Response Model Research Articles

Constitutive modelling of deformation rate dependent response of steel timber shear connections

This paper presents experimental and numerical investigations into the deformation rate-dependent constitutive response of steel-timber shear connections with screws. After describing the test specimens and experimental arrangement, a detailed account of the complete deformation response and main mechanical parameters of the tested shear connections under three applied displacement rate levels are given. Specimens with small screw diameters had a relatively brittle response, failing shortly after yielding, whilst those with higher diameters showed ductile failure modes, with plastic hinges forming in the screws and concurrent extensive timber crushing. It was observed that the stiffness increases with the deformation rate due to the viscoelastic response of wood materials, whilst the peak load is largely constant. Nonlinear finite element simulations were carried out to validate the main numerical parameters for steel, timber, and interaction characteristics. After gaining confidence in the ability of the numerical models to predict closely the stiffness and peak load, numerical investigations were carried out to examine the influence of key material and geometric parameters on the stiffness, load resistance and deformation response. The studies showed that higher timber strength increases the initial stiffness and the peak load, while higher screw grades improved both stiffness and strength. Based on the results and observations, code-modified expressions for evaluating the stiffness and load resistance, as a function of the deformation rate, are proposed within the ranges considered, and validated against a collated database. Comparative assessments with existing literature indicate that the proposed equations provide improved estimates. Suggested closed-form relationships enable the characterisation of the full constitutive response of steel-timber shear connections, that can be adopted for discrete nonlinear modelling of connectors.

ID: 342272 Computational Modeling of the Neural Response to Inferferential Spinal Cord Stimulation

ID: 342272 Computational Modeling of the Neural Response to Inferferential Spinal Cord Stimulation

Twinning in porous elastomers

It has been known for some time that constitutive models for the large-deformation response of porous elastomers can develop ‘macroscopic’ instabilities as a consequence of loss of strong ellipticity. Indeed, constitutive models obtained by homogenization methods for porous elastomers with periodic and random microstructures can lose strong ellipticity under appropriate loading conditions. For periodic microstructures, it has been shown theoretically, and verified experimentally and numerically, that ‘microscopic’ instabilities consisting in solutions that are periodic on ensembles of unit cells tend to occur before the long-wavelength ‘macroscopic’ instabilities that are captured by the loss of strong ellipticity of the homogenized response. But what about the response of porous elastomers with random microstructures? In this case, microscopic instabilities can be excluded, and the question then arises as to what happens after the onset of a macroscopic instability. Building on earlier work for reinforced elastomers, it is shown here that porous elastomers can undergo twinning after the onset of a macroscopic instability. For this purpose, use is made of a generalized Maxwell-type construction arising from the theory of relaxation and applied to linear comparison variational homogenization estimates for a certain class of two-dimensional porous elastomers consisting of aligned cylindrical pores in a rubber matrix subjected to plane strain loadings. It is shown that such porous elastomers recover Legendre-Hadamard stability by twinning after the onset of a macroscopic instability. Interestingly, the porous elastomers behave like elastic fluids in the twinned region, losing their ability to support shear stresses.

Predictive modeling of sensory responses in deep brain stimulation.

Although stimulation-induced sensations are typically considered undesirable side effects in clinical DBS therapy, there are emerging scenarios, such as computer-brain interface applications, where these sensations may be intentionally created. The selection of stimulation parameters, whether to avoid or induce sensations, is a challenging task due to the vast parameter space involved. This study aims to streamline DBS parameter selection by employing a machine learning model to predict the occurrence and somatic location of paresthesias in response to thalamic DBS. We used a dataset comprising 3,359 paresthetic sensations collected from 18 thalamic DBS leads from 10 individuals in two clinical centers. For each stimulation, we modeled the Volume of Tissue Activation (VTA). We then used the stimulation parameters and the VTA information to train a machine learning model to predict the occurrence of sensations and their corresponding somatic areas. Our results show fair to substantial agreement with ground truth in predicting the presence and somatic location of DBS-evoked paresthesias, with Kappa values ranging from 0.31 to 0.72. We observed comparable performance in predicting the presence of paresthesias for both seen and unseen cases (Kappa 0.72 vs. 0.60). However, Kappa agreement for predicting specific somatic locations was significantly lower for unseen cases (0.53 vs. 0.31). The results suggest that machine learning can potentially be used to optimize DBS parameter selection, leading to faster and more efficient postoperative management. Outcome predictions may be used to guide clinical DBS programming or tuning of DBS based computer-brain interfaces.

A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis

Although current deep learning models for bearing fault diagnosis have achieved excellent accuracy, the lack of constraint-guided learning of the physical mechanisms of real bearing failures and a physically scientific training paradigm leads to low interpretability and unreliability of intelligent fault diagnosis models. In this study, a sound-vibration physical-information fusion constraint-guided (PFCG) deep learning (DL) method is proposed, aiming at weighted fusion of sound and vibration multi-physical information into a deep learning model, to guide the DL model to learn more realistic physical laws of bearing failure. Firstly, a 15-degree-of-freedom nonlinear dynamics model of multi-stage degraded bearing failure mechanism with sound-vibration response is developed, which considers the evolutionary mechanism of bearing failure from healthy state to different stages, and utilizes a particle filtering algorithm for dynamic calibration of hidden parameters. Moreover, a lightweight DL fault diagnosis model is designed to realize the deep interaction between the physical model and the DL model through the weighted fusion of the cross-entropy loss function, physical consistency loss and uncertainty loss. Moreover, the superior diagnostic performance of the proposed sound and vibration PFCG-DL model is verified by comparing the performance fluctuations and parameter attributes of different DL benchmark models before and after being guided by physical information fusion constraints (PFCG). Eventually, the proposed PFCG-Transformer model achieves a diagnostic accuracy of 99.45% while keeping the number of parameters at only 0.62M, which significantly improves the accuracy and reduces the computational complexity by 81.5% compared to the CAME-Transformer model's 3.24 M number of parameters and 95.00% diagnostic accuracy. In addition, the test time of PFCG-Transformer is reduced to 1.02 s, which is 60.2% less than CAME-Transformer, demonstrating higher computational efficiency and real-time performance. Importantly, in terms of interpretability, the engineering interpretability and credibility of the models are further improved by visualizing the feature learning results of the vibration modal and multimodal fusion models and the sensitivity analyses of the sound-vibration response models with internal and external physical hyperparameters. Therefore, this study proposes a physical information-guided deep learning method with strong interpretability and superior performance, which provides an important reference for further research and application in the field of bearing fault diagnosis.

Comparing robust proton versus online adaptive photon radiotherapy for short-course treatment of rectal cancer

Background and purposeImage-guided proton beam therapy (IG-PBT) and cone-beam CT (CBCT)-based online adaptive photon radiotherapy (oART) have potentials to restrict radiation toxicity. They are both hypothesised to reduce therapy limiting bowel toxicity in the multimodality treatment of locally advanced rectal cancer (LARC). This study aimed to quantify the difference in relevant dose-volume metrics for these modalities. Material and MethodsSix-degrees-of-freedom IG-PBT and oART short-course radiotherapy (SCRT) were simulated for 18 LARC patients. Relative biological effectiveness (RBE) was 1.1 for IG-PBT. Delivered dose was evaluated using post-CBCTs. Target coverage was considered robust if average dose to 99 % of the clinical target volume was ≥ 95 % of the prescription. Organ at risk (OAR) doses were compared using dose-volume histograms and severe bowel toxicity estimated using dose–response modelling. ResultsTarget coverage was robust in all patients for oART and all but one patient for IG-PBT. For the main OARs, IG-PBT increased the volume exposed to ≥ 15 Gy (RBE), but reduced volumes exposed to lower doses. Both low- and high-dose exposure to bowel loops were significantly different between the modalities (median (interquartile range) IG-PBT-V8.9Gy(RBE) = 92 cm3 (51–156), oART-V8.9Gy(RBE) = 166 cm3 (107–234), p < 0.001; IG-PBT-V23Gy(RBE) = 62 cm3 (25–106), oART-V23Gy(RBE) = 38 cm3 (18–75), p < 0.001), translating into similar total grade ≥ 3 bowel toxicity risk. ConclusionIG-PBT and oART delivered comparable and satisfying target coverage in SCRT for LARC with similar estimated risk of severe bowel toxicity. Volumes of OAR exposed to 15 Gy (RBE) or more were reduced by oART, while IG-PBT reduced the volumes receiving doses below this level.

Two-stage low-carbon economic dispatch of an integrated energy system considering flexible decoupling of electricity and heat on sides of source and load

Under the goal of "double carbon", in order to further enhance the level of new energy consumption and solve the problem of restricting the flexibility of the system by "ordering power by heat" of combined heat and power (CHP) units, a low-carbon economic planning strategy with flexible decoupling of electricity and heat is proposed, by introducing a new type of electric-thermal coupling equipment on both sides of the source and load. Firstly, Consideration of the low-carbon and environmentally friendly characteristics of green ammonia production and ammonia-doped combustion technologies, a wind power(WT) – power to ammonia(P2A) - CHP units - thermal power (TH) units joint operation strategy is proposed on the source side. This strategy realizes the conversion of abandoned wind to green ammonia to ammonia coal hybrid generation, the decoupled operation of CHP units and promotes the consumption of wind power and the low-carbon operation of the system. Secondly, A dynamic incentive demand response model is developed to meet the demand of high proportion distributed PV in situ consumption on the load side. The dynamic incentive price guides the distributed power-to-heat load to change the response capacity, tracks the abandonment of wind and light, realizes the flexible conversion of power and heat load, and cooperates with the source side to promote the coupling operation of electric pyrolysis. On this basis, consider the flexible decoupling capability of electric-heat coupling equipment on both sides of the source and load to establish a two-phase low-carbon scheduling model for the day-before and day-after phases. In the day-ahead phase, the source-side electric-thermal-ammonia joint operation strategy is considered, and the electric and thermal energy supply plans are adjusted centrally; In the intra-day phase, the flexible adjustment range of power-to-heat devices and the heat load inertia on the load side are taken into account, and the electricity and heat planning strategies are adjusted in a distributed-centralised manner in conjunction with the source side. Finally, through the simulation of different cases, the results show that compared with the traditional electric heating system, the total cost of the system considering the scheduling strategy proposed in this paper decreases by ￥826,900, the carbon emission decreases by 1.2t, and basically realises the consumption of wind power and distributed photovoltaic power output. The proposed scheme reduces carbon emissions, promotes the consumption of wind power and distributed photovoltaic output, and is able to reach the goal of low-carbon economic dispatch.

Rolling planning model for high proportion renewable energy generation power system considering frequency security constraints

In this paper, a rolling planning model for high proportion renewable energy generation power systems is proposed, considering frequency security constraints, to address the frequency stability challenges posed by increased integration of wind and solar energy into the power grid under the “double carbon” goal. First, this study establishes a frequency response model for a thermal generation unit (TGU) and analyzes the impact of the high proportion of renewable energy on system frequency stability. Subsequently, a dynamic frequency security constraint is formulated, which can be used in generation planning to address the frequency issues. Second, a bi-level rolling planning model for high proportion renewable energy generation power systems considering dynamic frequency security constraints is established. The upper level focuses on investment decisions. The lower level incorporates frequency security constraints into the operational simulation to guarantee the frequency stability of the power system when making decisions on the size of newly built TGUs annually. Finally, case studies are conducted to demonstrate the validity and effectiveness of the proposed rolling planning model.

ToxProfiler: A novel human-based reporter assay for in vitro chemical safety assessment

In vitro chemical safety assessment often relies on simple and general cytotoxicity endpoint measurements and fails to adequately predict human toxicity. To improve the in vitro chemical safety assessment, it is important to understand the underlying mechanisms of toxicity. Here we introduce ToxProfiler, a novel human-based reporter assay that quantifies the chemical-induced stress responses at a single-cell level and reveals the toxicological mode-of-action (MoA) of novel drugs and chemicals. The assay accurately measures the activation of seven major cellular stress response pathways (oxidative stress, cell cycle stress, endoplasmic reticulum stress, ion stress, protein stress, autophagy and inflammation) that play a role in the adaptive responses prior to cellular toxicity. To assess the applicability of the assay in predicting the toxicity MoA of chemicals, we tested a set of 100 chemicals with well-known in vitro and in vivo toxicological profiles. Concentration response modeling and point-of-departure estimation for each reporter protein allowed for chemical potency ranking and revealed the primary toxicological MoA of chemicals. Furthermore, the assay could effectively group chemicals based on their shared toxicity signatures and link them to specific toxicological targets, e.g. mitochondrial toxicity and genotoxicity, and different human pathologies, including liver toxicity and cardiotoxicity. Overall, ToxProfiler is a quantitative in vitro reporter assay that can accurately provide insight into the toxicological MoA of compounds, thereby assisting in the future mechanism-based safety assessment of chemicals.

A novel efficient randomized response model designed for attributes of utmost sensitivity

The existence of potential incomplete truthful reporting emerges when dealing with topics of great sensitivity. This paper introduces a novel, efficient randomized response model to address the challenges of untruthful reporting. It is meant to improve the accuracy of estimating highly sensitive attributes. The suggested model represents a modified version of Aboalkhair's model (2024) that was found to be an efficient substitute for the models developed by Warner and Mangat & Singh. This study examines the circumstances under which the suggested model outperforms alternative models in terms of efficiency. Theoretical and numerical comparisons of efficiency with alternative models, assuming incomplete truthful reporting, revealed the superior performance of the suggested model. Furthermore, a measure of privacy protection is computed.

EU Economic Governance as a Supranational Determinant of Health Inequalities in the Eurozone

The COVID-19 pandemic raises the question of austerity's problematic social toll for health in the south of Europe. Has EU economic governance constrained health spending? If so, have these spending levels led to inequalities, which in turn shaped responses to the pandemic? EU economic governance is often dismissed as ineffective because of its poor track record of compliance. Yet austerity is blamed for negative health outcomes. This article shows that the EU fiscal rule is a determinant of health because it affects fiscal policies of European countries. First, the analysis of EU member states during 1995–2018 shows that austerity policies affect health spending and health inequalities. Euro-area countries under the EU Excessive Deficit Procedure significantly consolidated their health spending. The contractionary effect was concentrated in southern countries, contributing to rising health inequalities across the core and periphery. Finally, the analysis shows the pandemic implications of health inequalities, as periphery countries with a track record of high consolidation display more stringent (and costly) COVID-19 response models. This analysis contributes to understanding the supranational determinants of health in the EU, showing the pervasive spillover effects of the fiscal framework on national health policies.

Long-chain perfluoroalkylether carboxylic acids PFO5DoA and PFO4DA alter glucose, bile acid, and thyroid hormone homeostasis in fetal rats from 5-day maternal oral exposure

Chemical monitoring studies in North Carolina, USA and Shandong, China have reported detections of perfluoroalkylether carboxylic acids of increasing chain length with ether bonds between each fluorinated carbon. Despite detection there is limited hazard data available to inform risk assessment. Here, we exposed pregnant Sprague-Dawley rats to two of these compounds, perfluoro-3,5,7,9-butaoxadecanoic acid (PFO4DA) and perfluoro-3,5,7,9,11-pentaoxadodecanoic acid (PFO5DoA), from gestation days 18-22 across a series of doses (0.3 - 62.5 mg/kg/d) via oral gavage. PFO5DoA was acutely toxic to rat dams and fetuses at the top two doses (30 and 62.5 mg/kg), while PFO4DA did not cause acute toxicity at any doses tested. PFO5DoA significantly increased maternal liver weight (≥3 mg/kg; 28% increase at 10 mg/kg) while PFO4DA did not affect maternal liver weight up to 62.5 mg/kg. PFO4DA and PFO5DoA both significantly reduced serum total thyroxine in maternal (≥10 mg/kg for both) and fetal (≥1 mg/kg) rats. Both compounds significantly reduced fetal liver glycogen concentrations, increased fetal serum total bile acids, and altered expression levels of multiple genes associated with glucose metabolism in the fetal liver. Serum concentrations of PFO5DoA were higher than PFO4DA in both rat dams and fetuses at equivalent maternal oral doses indicating greater accumulation. Dose response modelling of several fetal endpoints as a function of serum molar concentration indicates PFO5DoA was ∼3-4-fold more potent than PFO4DA. PFO5DoA and PFO4DA produced maternal and fetal toxicity from short-term oral maternal exposure indicating need for additional toxicity data to evaluate potential human health risks.

A Hybrid Approach to Modeling Heart Rate Response for Personalized Fitness Recommendations Using Wearable Data

Heart rate (HR) is a key indicator of fitness and cardiovascular health, and accurate HR monitoring and prediction are essential for enhancing personalized fitness experiences. The rise of wearable technology has significantly improved the ability to track personal health, including HR metrics. Accurate modeling of HR response during workouts is crucial for providing effective fitness recommendations, which help users achieve their goals while maintaining safe workout intensities. Although several HR monitoring and prediction models have been developed for personalized fitness recommendations, many remain impractical for real-world applications, and the domain of personalization in fitness applications still lacks sufficient research and innovation. This paper presents a hybrid approach to modeling HR response to workout intensity for personalized fitness recommendations. The proposed approach integrates a physiological model using Dynamic Bayesian Networks (DBNs) to capture heart rate dynamics during workout sessions. DBNs, combined with Long Short-Term Memory (LSTM) networks, model the evolution of HR over time based on workout intensity and individual fitness characteristics. The DBN parameters are dynamically derived from flexible neural networks that account for each user’s personalized health state, enabling the prediction of a full HR profile for each workout, while incorporating factors such as workout history and environmental factors. An adaptive feature selection module further enhances the model’s performance by focusing on relevant data and ensuring responsiveness to new data. We validated the proposed approach on the FitRec dataset, and experimental results show that our model can accurately predict HR responses to workout intensity in future sessions, achieving an average mean absolute error of 5.2 BPM per workout—significantly improving upon existing models. In addition to HR prediction, the model provides real-time fitness personalized recommendations based on individual’s observed workout intensity to an exercise. These findings demonstrate the model’s effectiveness in delivering precise, user personalized heart response to exercise with potential applications in fitness apps for personalized training and health monitoring.

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Innovation and Practice of Diversified Business Model of New Energy Power Generation Enterprises

With the growing global demand for clean energy, new energy power generation enterprises are facing new opportunities and challenges. This paper explores the diversified business model of new energy power generation enterprises. Firstly, the theoretical basis of the business model of new energy power generation enterprises is analyzed, including the characteristics and income sources of single and multiple industrial chain models. The advantages and applications of different business models are analyzed through the study of cases at home and abroad, such as the European aggregated generation side resource model, the United States focused on controllable load demand response model, and the domestic cases of Shanghai and Shenzhen virtual power plant projects and distributed photovoltaic power generation. Then it puts forward the diversified business model innovation strategy of new energy power generation enterprises, covering the innovation of comprehensive energy services, the development of power side energy storage business, and the expansion of key businesses and profit models. Finally, the research conclusions are summarized and the future research directions are prospected. In short, the exploration of diversified business models of new energy power generation enterprises is of great significance to promote the development of clean energy and improve the competitiveness of enterprises.

IMPACT OF ARTIFICIAL INTELLIGENCE ON E-COMMERCE:"A STUDY OF CONSUMER BEHAVIOR"

Artificial Intelligence (AI) has transformed the e-commerce landscape, influencing consumer behavior significantly. This study aims to explore the various ways AI impacts e-commerce, utilizing numerical data to analyze consumer preferences, purchasing patterns, and overall shopping experiences. The findings reveal both advantages and disadvantages, providing a comprehensive overview of AI's role in shaping modern consumer behavior. This study examines the transformative effects of artificial intelligence on E-commerce and consumer preferences and purchasing patterns have evolved. this paper analyzes various numerical data, identifies key trends, and discusses the advantages and disadvantages of AI in E-commerce. In online shopping enterprises, AI technology has been widely used to provide accurate and fast personalized consumer services. This research demonstrates the use of AI technology in the e-commerce business, specifically online enterprises, to determine different effects. The study was conducted in Jordan and involved about 230 participants. The study evaluated different impacts of AI, such as e-payment and stimulating consumers' sentiments. The study used the Stimulus–Organism–Response model (SOR) empirical model, which states that the examination of human processes differs from that of the machine assessment. The model classified the AI technology experienced by the customers' when they visit online to do purchasing. Online purchasing behaviors can be influenced by insight, accuracy, and interaction experience. Also, the perceived value was used as a mediating variable from the prospects of perceived hedonic and utility value. The research integrated empirical research models such as SEM and SPSS to analyze the data on the effects of three-dimension. The results indicated that the AI technology accuracy, interactive experience, and insight significantly affected customers' perceived hedonic and utilitarian values. KEYWORDS: AI in consumer behavior, Artificial Intelligence etc

Coupled field modelling of thermomechanical response in materials using peridynamic heat transport model and non-ordinary state-based peridynamics

Predicting the mechanical response of materials subjected to transient heat processes is crucial in various engineering applications. This paper introduces a novel peridynamic (PD) coupled field model formulated within the framework of Non-Ordinary State-Based Peridynamics (NOSBPD). This model offers the capability to simulate the coupled thermo-mechanical behavior of materials by incorporating both thermal transport and mechanical deformation analyses. To verify the model’s accuracy, a benchmark problem involving a steel plate undergoing transient thermal expansion is investigated. This model presents a valuable tool for various engineering applications involving coupled thermo-mechanical processes.

Mean and sensitivity estimation using optional scrambled randomized response technique

In this paper, a new optional randomized response model has been proposed for estimating the population mean of a sensitive variable and sensitivity level in presence of scrambled response under simple random sampling. The properties of proposed model are described and verified its theoretical results with empirical analysis. Both efficiency and privacy measures are also explained and it is found that the proposed optional scrambled randomized response model outperform as compared to some existing additive and multiplicative models.

Predictive modeling of nonlinear system responses using the Residual Improvement Deep Learning Algorithm (RIDLA)

Predictive modeling of nonlinear system responses using the Residual Improvement Deep Learning Algorithm (RIDLA)

A class of infinite number of unbiased estimators using weighted squared distance for two-deck randomized response model

ABSTRACT We develop a collection of unbiased estimators for the proportion of a population bearing a sensitive characteristic using a randomized response technique with two decks of cards for any choice of weights. The efficiency of the estimator depends on the weights, and we demonstrate how to find an optimal choice. The coefficients of skewness and kurtosis are introduced. We support our findings with a simulation study that models a real survey dataset. We suggest that a careful choice of such weights can also lead to all estimates of proportion lying between [0, 1]. In addition, we illustrate the use of the estimators in a recent study that estimates the proportion of students, 18 years and over, who had returned to the campus and tested positive for COVID-19.