Local Model Research Articles

Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

Evaluating the Efficacy of LLMs to Emulate Realistic Human Personalities

To enhance immersion and engagement in video games, the design of Affective Non-Player Characters (NPCs) is a key focus for researchers and practitioners. Affective Computing frameworks improve Non-player characters (NPC) by providing personalities, emotions, and social relations. Large Language Models (LLMs) bring the promise to dynamically enhance character design when coupled with these frameworks, but further research is needed to validate the models truly represent human qualities. In this research, a comprehensive analysis investigates the capabilities of LLMs to generate content that aligns with human personality, using the Big Five and human responses from the International Personality Item Pool (IPIP) questionnaire. Our goal is to benchmark the performance of various LLMs, including frontier models and local models, against an extensive dataset comprising over 50,000 human surveys of self-reported personality tests to determine whether LLMs can replicate human-like decision-making with personality-driven prompts. A range of personality profiles were used to cluster the test results from the human survey dataset. Our methodology involved prompting LLMs with self-evaluated test items for each personality profile, comparing their outputs to human baseline responses, and evaluating the accuracy and consistency. Our findings show that some local models had 0% alignment of any personality profiles when compared to the human dataset, while the frontier models, in some cases, had 100% alignment. The results indicate that NPCs can successfully emulate human-like personality traits using LLMs, as demonstrated by benchmarking the LLM's output against human data. This foundational work serves as a methodology for game developers and researchers to test and evaluate LLMs, ensuring they accurately represent the desired human personalities and can be expanded for further validation.

Measurements of N2 + ions in an inductively coupled plasma using saturated cavity ringdown spectroscopy

Abstract This paper presents a unique study of the bulk plasma characteristics in a low pressure inductively coupled nitrogen plasma. Saturated cavity ringdown spectroscopy (sat-CRDS) has been used to determine the absolute number densities and translational temperatures of N2 +(X, ν = 0). The effect of saturation is readily accounted for by using an effective saturation parameter, Seff , and determined by a simple method employing measurements at two different gain settings of the detection system. The appropriateness of this method is confirmed by comparison with fitting individual ringdown data using a time-dependent saturation parameter, S(t), within the local approximation model for sat-CRDS; the two methods are in excellent agreement in returning absolute number densities and translational temperatures. N2 +(X, ν = 0) number densities are determined across a matrix of pressure (10 − 100 mTorr) and rf power (200 − 400 W) conditions with maximum number densities of ca. 1.3 × 1010 cm−3 while translational temperatures range from 600 − 1500 K.

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Technical Note: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application.

The advancement of medical image deep learning necessitates tools that can accurately identify body regions from whole-body scans to serve as an essential pre-processing step for downstream tasks. Typically, these deep learning models rely on labeled data and supervised learning, which is labor-intensive. However, the emergence of self-supervised learning is revolutionizing the field by eliminating the need for labels. The purpose of this study was to compare neural network architectures of self-supervised models that produced a body part regression (BPR) slice score to aid in the development of anatomically localized segmentation models. VGG, ResNet, DenseNet, ConvNext, and EfficientNet BPR models were implemented in the MONAI/Pytorch framework. Landmark organs were correlated to slice scores and mean absolute error (MAE) was calculated from the predicted slice and the actual slice of various organ landmarks. Four localized DynUNet segmentation models (thorax, upper abdomen, lower abdomen, and pelvis) were developed using the BPR slice scores. Dice similarity coefficient (DSC) was compared between the localized and baseline segmentation models. The best performing BPR model was the EfficientNet architecture with an overall 3.18 MAE, compared to the VGG baseline model with a MAE of 6.29. The localized segmentation model significantly outperformed the baseline in 16 out of 20 organs with a DSC of 0.88. Enhanced neural networks like EfficientNet have a large performance increase in localizing anatomical structures in a CT compared in BPR task. Utilizing BPR slice score is shown to be effective in anatomically localized segmentation tasks with improved performance.

Gain-of-Function and Loss-of-Function Mutations in the RyR2-Expressing Gene Are Responsible for the CPVT1-Related Arrhythmogenic Activities in the Heart

Mutations in the ryanodine receptor (RyR2) gene have been linked to arrhythmia and possibly sudden cardiac death (SCD) during acute emotional stress, physical activities, or catecholamine perfusion. The most prevalent disorder is catecholaminergic polymorphic ventricular tachycardia (CPVT1). Four primary mechanisms have been proposed to describe CPVT1 with a RyR2 mutation: (a) gain-of-function, (b) destabilization of binding proteins, (c) store-overload-induced Ca2+ release (SOICR), and (d) loss of function. The goal of this study was to use computational models to understand these four mechanisms and how they might contribute to arrhythmia. To this end, we have developed a local control stochastic model of a ventricular cardiac myocyte and used it to investigate how the Ca2+ dynamics in the mutant RyR2 are responsible for the development of an arrhythmogenic episode under the condition of β-adrenergic (β-AR) stimulation or pauses afterward. Into the model, we have incorporated 20,000 distinct cardiac dyads consisting of stochastically gated L-type Ca2+ channels (LCCs) and ryanodine receptors (RyR2s) and the intervening dyadic cleft to analyze the alterations in Ca2+ dynamics. Recent experimental findings were incorporated into the model parameters to test these proposed mechanisms and their role in triggering arrhythmias. The model could not find any connection between SOICR and the destabilization of binding proteins as the arrhythmic mechanisms in the mutant myocyte. On the other hand, the model was able to observe loss-of-function and gain-of-function mutations resulting in EADs (Early Afterdepolarizations) and variations in action potential amplitudes and durations as the precursors to generate arrhythmia, respectively. These computational studies demonstrate how GOF and LOF mutations can lead to arrhythmia and cast doubt on the feasibility of SOICR as a mechanism of arrhythmia.

QCD phase diagram in the T–eB plane for varying pion mass

We study the effect of a varying pion mass on the quantum chromodynamics (QCD) phase diagram in the presence of an external magnetic field, aiming to understand it, for the first time, using Nambu–Jona-Lasinio like effective models. We compare results from both its local and nonlocal versions. In both cases, we find that the inverse magnetic catalysis (IMC) near the crossover is eliminated with increasing pion mass, while the decreasing trend of crossover temperature with increasing magnetic field persists for pion mass values at least up to 440 MeV. Thus, the models are capable of capturing qualitatively the results found by lattice QCD (LQCD) for heavy (unphysical) pions. The key feature in the models is the incorporation of the effect of a reduction in the coupling constant with increasing energy. Along with reproducing the IMC effect, it enables models to describe the effects of heavier current quark masses without introducing additional parameters. For the local NJL model, this agreement depends on how the parameters of the model are fit at the physical point. In this respect, the nonlocal version, which, due to its formulation, automatically exhibits the IMC effect around the crossover region, captures the physics more naturally. We further use the nonlocal framework to determine the pion mass beyond which the IMC effect around the transition region does not exist anymore. Published by the American Physical Society 2024

An empirical model of carbon-ion relative biological effectiveness based on the linear correlation between radiosensitivity to photons and carbon ions.

To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE). 

Approach. We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D5%), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5% and D37%, we constructed a model containing four free parameters that predicts cells' linear quadratic model (LQM) survival curve parameters for C-ions, αCand βC, from the reference LQM parameters for photons, αXand βX, for a given C-ion LET value. We fit our model's free parameters to the training dataset and assessed its accuracy via leave-one out cross-validation. We further compared our model to the local effect model (LEM) and the microdosimetric kinetic model (MKM) by comparing its predictions against published predictions made with those models for clinically relevant LET values in the range of 23-107 keV/μm. 

Main Results. Our model predicted C-ion RBE within ±7%-15% depending on cell line and dose which was comparable to LEM and MKM for the same conditions. 

Significance. Our model offers comparable accuracy to the LEM or MKM but requires fewer input parameters and is less computationally expensive and whose implementation is so simple we provide it coded into a spreadsheet. Thus, our model can serve as a pragmatic alternative to these mechanistic models in cases where cell-specific input parameters cannot be obtained, the models cannot be implemented, or for which their computational efficiency is paramount.

Character Education Based on Local Wisdom in Learning Science: A Systematic Literature

Character education based on local wisdom is an effort to prevent the decline of ethical and moral values among the younger generation in the educational process. This literature review aims to discuss the cultivation of local wisdom-based character education for students within science learning. The method used in this study is a Narrative Literature Review (NLR). Data collection was conducted through the Science Direct, Scopus and Google Scholar databases, focusing on articles published from 2018 to April 2024. The results of the literature review indicate that local wisdom-based character education in science learning can be effectively instilled in schools by integrating the potential of local wisdom with extracurricular activities, practicums or group learning, local tourism-based learning models, game-based learning, and interactions between students using local languages.

Framework of Scan to Building Information Modeling for Geometric Defect Localization in Railway Track Maintenance

Railway transportation plays a vital role in modern society, enabling the safe and efficient movement of people and goods over long distances. To ensure the longevity and safety of a railway infrastructure, the regular maintenance of tracks is crucial. Traditional track inspections, conducted manually to monitor geometric parameters and to identify defects, are time-consuming, labor-intensive, and prone to human error. Current Scan-to-BIM frameworks for railway maintenance also lack standardized methods for extracting geometric parameters that can be easily integrated into Building Information Modeling (BIM). Additionally, the Industry Foundation Classes (IFC) standard, used for BIM data exchange, does not support storing parameter values at specific chainage points along the track, limiting defect localization. A framework is proposed to address these challenges by standardizing the extraction of geometric parameters from point cloud data and ensuring seamless integration with BIM. The framework calculates parameters at station chainage points and generates additional chainage points along the track, associating the data with the corresponding chainage. A case study demonstrates the framework’s ability to enhance defect localization, using the EN 13848-5 European Standard to identify defects at specific chainages. Ultimately, this approach contributes to the more effective lifecycle management of railway tracks.

Localized corrosion behavior on a heterogeneous surface involving multicomponent reactive transport and morphology evolution

AbstractLocalized corrosion sometimes brings about unanticipated and catastrophic damages in petrochemical pipelines and pressure vessels. For localized corrosion modeling, multicomponent reactive transport and corrosion morphology evolution are two arduous problems that involve multi–physical‐(electro) chemical processes along with multi–temporal and spatial scales. In this study, the lattice Boltzmann method (LBM) is employed to shed some light on the evolution of localized corrosion on a heterogeneous surface focusing on the effects of electrolyte ohmic resistance and diffusion. The corrosion pit is always initiated at the junction of anode and cathode sites; however, quite different morphologies are prompted under ohmic control or ohmic‐diffusion control. Under ohmic control, the pit propagates along the interface of the anode and cathode. Under ohmic‐diffusion control, the pathway of reactive species is broadened, and the corrosion morphology is developed into a more regular shape, like a flattened semicircle in 2‐D modeling. This work presents the huge potential of LBM in long‐term corrosion modeling.

Localized Internationalization Framework in Higher Education for Undergraduate Institutions in the Context of Emerging Engineering Disciplines

This study explores the integration of global perspectives within engineering literacy education through localized internationalization efforts at Guilin University of Electronic Science and Technology (GUET). Focusing on the experiences of faculty members and administrative staff, the research investigates how GUET applies its localized internationalization model to emerging engineering disciplines. Utilizing a qualitative research design, data were collected through semi-structured interviews and focus group discussions with participants directly involved in curriculum development and international collaboration projects. The Concept-Construct-Theme (CCT) method was employed for data analysis, allowing for the identification of key themes related to the implementation, challenges, and effectiveness of the internationalization initiatives at GUET. The findings highlight the critical role of tailored educational strategies in enhancing global competencies while maintaining regional relevance. The study also underscores the importance of addressing resource limitations, institutional support, and faculty development to strengthen internationalization efforts. The insights gained from this research offer valuable recommendations for improving practices and policies in higher education institutions aiming to balance global engagement with local context.

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation

Distributed Graph Neural Network (GNN) training facilitates learning on massive graphs that surpass the storage and computational capabilities of a single machine. Traditional distributed frameworks strive for performance parity with centralized training by maximally recovering cross-instance node dependencies, relying either on inter-instance communication or periodic fallback to centralized training. However, these processes create overhead and constrain the scalability of the framework. In this work, we propose a streamlined framework for distributed GNN training that eliminates these costly operations, yielding improved scalability, convergence speed, and performance over state-of-the-art approaches. Our framework (1) comprises independent trainers that asynchronously learn local models from locally-available parts of the training graph, and (2) synchronizes these local models only through periodic (time-based) model aggregation. Contrary to prevailing belief, our theoretical analysis shows that it is not essential to maximize the recovery of cross-instance node dependencies to achieve performance parity with centralized training. Instead, our framework leverages randomized assignment of nodes or super-nodes (i.e., collections of original nodes) to partition the training graph in order to enhance data uniformity and minimize discrepancies in gradient and loss function across instances. Experiments on social and e-commerce networks with up to 1.3 billion edges show that our proposed framework achieves state-of-the-art performance and 2.31x speedup compared to the fastest baseline, despite using less training data.

Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes

In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple and parameter-free attention module (SimAM), and soft non-maximum suppression (Soft NMS) are introduced. In order to improve the accuracy of target recognition and fine localization based on a depth camera (eye-in-hand), the SENetV2 backbone network structure, dynamic head module, deformable attention mechanism, and chain-of-thought prompted adaptive enhancer network are introduced. After that, on the basis of constructing a dual camera platform for target hybrid recognition and localization, the hand–eye calibration, collection and production of image datasets required for model training are completed. Finally, for the docking of the oil filling port, the hybrid recognition and localization experimental tests are completed in sequence. The test results show that in target recognition and coarse localization based on the industrial camera, the recognition accuracy of the designed model reaches 99%, and the average localization errors in the horizontal and vertical directions are 2.22 mm and 3.66 mm, respectively. In target recognition and fine localization based on the depth camera, the recognition accuracy of the designed model reaches 98%, and the average errors in depth, horizontal, and vertical directions are 0.12 mm, 0.28 mm, and 0.16 mm, respectively. These not only verify the effectiveness of the target hybrid recognition and localization methods based on dual cameras, but also demonstrate that they meet the high-precision recognition and localization requirements in complex scenes.

Emergy-based evaluation of production efficiency and sustainability of diversified multi-cropping systems in the Yangtze River Basin

Excessive agricultural investment brought about by increased multiple-cropping index may compromise environmental sustainability. There are few studies on the sustainability of diversified multi-cropping systems in the Yangtze River Basin (YRB). Therefore, this study selected five representative locations in the YRB. According to the local climate characteristics and food demand, diversified multi-cropping systems were designed, and the main local winter crops were selected as the previous crops of the corn–soybean strip compound cropping system, with the local traditional double-cropping model as the control (CK). The emergy evaluation method was introduced to quantitatively compare the efficiency and sustainability of diversified multi-cropping systems in the YRB. The results showed that by incorporating soybean by intercropping with corn, compared with the CK, the total energy input, annual energy output, and annual economic output increased by 15.80%, 9.78%, and 33.12% on average, respectively. The unit emergy value (UEV) and unit non-renewable value (UNV) increased by 6.03% and 5.98%, respectively; the emergy yield ratio (EYR) and environmental loading ratio (ELR) decreased by 0.91% and 0.44%, respectively; the emergy sustainability index (ESI) was the same. In the third mature crop selection, compared with that of corn, the ELR of soybean decreased by 14.32%, and the ESI increased by 18.55%. In addition, the choice of winter crops plays a vital role in the system’s efficiency and sustainability. Compared with those of other winter crops, the annual economic outputs of potato (upper reaches of the YRB), potato or forage rape (middle reaches of the YRB), and wheat (lower reaches of the YRB) increased by 51.02%, 32.27%, and 0.94%, respectively; their ESI increased by 71.21%, 47.72%, and 12.07%, respectively. Potato–corn/soybean or potato/corn/soybean (upper reaches of the YRB), forage rape–corn/soybean or potato/corn/soybean (middle reaches of the YRB), and wheat–corn/soybean (lower reaches of the YRB) were chosen to facilitate the coexistence of high economic benefits and environmental sustainability. Additionally, promoting mechanization and reducing labor input were essential to improve the efficiency and sustainability of multi-cropping systems. This study would provide a scientific basis and theoretical support for the development of efficient and sustainable multiple-cropping systems in the dryland of the YRB.

A machine learning assisted preliminary design methodology for bolted composite joints in large structures

Abstract Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

A temporal convolution network-based just-in-time learning method for industrial quality variable prediction

Real-time acquisition of quality variables is paramount for enhancing control and optimization of industrial processes. Process modeling methods, such as soft sensors, offer a means to predict difficult-to-obtain quality variables using easily measurable process parameters. However, the dynamic nature of industrial processes poses significant challenges to modeling. For instance, conventional models are typically trained offline using historical data, rendering them incapable of adapting to real-time changes in data distribution or environmental conditions. To tackle this challenge, we introduce a novel approach termed the Residual Temporal Attention Temporal Convolution Network (RTA-TCN) and propose a just-in-time learning method based on RTA-TCN for industrial process modeling. The RTA-TCN model incorporates temporal attention into TCN, enabling the integration of previous time-step process variables into the current ones, as well as the fusion of internally relevant features among inputs. Moreover, to prevent the partial loss of original information during feature integration, residual connections are introduced into the temporal attention mechanism. These connections facilitate the retention of original feature information to a maximal extent while integrating relevant features. Consequently, the proposed RTA-TCN demonstrates significant advantages in handling the non-linearity and long-term dynamic dependencies inherent in industrial variables. Additionally, the proposed just-in-time learning method leverages RTA-TCN as a local model and updates it in real-time using online industrial data. This just-in-time learning method enables effective adaptation to varying data distributions and environmental conditions. We validate the performance of our method using two industrial datasets (Debutanizer Column and Sulfur Recovery Unit).

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

Pricing Variable Annuity Contract with GMAB Guarantee Under a Regime Switching Local Volatility Model

Pricing Variable Annuity Contract with GMAB Guarantee Under a Regime Switching Local Volatility Model

XAIP: An eXplainable AI‐Based Pipeline for Identifying Key Factors of Surface Defects in Strip Steel

The surface defect has a direct impact on the performance and quality of the final product, so it is crucial to identify the key factors causing the defect. To achieve accurate key factor identification, an eXplainable AI‐based Pipeline (XAIP) is proposed herein. The proposed XAIP combines data mode clustering and local model construction to meet practical application requirements. Meanwhile, after analyzing the characteristics of practical data, a strategy for defect rate calculation is designed to provide more fine‐grained defect‐related information to supervise model training. The final identification results are presented by a two‐stage explainable method, which is designed to reveal the information of data modes and key variables and can give engineers more specific defect‐related information. The experiment results based on two practical manufacturing datasets show the effectiveness of the proposed method.