Unified Framework Research Articles

Oscillatory responses of a spatially random seabed in the vicinity of structures: Validation and application of the integrated CFD-SFEM

A comprehensive examination of the wave-induced oscillatory response of seabeds around structures is of great significance for ensuring the safe operation of marine engineering projects and enhancing the efficiency of marine resource development. Soil properties in nature exhibit spatial variability due to various geological processes, which should be considered in seabed stability analysis. An integrated CFD-SFEM is proposed for spatially heterogeneous seabeds, incorporating multi-physical solvers for nonlinear wave motion and poroelastic seabed response within a unified framework through a one-way coupling procedure. The wave sub-model for nonlinear fluid flow is based on RANS equations, with an internal wave-maker and absorbing layers realized by employing a momentum source function and damping source terms. The spatially heterogeneous seabed sub-model is based on Biot’s poroelastic theory and the random field method. We have implemented the integrated model and automated the iterative algorithm for MCS using MATLAB codes, assisted by the LiveLink platform. The proposed method has been validated from various perspectives and applied to a study of soil response around a partially buried pipeline to demonstrate its practical utility. This study offers a novel framework and perspective for the probabilistic evaluation of oscillatory responses in spatially varied seabeds surrounding structures.

A unified framework based on semantic segmentation for extraction of weld seam profiles with typical joints

Weld seam profile (WSP) extraction is the crucial step in laser vision-based robotic arc welding. Faced variable joints and profiles and different interference background, traditional machine learning methods generally produce unsatisfactory extraction results in accuracy and the generalization and anti-interference abilities. This paper presents a unified framework based on an improved U-Net for WSP extraction with various typical joints, focusing on upgrading the performances above. In this proposed network, the channel attention feature fusion block is firstly designed to learn the global contexts of WSPs from high and low-level feature maps. Secondly, the residual spatial pyramid pooling-fast block is proposed to distinguish the features of laser stripes and spatter, via independent chunked-area pooling. Thirdly, the multi-scale bottleneck attention module is developed to realize accurate prediction of boundary pixels of WSPs, through accumulating multi-scale features. Finally, the dice loss function is used for model training to address the problem of the unbalanced category, involving WSPs and the background. Experimental results show that the proposed network can effectively extract various WSPs with strong arc background, spatter, and noise, covering butt, fillet, and lap joints. Also, this improved U-Net is superior to four classical models FCN, SegNet, Deeplabv3+, U-Net, and to our previous traditional methods, in terms of accuracy, and the generalization and anti-interference abilities. This network provides a unified framework for extracting WSPs with various joints, regardless of positions, directions, and shapes of WSPs in images. It shows potential for automatic and intelligent arc welding.

Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation.

Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework, Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we first design a meta-architecture that decouples part features and things/stuff features, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Second, we propose a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives. It also decouples the errors for part segmentation and panoptic segmentation. Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further. We design a new part-whole interaction method using masked cross attention. Finally, extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results. Our models can serve as a strong baseline and aid future research in PPS.

Health and Well-Being: Distinct and Intertwined Concepts.

The concepts of health and well-being are deeply intertwined and interdependent. Situating both health and well-being within a unified framework is important to ensure their inclusion in the full spectrum of health research and to achieve the crucial task of building a body of basic, translational, and clinical studies to guide the implementation of whole-person health care. This commentary proposes such a framework where an individual's own assessment of their physical, emotional, and spiritual well-being complements objective measures of their physical and psychological function and where social, environmental, economic, educational, and vocational well-being are located within positive determinants of health.

Tensorized and Compressed Multi-View Subspace Clustering via Structured Constraint.

Multi-view learning has raised more and more attention in recent years. However, traditional approaches only focus on the difference while ignoring the consistency among views. It may make some views, with the situation of data abnormality or noise, ineffective in the progress of view learning. Besides, the current datasets have become high-dimensional and large-scale gradually. Therefore, this paper proposes a novel multi-view compressed subspace learning method via low-rank tensor constraint, which incorporates the clustering progress and multi-view learning into a unified framework. First, for each view, we take the partial samples to build a small-size dictionary, which can reduce the effect of both redundancy information and computation cost greatly. Then, to find the consistency and difference among views, we impose a low-rank tensor constraint on these representations and further design an auto-weighted mechanism to learn the optimal representation. Last, due to the non-square of the learned representation, the bipartite graph has been introduced, and under the structured constraint, the clustering results can be obtained directly from this graph without any post-processing. Extensive experiments on synthetic and real-world benchmark datasets demonstrate the efficacy and efficiency of our method, especially for the views with noise or outliers.

A novel semi-analytical approach based on scaled boundary finite element method for fluid-structure coupling analysis of liquid sloshing in 3D containers

In this paper, a novel semi-analytical approach is proposed for the three-dimensional fluid-structure coupling analysis of liquid sloshing in elastic containers subjected to harmonic and seismic loading in the horizontal direction, based on the scaled boundary finite element method (SBFEM). A modified SBFEM model, referred to as the scaling surface-based SBFEM, is developed to simulate the container wall, which is treated as a thin shell structure. Within the framework of the scaling surface-based SBFEM, the geometry of the shell structure is entirely determined by scaling one surface of the structure. This approach differs significantly from the standard SBFEM, where approximation is achieved through coordinate mapping based on a scaling center, thereby enhancing the modeling accuracy and efficiency. Hydrodynamic pressure is treated as an independent nodal variables in the governing equations of the fluid domain, which is modeled using the standard scaling centre-based SBFEM. The coupled fluid-structure system is assembled by applying equilibrium and compatibility boundary conditions to ensure the balance of interaction forces. A synchronous solution algorithm, combined with the implicit-implicit scheme of the Newmark method, is used to determine the dynamic responses of the coupled system. The main advantage of this novel approach is that it meshes and discretizes the boundaries instead of the entire structural and fluid domains, thereby reducing computational costs. Additionally, analytical solutions can be obtained along the radial direction of the interior domain, enhancing the accuracy and convergence of the results. Another advantage is the approach's ability to provide a unified modeling framework for structures of any shape. Furthermore, the asymmetry issue of the coefficient matrix can be effectively avoided by using a synchronous solution algorithm. Benchmark examinations confirm the superior computational accuracy and robustness of the proposed approach. A comprehensive parametric study is conducted, focusing on the effects of liquid filling levels, as well as geometric and material parameters, on the transient vibration and distribution behaviors of the fluid-structure coupling system.

Vision + X: A Survey on Multimodal Learning in the Light of Data.

We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified sensing system. To endow the machines with true intelligence, multimodal machine learning that incorporates data from various sources has become an increasingly popular research area with emerging technical advances in recent years. In this paper, we present a survey on multimodal machine learning from a novel perspective considering not only the purely technical aspects but also the intrinsic nature of different data modalities. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions, and then present the methodological advancements categorized by the combination of data modalities, such as Vision+Text, with slightly inclined emphasis on the visual data. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels, and provide an additional comparison in the light of their technical connections with the data nature, e.g., the semantic consistency between image objects and textual descriptions, and the rhythm correspondence between video dance moves and musical beats. We hope that the exploitation of the alignment as well as the existing gap between the intrinsic nature of data modality and the technical designs, will benefit future research studies to better address a specific challenge related to the concrete multimodal task, prompting a unified multimodal machine learning framework closer to a real human intelligence system.

Water Demand Forecasting in Multiple District Metered Areas Based on a Multi-Scale Correction Module Neural Network Architecture

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5%-20% per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

An efficient computational framework for data assimilation of fractional-order dynamical system with sparse observations

We introduce an efficient computational framework for data assimilation of fractional dynamical systems, extending traditional data assimilation techniques to fractional models. This framework offers effective computational methods that eliminate the need for complex adjoint model derivations and algorithm redesign. We establish the fundamental problem formulation, develop both the AtD and DtA approaches, and derive adjoint forms and numerical schemes for each method. Additionally, we create a unified fractional-order variational data assimilation framework applicable to both linear and nonlinear models, incorporating both explicit and implicit discrete methods. Specific discretization schemes and gradient formulas are derived for three distinct types of fractional-order models. The method's reliability and convergence are verified, and the effect of observation sparsity and quality is examined through numerical examples.

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation.

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes with limited support examples. Existing methods based on Region Proposal Networks (RPNs) face two issues: 1) overfitting suppresses novel class objects and 2) dual-branch models require complex spatial correlation strategies to prevent spatial information loss when generating class prototypes. We introduce a unified framework, Reference Twice (RefT), to exploit the relationship between support and query features for FSIS and related tasks. Our three main contributions are: 1) a novel transformer-based baseline that avoids overfitting, offering a new direction for FSIS; 2) demonstrating that support object queries encode key factors after base training, allowing query features to be enhanced twice at both feature and query levels using simple cross-attention, thus avoiding complex spatial correlation interaction; and 3) introducing a class-enhanced base knowledge distillation loss to address the issue of DETR-like models struggling with incremental settings due to the input projection layer, enabling easy extension to incremental FSIS. Extensive experimental evaluations on the COCO dataset under three FSIS settings demonstrate that our method performs favorably against existing approaches across different shots, e.g., +8.2/+9.4 performance gain over state-of-the-art methods with 10/30-shots.

Latency-Aware Unified Dynamic Networks for Efficient Image Recognition.

Dynamic networks have become a pivotal area of study in deep learning due to their ability to selectively activate computing units (such as layers or channels) or dynamically allocate computation to information-rich regions. This capability significantly curtails unnecessary computations, adapting to varying inputs. Despite these advantages, the practical efficiency of dynamic models often falls short of theoretical computation. This discrepancy arises from three primary challenges: 1) a lack of a unified framework across different dynamic inference paradigms due to the fragmented research landscape; 2) an excessive focus on algorithm design at the expense of scheduling strategies, which are essential for optimizing resource utilization on hardware; and 3) the complexity of latency evaluation, since most current libraries cater to static operators. To tackle these issues, we introduce Latency-Aware Unified Dynamic Networks (LAUDNet), a general framework that integrates three fundamental dynamic paradigms-spatially-adaptive computation, layer skipping, and channel skipping-into a single unified formulation. LAUDNet not only refines algorithmic design but also enhances scheduling optimization with the aid of a latency predictor. This predictor efficiently and accurately predicts the inference latency of dynamic operators on specific hardware setups. Our empirical assessments across multiple vision tasks-image classification, object detection, and instance segmentation-confirm that LAUDNet significantly bridges the gap between theoretical and practical efficiency. For instance, LAUDNet cuts down the practical latency of its static counterpart, ResNet-101, by over 50% on hardware platforms like V100, RTX 3090, and TX2 GPUs. Additionally, LAUDNet excels in the accuracy-efficiency trade-off compared to other methods.

A parametrized approach to generalized fractional integral inequalities: Hermite–Hadamard and Maclaurin variants

This paper introduces a novel parametrized integral identity that forms the basis for deriving a comprehensive class of generalized fractional integral inequalities. Building on recent advancements in fractional calculus, particularly in conformable fractional integrals, our approach offers a unified framework for various known inequalities. The novelty of this work lies in its ability to generate new and more general inequalities, including Hermite–Hadamard-, Maclaurin-, and corrected Maclaurin-type inequalities, by selecting specific parameter values. These results extend the scope of fractional integral inequalities and provide new insights into their structure. To demonstrate the practical applicability and accuracy of the theoretical findings, we present a detailed numerical example along with graphical representations.

Emerging Readers: Systematic Exploration of Effective English Reading Strategies for Preschoolers

This study reviews methods for improving preschoolers' phonological awareness and English reading abilities from 2013 to 2023 using the PRISMA protocol. Analysing 22 articles, it identifies 11 strategies for reading development and 5 for phonological awareness. An interactive reading model is introduced, providing a unified framework to support early childhood educators in enhancing literacy skills in young learners.

Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes.

Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated. Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity. MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust's predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction. MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

A computationally efficient method for induction motor bearing fault detection based on parallel convolutions and semi-supervised GAN

ABSTRACT Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively.

Psychometric Validation of the New Misophonia Screening List-Child and Youth and AMISOS-Y (Dutch Child- and Parent-Report Versions) for Assessing Misophonia in Youth.

Misophonia is a recently identified disorder of decreased sound tolerance that often originates in childhood. Currently, there is a lack of validated questionnaires for screening and assessing misophonia severity in children/adolescents. This paper presents an iterative validation process of two innovative (parallel child-/parent-reported) questionnaires: the Misophonia Screening List-Child and Youth for screening, and the Amsterdam Misophonia Scale-Youth (AMISOS-Y) for assessing misophonia severity in youth. After instrument refinement, we performed ROC curve, reliability, and principal component analyses, and assessed concurrent, convergent and divergent validity, on a combined sample (aged 8-18; clinical sample N = 94 youth, 95 parents; control group screening N = 197 youth, 56 parents; control group AMISOS-Y N = 192 youth, 55 parents). Both questionnaires were unidimensional and displayed excellent psychometric properties (α = 0.95-0.96). Future replication studies are needed in community and clinical samples to contribute to a unified diagnostic framework. Trial registered 09/2021: NL-OMON20775.

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea.

Green technology innovation or pollution substitution? an evolutionary game for high-Energy-consuming enterprises’ strategy from a consideration of energy-consuming trading rights

ABSTRACT The energy-consuming rights trading (ECRT) environmental regulatory policy starting from demand side reform has a significant influence on the enterprises’ production strategies. However, there is still a lack of research on dynamic mechanisms from a micro perspective. Referencing ECRT policy implementation, this paper incorporates government, the public, and high-energy-consuming enterprises into a unified framework, and constructs a tripartite evolutionary game model to explore the enterprises’ production strategy under the reward and punishment accountability mechanism. Then, a case study is addressed to examine the role of government. The results show that: (1) The steady-state conditions of the three parties depend on their own cost–benefit circumstances. Enterprises’ production strategy is driven by the coordination of the government and the public. (2) Achieving ideal equilibrium path evolution requires that the expected benefits of green technology innovation more strongly than the expected benefits of ‘pollution substitution’ practices. (3) A strict accountability system that includes penalties and rewards can enhance public supervision and accelerate the transformation of government active regulatory supervision strategies, stimulating the internal driving forces that motivate enterprises’ green technology innovation. The findings provide references for promoting the implementation of ECRT and helping to achieve the strategy of ecological civilisation construction.

Application research on nonlinear partitioned analysis of soil-structure interaction method of seismic response for seawater-seabed-structure.

The analysis of seismic response in marine engineering structures is pivotal for guaranteeing their seismic safety. Such analyses are intricate due to the complexity of fluid-structure and soil-structure interactions. This paper introduces a unified computational framework for wave motion within a water-saturated seabed-bedrock system, employing the Davidenkov model and a modified Massing rule to characterize the nonlinear properties of the saturated seabed. A comparative analysis is conducted between the nonlinear responses of marine sites subjected to linear and nonlinear free-field inputs, with a specific focus on the seismic response of bridges under seawater-seabed-structure interaction. The study demonstrates that the modulus of a nonlinear saturated seabed diminishes, leading to a decrease in the wave impedance ratio between the saturated seabed and bedrock. Consequently, the reflection and transmission coefficients from the bedrock to the saturated seabed increase, amplifying the seismic response. For deepwater bridges, under harder site conditions, the abutment's bottom response is largely insensitive to increasing water depth, whereas the shear at the abutment's base diminishes with increasing water depth. The proposed method is validated through two case studies, with an examination of the impact of saturated sites on the analysis results.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.