research based on clinical data is the main method to explore the law of TCM syndrome and treatment, but the internal relationship of the key factors of "disease-syndrome-formula-medicine-effect" has not been truly and comprehensively analyzed, resulting in low clinical value of research results. Therefore, the author systematically sorted out the core problems of poor matching between electronic medical record and clinical research, the effect of data governance on data accuracy, difficulties to discover the law of TCM syndrome and treatment by data analysis methods. In addition, in the context of data driven, the big data platform of TCM clinical research should be established, and the data governance and analysis technology with artificial intelligence as the core should be developed, so as to realize the integration of clinical practice and research, providing new ideas and methods for the research of the law of TCM syndrome and treatment and promoting the development of TCM.

Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau. Endemic abnormal groundwater fluoride ( F - ) lacks spatiotemporal studies. This research integrates geospatial distribution, geochemical signatures, and data driven method for evaluating F - levels and population at risk. Groundwater F - ranged from 0 to 3.4 mg/l in (n = 100) with 52% samples found unfit for drinking. Through geospatial IDW tool hotspot areas affected with low and high groundwater F - levels were identified. Geochemical distribution in geological setups recognized sediment variation leads to high F - (NaHCO3) and low F - (CaHCO3) water types in low elevation (central plain) and high elevation (mountain foot) respectively. Results of the modified water quality index identified 60% samples to be unsuitable for drinking. Support vector machine (SVM), random forest regression (RFR) and classification and regression tree (CART) machine learning models found Na + , Salinity and Ca + 2 as important contributing variables in groundwater F - prediction. CART model with R2 value of 0.732 outperformed RFR and SVM in predicting F - . Noncarcinogenic health risk vulnerability from F - increased from Adults < Teens < Children < Infants. Infants and children with hazard quotient values of 11.3 and 4.2 were the most vulnerable population at risk for consuming F - contaminated groundwater. The research emphasizes on both nutritional need and hazardous effect of F - , and development of desirable limit for F - . [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of big data and artificial intelligence technology, data-driven automatic driving vehicle lane change intention recognition has become an active research area in the transportation field. Numerous studies have reported innovative and practical research results. However, this field still presents common technical challenges, such as accurately identifying the lane change process, handling missing lane change labels, and addressing imbalanced data categories. These issues remain the focal points of current research. This paper aims to classify and organize various data-driven methods, mainly focusing on lane change intention recognition methods based on traditional machine learning, deep learning, and ensemble learning. In the academic community, two primary approaches exist for identifying lane change behavior. The first approach mainly focuses on the vehicle not crossing the lane line, which is suitable for early recognition of the driver’s intention to change lanes. The second approach focuses on the actual crossing of lane markings by vehicles, which is often considered the complete lane change process. In academic research on lane change intention annotation, the selection of fixed time windows and heading angle thresholds plays a crucial role in the accuracy of annotation. These parameters affect the accurate recognition of lane change behavior and are directly related to the stability and reliability of autonomous driving and intelligent transportation system performance. Therefore, researchers have conducted in-depth investigations on the impact of these two parameters on annotation accuracy. To identify the optimal fixed time window and heading angle threshold, researchers have used the grid search optimization algorithm. This method performs well in fixed driving scenarios by traversing all possible parameter combinations and selecting the optimal parameters according to preset evaluation criteria. However, in practical applications, driving scenarios often exhibit diversity and complexity. Different driving environments, road conditions, and driving styles can impact the recognition of lane change intentions. Therefore, achieving adaptive parameter adjustment so that the annotation algorithm maintains high accuracy across various driving scenarios remains a challenging problem. To address the issue of imbalanced data categories in lane changing, researchers adopt two strategies. The first strategy involves adjusting the data sampling method, and under-sampling and oversampling techniques are used to balance the number of samples in each category. The second strategy involves the use of classification models with strong adaptability to imbalanced data, such as ensemble learning algorithms or cost-sensitive learning models, to maintain good classification performance.

Due to the important strategic position of Pumped Storage Power Plants (PSPP) in global energy upgrading, conducting in-depth research on the various operating conditions of pump turbine units is important for their safe and stable operation. This study sought to clarify the gas–liquid phase motion and the nonlinear chaotic characteristics of the process of exhaust and pressurization in pump mode; with the simplified objective model proposed here, a visualization of the process is achieved using data-driven methods, and the nonlinear characteristics of gas–liquid phase motion during the process are theoretically demonstrated. A method that combines data-driven and chaotic analysis is proposed to qualitatively and quantitatively analyze the force and torque time-series signals of the runner under different exhaust rates. The results indicate that the chaotic characteristics of the force signals and torque signals of the runner are not in a single linear relationship with the exhaust rates. Therefore, this research also provides guidance on exhaust rates with the aim of informing actual engineering practice, the purpose of which is to reduce the vibration amplitude caused by repetitive torque and improve the stability of the unit operations.

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data‐driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization‐back propagation neural network (PSO‐BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within ±10, ±15, and ±20 °C, respectively, and the PSO‐BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO‐BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter. [ABSTRACT FROM AUTHOR]

Reference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around 20%$$ 20\% $$ reduced weighted mean absolute percentage error. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to establish a systematic data-driven analysis that exemplifies a precious reverse logistic portrayal toward industry 4.0 and extension impute for sustainable growth due to there are plenty of indicators unveils among the massive diffusion of reverse logistic and industry 4.0 in extending literature. A combination of content analysis, networking analysis, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and Choquet integral is adopted. This study contributes to insight on a valid hierarchical structure of reverse logistic toward industry 4.0, explore the causal inter-relationships among the attributes and identify the critical attributes for achieving sustainable performance, and identify the decisive reverse logistic activities in electronic industry practices in Vietnam. The result shows that sustainable circularity, smart municipality and digitalising accessibility are causal aspects with potential opportunities, challenges for prominent improvement. The digitalising accessibility is emphasised as the most important aspect in the structure. The collection stage and disposition stage are indicated to be top prioritised to enhance the electronic reverse logistic practices toward the industry 4.0 in Vietnam. [ABSTRACT FROM AUTHOR]

The organization and operation of public transport have not escaped the impact of changes taking place worldwide which are driven and influenced by the rapid spread of disruptive technologies and efforts related to climate protection and sustainable development. The challenges posed by the proliferation of electric vehicles (EVs) - including electric buses (BEBs) - require new solutions and new ways of thinking, and a more holistic and innovative operating model for road transport companies using buses. In this model, the authors present a methodology for the delivery of contracted scheduled services that, as part of a systems approach, links a mixed fleet and its appropriate route plan within a data-driven circular operational model, allowing room for innovative energy solutions, the use of renewable energy sources and for satisfaction of increasingly demanding climate and sustainability requirements, all using a logistics science approach. [ABSTRACT FROM AUTHOR]

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases. [ABSTRACT FROM AUTHOR]

Severe ice accretion on transmission lines can disrupt electrical grids and compromise the stability of power systems. Consequently, precise prediction of ice coating on transmission lines is vital for guiding their operation and maintenance. Traditional single-model icing prediction methods often exhibit limited accuracy under varying environmental conditions and fail to yield highly accurate predictions. We propose a multi-scenario, two-stage adaptive ensemble strategy (MTAES) for ice coating prediction to address this issue. A combined clustering approach is employed to refine the division of ice weather scenarios, segmenting historical samples into multiple scenarios. Within each scenario, the bagging approach generates multiple training subsets, with the extreme learning machine (ELM) used to build diverse models. Subsequently, a two-stage adaptive weight allocation mechanism is introduced. This mechanism calculates the distance from the scenario cluster centers and the prediction error of similar samples in the validation set for each test sample. Weights are dynamically allocated based on these data, leading to the final output results through an adaptive ensemble from the base model repository. The experimental results show that the model is significantly better than traditional models in predicting ice thickness. Key indicators of RMSE, MAE, and  R 2  reach 0.675, 0.522, and 83.2%, respectively, verifying the effectiveness of multi-scene partitioning and adaptive weighting methods in improving the accuracy of ice cover prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quality of knowledge graphs (KGs) significantly influences their utility in downstream applications. Traditional methods for enhancing KG quality typically involve manual efforts and knowledge pattern learning to detect errors and complete missing triples. These approaches often incur high manual costs. To address these challenges, this paper proposes a novel "data-driven" approach to KG improvement. This method utilizes numerical data records to validate and enhance the information within KGs, overcoming limitations such as the requirement for a robust internal structure of KGs or the scarcity of expert resources. A pioneering technique that integrates Markov Boundary discovery with correlation analysis of data properties is developed in this study. This technique aims to identify and correct errors, as well as to fill in missing components of the KGs. To evaluate the effectiveness of this approach, experimental analysis was conducted, highlighting its potential to significantly improve KG accuracy and completeness. This data-driven strategy reduces reliance on extensive manual intervention and expert knowledge, introducing a scalable way to refine KGs using empirical data. The results from the experiments demonstrate the capability of this method to enhance the quality of KGs, marking it as a valuable contribution to the field of knowledge management. [ABSTRACT FROM AUTHOR]

This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The algorithm comprises a particle filter-enabled state estimation component, estimating the controlled system's state via indirect observations, alongside an efficient stochastic maximum principle-type optimal control solver. By integrating weak convergence techniques for the particle filter with convergence analysis for the stochastic maximum principle control solver, we derive a weak convergence result for the optimization procedure in search of optimal data-driven feedback control. Numerical experiments are performed to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

The rapid development of the metaverse has sparked extensive discussion on how to estimate its development maturity using quantifiable indicators, which can offer an assessment framework for governing the metaverse. Currently, the measurable methods for assessing the maturity of the metaverse are still in the early stages. Data-driven approaches, which depend on the collection, analysis, and interpretation of large volumes of data to guide decisions and actions, are becoming more important. This paper proposes a data-driven approach to assess the maturity of the metaverse based on K-means-AdaBoost. This method automatically updates the indicator weights based on the knowledge acquired from the model, thereby significantly enhancing the accuracy of model predictions. Our approach assesses the maturity of metaverse systems through a thorough analysis of metaverse data and provides strategic guidance for their development. [ABSTRACT FROM AUTHOR]

Accurate runoff prediction is crucial for watershed water resource management, flood prevention, and hydropower station scheduling. Data-driven models have been increasingly applied to runoff prediction tasks and have achieved impressive results. However, existing data-driven methods may produce unreasonable predictions due to the lack of prior knowledge guidance. This study proposes a multivariate runoff prediction model that couples knowledge embedding with data-driven approaches, integrating information contained in runoff probability distributions as constraints into the data-driven model and optimizing the existing loss function with prior probability density functions (PDFs). Using the main stream in the Yellow River Basin with nine hydrological stations as an example, we selected runoff feature factors using the transfer entropy method, chose a temporal convolutional network (TCN) as the data-driven model, and optimized model parameters with the IPSO algorithm, studying univariate input models (TCN-UID), multivariable input models (TCN-MID), and the coupling model. The results indicate the following: (1) Among numerous influencing factors, precipitation, sunshine duration, and relative humidity are the key feature factors driving runoff occurrence; (2) the coupling model can effectively fit the extremes of runoff sequences, improving prediction accuracy in the training set by 6.9% and 4.7% compared to TCN-UID and TCN-MID, respectively, and by 5.7% and 2.8% in the test set. The coupling model established through knowledge embedding not only retains the advantages of data-driven models but also effectively addresses the poor prediction performance of data-driven models at extremes, thereby enhancing the accuracy of runoff predictions. [ABSTRACT FROM AUTHOR]

Large underground spaces are complex and huge, with problems such as fragmented data that cannot be shared, outdated management methods, and high operation and maintenance costs. The digitization of building information and the use of digital twin technology can effectively improve the efficiency of building operation and maintenance. Using information technology to build a digital-twin-based operation and maintenance management system, the huge and discrete data and equipment system information are effectively integrated and explored for application. First, we analyze the shortcomings of the traditional delivery and operation and maintenance methods and introduce the necessity of a new method of operation and maintenance management based on digital twin; then, we divide the operation and maintenance information of the construction project into four major categories of spatial information and eight major categories of equipment information and complete the coding work, and the categorized data serve as the data basis for operation and maintenance; second, we develop a digital twin operation and maintenance management platform based on the operation and maintenance data of BIM; finally, we carry out case validation for the three major constructions. Finally, case validation is carried out for three major buildings and a large underground space and the practical application shows that the operation and maintenance management system based on digital twin technology provides technical guarantee for decentralized information and system integration management and the information management quality and efficiency of the operation and maintenance process are effectively improved. Through the practice of summarizing the experience, which is worth learning, we aim to create a large underground space operation and maintenance management method to provide a reference. [ABSTRACT FROM AUTHOR]

Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently exceeded. Therefore, this paper investigates a data-driven combined vector loop method (VLM)–Skin Model Shapes (SMS) method to realize aerospace vehicle structural geometric accuracy analysis; assembly optimization targeting contour deviation is also achieved. Tests are carried out on a typical aerospace vehicle's underconstrained structural workpieces to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework's capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method's effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems. [ABSTRACT FROM AUTHOR]

The accurate prediction of aerothermal loads is the basis to guide hypersonic vehicle design. Under the background that classical aerothermal prediction methods are more and more difficult to meet the demand of efficient and accurate aerothermal prediction in engineering, data-driven aerothermal modeling prediction methods have gradually become a new paradigm of aerothermal prediction in recent years. Firstly, the relationship between the data-driven aerothermal modeling prediction method and the classical aerothermal prediction method was described. Then, from the modeling idea, the data-driven aerothermal modeling prediction methods were summarized into three categories: The dimensionality reduction modeling method of feature space, pointwise modeling method and physical information embedding modeling method were introduced and analyzed in detail. It is found that the data-driven aerothermal modeling prediction method is not only more accurate than the engineering algorithm, but also can effectively reduce the workload of test measurement and numerical calculation when combined with the sampling method, and the model given is more efficient and concise. Finally, the development trend of data-driven aerothermal modeling prediction methods was prospected. It is pointed out that the deep combination of data-driven technology and classical aerothermal prediction methods, aerothermal physical information embedding modeling methods and aerothermal prediction big models will be the key points of future research.

A COP 28, Conferência das Partes sobre Mudanças Climáticas, destaca-se como um evento crucial para discutir e abordar os desafios relacionados aos gases de efeito estufa (GEE) em um mundo cada vez mais impactado pelas mudanças climáticas. Os GEE, como o dióxido de carbono (CO2), metano (CH4) e óxido nitroso (N2O), são os principais impulsionadores do aquecimento global e representam a preocupação central na COP 28. A necessidade de reduzir as emissões desses gases é incontestável, e estratégias baseadas em dados (data driven) emergem como ferramentas fundamentais para entender e abordar esse desafio complexo. O uso de análises baseadas em dados permite uma compreensão mais precisa das fontes de emissões de GEE, identificando áreas críticas para ação e facilitando a implementação de medidas eficazes de mitigação. Este estudo faz um levantamento dos dados da COP 28, combinando discussões sobre GEE e abordagens data driven os quais desempenham um papel crucial na formulação de políticas e na tomada de decisões direcionadas para enfrentar as mudanças climáticas de forma eficaz e sustentável.

With the advent of large-scale data, the demand for information is increasing, which makes signal sampling technology and digital processing methods particularly important. The utilization of 1-bit compressive sensing in sparse recovery has garnered significant attention due to its cost-effectiveness in hardware implementation and storage. In this paper, we first leverage the minimax concave penalty equipped with the least squares to recover a high-dimensional true signal x ∈ R p with k-sparse from n-dimensional 1-bit measurements and discuss the regularization by combing the nonconvex sparsity-inducing penalties. Moreover, we give an analysis of the complexity of the method with minimax concave penalty in certain conditions and derive the general theory for the model equipped with the family of sparsity-inducing nonconvex functions. Then, our approach employs a data-driven Newton-type method with stagewise steps to solve the proposed method. Numerical experiments on the synthesized and real data verify the competitiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study explores the application of machine learning techniques to evaluate key aspects of user preferences and usability on e-commerce websites. As online shopping becomes increasingly prevalent, understanding user behavior and improving site usability are critical for enhancing customer satisfaction and driving sales. We employ various machine learning models to analyze user interaction data, including clickstreams, purchase history, and navigation patterns. The analysis focuses on identifying factors that influence user preferences, such as product recommendations, page load times, and user interface design. Additionally, usability metrics are assessed to determine their impact on the overall user experience. The findings highlight significant correlations between specific website features and user engagement levels, providing actionable insights for optimizing e-commerce platforms. By leveraging machine learning, this research offers a data-driven approach to enhancing user satisfaction and operational efficiency in the e-commerce industry. [ABSTRACT FROM AUTHOR]

Waveform inversion is concerned with estimating a heterogeneous medium, modeled by variable coefficients of wave equations, using sources that emit probing signals and receivers that record the generated waves. It is an old and intensively studied inverse problem with a wide range of applications, but the existing inversion methodologies are still far from satisfactory. The typical mathematical formulation is a nonlinear least squares data fit optimization and the difficulty stems from the nonconvexity of the objective function that displays numerous local minima at which local optimization approaches stagnate. This pathological behavior has at least three unavoidable causes: (1) The mapping from the unknown coefficients to the wave field is nonlinear and complicated. (2) The sources and receivers typically lie on a single side of the medium, so only backscattered waves are measured. (3) The probing signals are band limited and with high frequency content. There is a lot of activity in the computational science and engineering communities that seeks to mitigate the difficulty of estimating the medium by data fitting. In this paper we present a different point of view, based on reduced order models (ROMs) of two operators that control the wave propagation. The ROMs are called data driven because they are computed directly from the measurements, without any knowledge of the wave field inside the inaccessible medium. This computation is noniterative and uses standard numerical linear algebra methods. The resulting ROMs capture features of the physics of wave propagation in a complementary way and have surprisingly good approximation properties that facilitate waveform inversion. [ABSTRACT FROM AUTHOR]

Achieving a good strength–toughness match in nacre-inspired composites (NICs) is a difficult issue since it involves an optimum combination of many microstructural parameters. A multi-parameter design rule for the strengthening and toughening of NICs, which is based on extensive finite element (FE) data analysis and experimental verification, is given in this paper. Extensive FE calculations of NICs under uniaxial tension are firstly performed, with which the effects of the aspect ratio of hard phase (ARHP), the volume fraction of soft phase (VFSP) and the hard/soft-phase modulus ratio (HSMR) on the strength and toughness of composites are analyzed. Combinatorial conditions of these parameters for NICs with low strength and low toughness, high strength and low toughness and well-matched strength and toughness are determined, respectively, by comparisons of FE data samples. Furthermore, 3 D-printed NIC specimens consistent with FE models are prepared and tested by tensile experiments. The experimental results agree well with the FE results, both of which disclose the microscopic mechanism behind the strength and toughness of composites. It is found that a mixed failure mode including tensile fracture of hard phase and shear failure of soft phase can be realized by the combination of a large ARHP, a moderate VFSP and a large HSMR, which consequently leads to an excellent strength–toughness match in NICs. Compared with existing nacre-inspired composites with the same hard phase but more complex microstructures such as interlocking interfaces, deflected hard phases and graded soft phases, the present composite not only shows superior strength and toughness but is also easier to produce. Therefore, a highly-efficient and simple method can be provided for the design of strong and tough biomimetic composites. [ABSTRACT FROM AUTHOR]

This study employed a data-driven approach involving the creation and training of a deep neural network model to swiftly compute spectral data for nano-photonic devices. Initially, the transfer matrix method was utilized to compute transmission and reflection spectra for one million layered materials composed of SiO2/Si3N4. These spectra were then used as the training data for the deep neural network. Remarkably, despite using a training set that represented just one billionth of all possible samples within the design space, the resulting model displayed exceptional accuracy. More than 99.71% of the predictions demonstrated a standard error below 1%. This method represents a significant advancement over traditional design approaches, as it drastically reduces the complexity for designers. Moreover, the deep neural network model is less than 1 megabyte in size, making it easy to integrate into micro-optoelectronic devices. [ABSTRACT FROM AUTHOR]

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Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as nonconvex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. Our RPINN is threefold: By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Furthermore, it employs physics principles to construct the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, the robustness of RPINN against noisy input measurements makes it particularly valuable in environments with fluctuating information. Specifically, we test a uniform mixture model and a gas-powered system as NOPT scenarios. Overall, with RPINN, its ANN-based foundation offers significant flexibility and scalability. [ABSTRACT FROM AUTHOR]

This paper systematically sorts out the generation methods of simulation test scenarios for autonomous vehicle, summarizes the latest research progress in the fields of autonomous vehicle simulation test scenario definition, scenario deconstruction, scenario generation based on data driven, and scenario generation based on mechanism modeling, and summarizes the relevant evaluation and application of test scenarios. Finally, the paper proposes that future research should focus on integrating the characteristics of Chinese driving scenarios, deepening the research on edge scenario generation strategies, and accelerating the construction of the standard system of scenario construction. [ABSTRACT FROM AUTHOR]