Unified Model Research Articles

Quantifying the Role of Orographic Processes in Producing Extreme Precipitation: A Case Study of an Atmospheric River Associated With Storm Bronagh

AbstractThis study explores the mechanisms contributing to heavy precipitation associated with landfalling atmospheric rivers (ARs) and orography, focusing on a high‐profile case study of an extratropical cyclone, storm Bronagh, which caused flooding and travel disruption in the UK. While prior research has established the connection between ARs, orography, and precipitation, the specific mechanisms leading to intense rainfall remain unclear. A novel methodology is introduced that quantifies the relative contribution to the total precipitation from cyclone‐induced processes and orographic processes using modifications to the Met Office Unified Model orography and microphysics parameterization scheme. Results show that the majority of storm Bronagh 's rainfall over land is attributed to cyclone‐related processes (frontal ascent and embedded convection). However, interaction of the AR associated with storm Bronagh and orography focuses more of the rain over the hills, enhancing precipitation in this region by a further . Further analysis reveals that the seeder‐feeder mechanism plays a predominant role in orographically enhanced rainfall. Accretion and riming processes occur as rain from the cyclone induced (seeder) cloud falls through the orographically induced (feeder) cloud enhancing precipitation rates. The feeder cloud formation is related to forced ascent of air within the AR over the orography. In conclusion, this study sheds light on the intricate interplay between ARs, orography, and cyclone precipitation during the landfall of extratropical cyclones. By separating the mechanisms behind heavy rainfall in the context of storm Bronagh, we quantify for the first time the effect of the seeder‐feeder effect at different heights over orography.

CelloType: A Unified Model for Segmentation and Classification of Tissue Images.

Cell segmentation and classification are critical tasks in spatial omics data analysis. We introduce CelloType, an end-to-end model designed for cell segmentation and classification of biomedical microscopy images. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multi-task learning approach that connects the segmentation and classification tasks and simultaneously boost the performance of both tasks. CelloType leverages Transformer-based deep learning techniques for enhanced accuracy of object detection, segmentation, and classification. It outperforms existing segmentation methods using ground-truths from public databases. In terms of classification, CelloType outperforms a baseline model comprised of state-of-the-art methods for individual tasks. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multi-scale segmentation and classification of both cellular and non-cellular elements in a tissue. The enhanced accuracy and multi-task-learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

SolBeePopecotox: A Population Model for Pesticide Risk Assessments of Solitary Bees.

In agricultural landscapes, solitary bees occur in a large diversity of species and are important for crop and wildflower pollination. They are distinguished from honey bees and bumble bees by their solitary lifestyle as well as different nesting strategies, phenologies, and floral preferences. Their ecological traits and presence in agricultural landscapes imply potential exposure to pesticides and suggest a need to conduct ecological risk assessments for solitary bees. However, assessing risks to the large diversity of managed and wild bees across landscapes and regions poses a formidable challenge. Population models provide tools to estimate potential population-level effects of pesticide exposures, can support field study design and interpretation, and can be applied to expand study data to untested conditions. We present a population model for solitary bees, SolBeePopecotox, developed for use in the context of ecological risk assessments. The trait-based model extends a previous version with the explicit representation of exposures to pesticides from relevant routes. Effects are implemented in the model using a simplified toxicokinetic-toxicodynamic model, BeeGUTS (GUTS = generalized unified threshold model for survival), adapted specifically for bees. We evaluated the model with data from semifield studies conducted with the red mason bee, Osmia bicornis, in which bees were foraging in tunnels over control and insecticide-treated oilseed rape fields. We extended the simulations to capture hypothetical semifield studies with two soil-nesting species, Nomia melanderi and Eucera pruinosa, which are difficult to test in empirical studies. The model provides a versatile tool for higher-tier risk assessments, for instance, to estimate effects of potential exposures, expanding available study data to untested species, environmental conditions, or exposure scenarios. Environ Toxicol Chem 2024;00:1-17. © 2024 SETAC.

Control Method for Ultra-Low Frequency Oscillation and Frequency Control Performance in Hydro–Wind Power Sending System

In a hydropower-dominated power grid, the primary frequency regulation (PFR) capability of hydropower units is typically compromised to suppress ultra-low frequency oscillations (ULFOs). However, as renewable wind power is further integrated, a practicable solution to damp ULFOs has emerged, which is to adjust the frequency control parameters of wind turbine (WT) units. Driven by the goals of overall damping enhancement and ULFO suppression, this paper first establishes an extended unified frequency model (EUFM) of a hydro–wind power sending system. Based on EUFM, the damping torque of the hydro–wind power sending system is derived, and the specific impact of WT control parameters on ULFOs and PFR characteristics is investigated. Then, a novel optimization objective function considering damping in the ultra-low frequency band and PFR is formulated and solved using an intelligence algorithm. By optimizing the parameters of the WT to suppress ULFOs, the PFR capability of hydropower units can be released. Finally, simulation results verify that the optimized WT parameters can simultaneously address the ULFO problem and guarantee PFR performance, thereby enhancing the frequency dynamic stability of the sending system.

Onset of Intragroup Conflict in a Generalized Model of Social Balance.

Dynamically changing interpersonal relationships lie at the core of the emergence of in-group structures, such as polarity or conflict. We explore these dynamics in a simple continuous dynamical model obtained by generalizing a previously proposed model based on Heider's balance theory from social psychology. Previous findings include a rigorous proof of the emergence of in-group harmony or bipolar conflict (global minima) and the identification of local minima called jammed states, along with their corresponding energy spectrum in terms of structural complexity. However, some unrealistic scenarios constrain our capacity to contextualize and directly apply these results to real-world social behaviors. To address these challenges, we first introduce a unified dynamical model and find that group size can be critical to the onset of in-group bipolar conflict. Using random matrix statistics, we then characterize the pathways leading to jammed states, which have previously been discussed only in stochastic models. Unexpectedly, we also show that perturbing our dynamical model can increase the chances of getting trapped in jammed states, giving rise to a novel notion called the noise-induced jammed state. We conclude this Letter by discussing possible real-world implications as well as potential contributions to the existing literature in relevant fields such as anthropology.

Autonomous Underwater Vehicle (AUV) Motion Design: Integrated Path Planning and Trajectory Tracking Based on Model Predictive Control (MPC)

This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles.

A unified nonlinear dynamic model for bolted flange joint disk-drum structures under different interface states: Theory and experiment

Bolted flange joint disk-drum structures (BFJDDSs) are key components in aero-engines, however, bolt looseness inevitably occurs due to extreme service conditions. This can cause the bolted joint interface to exhibit complex contact behaviors, which significantly complicate the vibration characteristics of BFJDDSs. Existing dynamic models for BFJDDSs have not considered the effect of bolt looseness (slip and separation), so their vibration behaviors are not well understood. In this study, a unified nonlinear dynamic model for BFJDDSs considering different interface states (stick, slip, and separation) is proposed, which is effective under both bolt looseness and non-looseness (stick) conditions. The bi-linear hysteretic model combined with the piecewise linear model is used to simultaneously consider different interface states. The Kirchhoff plate theory, the Sanders’ shell theory, and the Euler-Bernoulli beam theory are used to derive the energy functions of the disk, drum, and flange, respectively. Then, the Lagrange equations are employed to derive the governing equations of BFJDDSs. Modal and nonlinear forced vibration experiments are carried out on a BFJDDS to prove the correctness of the proposed mathematical model. Research results show that the proposed mathematical model is able to achieve good predictions of nonlinear dynamic properties of BFJDDSs under both bolt looseness and non-looseness conditions. This model is also able to well predict the jumping phenomenon observed in the experiment.

MSIDetector: Detecting Multi-Scenario industrial defects using an adapted visual foundation model and dual thresholding discriminator

Industrial defect detection is crucial for enhancing product quality. The diverse nature of industrial scenarios has posed challenges for developing a unified defect detection model that can address multiple industrial scenarios. To meet this challenge, we started from a data perspective, developing three self-designed data acquisition apparatuses to capture defect samples from multiple industrial scenarios. Leveraging grounding DINO (GDINO), an open-set detection approach, we propose a unified defect detection method, MSIDetector. By integrating an industrial feature adapter and a context-aware dual-thresholding defect discriminator, we successfully incorporate prior industrial knowledge into the model. The performance of the MSIDetector was evaluated across four industrial scenarios, including a public defect dataset, achieving a total state-of-the-art mAP@50 of 76.2, surpassing the performance of FasterRCNN, YOLO-7X, and the visual foundation model GDINO, with quantitative increases of 13.93, 6.55, and 2.1, respectively. This report represents the first successful attempt to apply foundation models to defect detection.

An exact constraint programming method for the multi-manned assembly line balancing problem with assignment restrictions

This paper proposes an exact Constraint Programming (CP) method with an extensive focus on real-world constraints for the Multi-manned Assembly Line Balancing Problem with Assignment Restrictions (MALBP-AR). We perform an in-depth literature review to gather examples from real assembly lines and organize the AR regarding tasks, stations, workers, and mounting positions. Our study classifies the AR related to transformed resources and provides a general and unified model. We explore the concept of variable workplaces to dynamically assign workers to mounting positions and aggregate no overlap restrictions to avoid interference between workers. The classic MALBP model is extended by gradually incrementing the number of restrictions. The model variant found in the literature is herein called Partial MALBP-AR. Compared to the previous state-of-the-art Tabu Search Algorithm (TSA) for this problem, the Partial MALBP-AR found twelve additional optimality proofs. Besides the relevant results regarding solution quality, the CP method also has a satisfactory CPU performance. We also propose an entirely new set of AR and test these practical conditions with the so-called Extended MALBP-AR. Such an extended model, which covers all the AR presented here, reached optimality within the computational time limit for 36 out of 38 instances. The worst-case gap for an open instance is 8.20%. The results show a trade-off between the number of deemed restrictions and the computational performance. However, considering the detailed set of AR, we can obtain more representative solutions regarding the final balancing implementation compared to theoretical cases. The method can be used to design experiments, turning certain constraints on and off and allowing managers to evaluate different resource allocation scenarios.

Altruistic and egoistic behaviors on enterprise social network platforms: Analysis using PLS-SEM and fsQCA

Enterprise social network platforms (ESNPs) are thriving in workplaces. Past studies have provided two competitive explanations − employees engage in egoistic behaviors to facilitate knowledge hiding and personal enjoyment or altruistic behaviors to facilitate knowledge sharing and offering assistance. However, these perspectives have generally not been synthesized into a unified model. We adopt an integrated perspective to reconcile the two paradoxical behaviors, using PLS-SEM and fsQCA from 174 employees. The results of PLS-SEM show that social and utilitarian motivation facilitate altruistic use, while social motivation reduces egoistic use, and utilitarian motivation and egoistic use have an inverted U-shaped relationship. Institutional deterrence positively moderates the relationship between egoistic use and work performance. Altruistic use mediates the relationship between three motivations and work performance. The fsQCA reveals causal configurations that portray the recipes of how different modes promote altruistic use, egoistic use, and work performance, which are complementary to the findings of PLS-SEM.

The G Protein-First Mechanism for Activation of the Class B Glucagon-like Peptide 1 Receptor Coupled to N-Terminal Domain-Mediated Conformational Progression.

Recently, there has been a great deal of excitement about new glucagon-like peptide 1 receptor (GLP-1R) agonists (e.g., semaglutide and tirzepatide) that have received FDA approval for type 2 diabetes and obesity. Although effective, these drugs come with side effects that limit their use. While research efforts continue to focus intensively on long-lasting, orally administered GLP-1R medications with fewer side effects, a major impediment to developing improved GLP-1R medications is that the mechanism by which an agonist activates GLP-1R to imitate signaling is not known. Here we present and validate the G protein (GP)-first mechanism for the GLP-1R supported by extensive atomistic simulations. We propose that GLP-1R is preactivated through the formation of a GLP-1R-GP precoupled complex at the cell membrane prior to ligand binding. Despite a transmembrane helix 6 (TM6)-bentout conformation characteristic of activated GLP-1R, this precoupled complex remains unactivated until an agonist binds to elicit signaling. Notably, this new hypothesis offers a unified and predictive model for the activities of a series of full and partial agonists, including the peptides ExP5, GLP-1(7-36), and GLP-1(9-36). Most surprisingly, our simulations reveal an N-terminus domain (NTD)-swing/agonist-insertion mechanism wherein the long extracellular NTD of GLP-1R tightly holds the C-terminal half of the peptide agonist and progressively shifts the N-terminal head of the peptide to facilitate insertion into the orthosteric pocket. Our findings provide novel mechanistic insights into the activation and function of class B GPCRs and should provide a realistic basis for structure-based ligand design.

Unified model for probing solar cell dynamics via cyclic voltammetry and impedance spectroscopy

Unified model for probing solar cell dynamics via cyclic voltammetry and impedance spectroscopy

Global Texts, Unified Action: Tackling Cyberbullying Across Multiple Languages

Cyberbullying, a widespread issue in digital communication, involves using online platforms to harass or demean individuals. Addressing it effectively requires understanding its manifestations across different linguistic contexts. This study presents a novel approach to cyberbullying detection, exploring its manifestations in seven languages through two distinct research paradigms: monolingual and multilingual scenarios. The monolingual approach focuses on developing and testing detection models within a single language framework. In contrast, the multilingual approach, which has shown superior performance, integrates data from multiple languages to train a unified model. This innovative strategy aims to harness broader linguistic diversity and enhance the model’s generalizability. We utilized three computational models: SONAR[Formula: see text]DNN, MUSE[Formula: see text]CNN-BiLSTM, and XLM-RoBERTa, with the SONAR[Formula: see text]DNN architecture demonstrating the most effective performance. This model combines SONAR’s sentence-level embeddings with the nuanced understanding of DNN, making it particularly adept at handling the complex variations of cyberbullying across languages. Our results indicate that multilingual models perform better, particularly in languages with significant representation, such as Arabic and English. Our evaluation shows that our models consistently outperform the best-recorded results on seven diverse datasets, achieving superior performance in six. This significant achievement underscores the robustness of our approach and marks an essential advancement in cyberbullying detection.

Experimental and theoretical study on the burning and pulsation behaviors of hydrogen-containing and hydrocarbon jet flames

Hydrogen-containing and hydrocarbon fuels have been widely used in industrial production. Jet fires involving these fuels emerge frequently after leakage or explosion accidents for gas pipelines and processing equipments. To reveal the influencing mechanisms of inclination angle on the pulsation behaviors of jet fires, the burning experiments of hydrogen, syngas, methane and propane jet fires with a nozzle diameter (D) of 15 mm, inclination angles (θ0) of 0°∼90°, fuel flow rates (Qf)of 2.5–40 L/min were conducted. The variations of the global and spatial pulsation frequencies with inclination angle and fuel flow rate are sensitive to the fuel types. Subharmonic pulsation was found at large fuel flow rates (Qf>Qcri). For hydrogen-containing jet flame, a transition from natural pulsation to subharmonic pulsation occurs at a critical inclination angle (θcri). In this case, the transition height where the local pulsation frequency begins to be dominated by subharmonic pulsation, increases with the inclination angle. The local Richardson number (Riy) at the transition height is close to 1. The interaction between the inner and outer vortices increases with the decrease of inclination angle, resulting in decreased natural frequency at the nozzle exit. With the theoretical velocity and flame width at y/D = 2, a unified dimensionless model of natural pulsation frequency was developed, Sty=0.411/Fry0.5, reasonably predicting the natural pulsation frequency of jet flames under different conditions.

Towards a unifying model for B-cell receptor triggering.

Antibodies are exceptionally versatile molecules with remarkable flexibility in their binding properties. Their natural targets range from small-molecule toxins, across viruses of different sizes, to bacteria and large multicellular parasites. The molecular determinants bound by antibodies include proteins, peptides, carbohydrates, nucleic acids, lipids and even synthetic molecules that have never existed in nature. Membrane-anchored antibodies also serve as receptors on the surface of the B cells that produce them. Despite recent structural insights, there is still no unifying molecular mechanism to explain how antibody targets (antigens) trigger the activation of these B-cell receptors(BCRs). After cognate antigen encounter, somatic hypermutation and class-switch recombination allow BCR affinity maturation and immunoglobulin class-specific responses, respectively. This raises the fundamental question of how one receptor activation mechanism can accommodate a plethora of variant receptors and ligands, and how it can ensure that individual B cells remain responsive to antigen after somatic hypermutation and class switching. There is still no definite answer. Here we give a brief historical account of the different models proposed to explain BCR triggering and discuss their merit in the context of the current knowledge of the structure of BCRs, theirdynamic membrane distribution, and recent biochemical and cell biological insights.

Unified viscoplastic constitutive model for creep–fatigue behavior of austenitic stainless steel 304 under axial–torsional loading

Unified viscoplastic constitutive model for creep–fatigue behavior of austenitic stainless steel 304 under axial–torsional loading

Dialogue agents 101: a beginner’s guide to critical ingredients for designing effective conversational systems

Abstract Sharing ideas through communication with peers is the primary mode of human interaction. Consequently, extensive research has been conducted in the area of conversational AI, leading to an increase in the availability and diversity of conversational tasks, datasets, and methods. However, with numerous tasks being explored simultaneously, the current landscape of conversational AI has become fragmented. Consequently, initiating a well-thought-out model for a dialogue agent can pose significant challenges for a practitioner. Toward highlighting the critical ingredients needed for a practitioner to design a dialogue agent from scratch, the current study provides a comprehensive overview of the primary characteristics of a dialogue agent, the supporting tasks, their corresponding open-domain datasets, and the methods used to benchmark these datasets. We observe that different methods have been used to tackle distinct dialogue tasks. However, building separate models for each task is costly and does not leverage the correlation among the several tasks of a dialogue agent. As a result, recent trends suggest a shift toward building unified foundation models. To this end, we propose Unit, a Unified dialogue dataset constructed from conversations of varying datasets for different dialogue tasks capturing the nuances for each of them. We then train a Unified dialogue foundation model, GPT-2 $^{\textrm{U}}$ and present a concise comparative performance of GPT-2 $^{\textrm{U}}$ against existing large language models. We also examine the evaluation strategies used to measure the performance of dialogue agents and highlight the scope for future research in the area of conversational AI with a thorough discussion of popular models such as ChatGPT.

A Unified Model of a Virtual Synchronous Generator for Transient Stability Analysis

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of control models in the existing VSG control, including active and reactive power models, which lead to their different transient stabilities. However, the evolution characteristics, correlation between different models of VSG, and the internal mechanism affecting transient stability have not been fully studied. To this effect, this paper analyzes their evolution characteristics based on the existing mainstream VSG control models and establishes a unified VSG model and its equivalent correspondence with other models. Then, the phase plane method is used to comprehensively analyze and compare the transient stability of the VSG unified model with other models. It is pointed out that the key factors affecting the transient stability of different models are three links of primary frequency regulation, reactive power regulation and reactive power tracking. Finally, the correctness of the established VSG unified model and the conclusion of transient stability analysis is verified by experiments.

A versatile model with a design framework for axially-loaded FRP-confined concrete with/without a stress reduction-recovery behavior

Given the magnitude of investigations conducted on axially-loaded FRP-confined concrete columns in the past two decades, the focus of these studies was mainly given on FRP-confined concrete with a full strain hardening behavior, categorized as Type A. Nonetheless, for a reliable design of FRP confinement arrangements imposed to concrete, it is essential to properly comprehend and accurately simulate the mechanical behavior of FRP-confined concrete considering the possibility of a stress reduction-recovery response (Type B). Accordingly, this study proposes a new unified design-oriented model to predict the stress-strain behavior of axially-loaded FRP-confined normal/high-strength concrete cylinders with either Type A or Type B. The model consists of a two-segment stress-strain function for Type A whereas it, with a smooth transition, is transformed into a three-segment stress-strain function for Type B. A new parabolic stress-strain relation was developed for the first portion of axial behavior prior to the transition zone. Based on a new analytical methodology, new formulations with a design framework were proposed to predict the slope of softening/hardening linear functions, calibrated based on a large test database of Type A and Type B specimens. Furthermore, based on a reliable numerical database, simplified formulations are proposed to calculate the stress and strain at the transition zone of the stress-strain relations. The comparative assessment of the developed design-oriented model with existing confinement models reveals the developed one has not only superior predictive performance but also a simpler calculation process, which makes it suitable for design purposes by balancing the reliability and the implementation simplicity of the model.

An improved dual shear unified strength model (IDSUSM) considering strain softening effect

This study proposes an improved dual shear unified strength model by introducing the plastic internal variable which reflects the collective effects of strain softening, intermediate principal stress and unequal strength under tension and compression. The improved model is then simplified into simple forms for typical stress states, including uniaxial tension and compression, plane stress pure shear and tri-axial stress states. The smooth method and conjugate gradient method are utilized to facilitate its numerical implementation, avoiding numerical singularity and non-convergence in the solution process. The physical meanings of the parameters are further clarified and their values for self-compacting concrete are determined from the results of triaxial compression tests through a combination of direct determination, equation solution and back propagation (BP) neural network optimization. Validated against the test results, the improved model gives a more accurate prediction than the traditional dual shear unified strength model and Mohr-Coulomb model, in terms of both the overall trend and representative values. Validation results show that the improved model is applicable to materials for which the compressive strength is greater than the tensile strength and the tensile strength is greater than the shear strength.