Input-output Relationship Research Articles

Using linear regression metamodels for evaluating interventions in an individual-based influenza epidemic model

Agent-based simulation modeling is frequently used to model and simulate the spread of transmissible diseases such as influenza, COVID-19, and HIV/AIDS in communities. Besides incorporating disease-specific parameters, these models include a set of parameters to observe the effect of different intervention combinations on the course of an epidemic, bringing the opportunity to use these models as virtual laboratories for decision-making. However, these models are primarily large-scale and complex, increasing the runtime of experimentation. As a solution, metamodeling approaches are frequently employed to represent input–output relationships of simulation models. Instead of running the time-consuming agent-based model, policymakers use the metamodel to obtain predicted outcomes in a comparatively short time. In addition to time-saving advantages, metamodels can provide insights into how disease-specific and intervention parameters affect the outcome of interest. In this regard, this study uses an influenza epidemic model, FluTE, as the experimental platform. Instead of using the original agent-based model, we fit linear regression metamodels to quantify the effect of interventions, such as vaccination, quarantine, and school closure, on the influenza attack rate. After validating the metamodel, we observe that the day on which interventions start, ascertainment delay, the daily number of vaccinations administered, isolation and quarantine compliance probabilities, and the number of school closure days stand as the significant intervention policies.

Influence of a topological artificial atom chain on the transmission properties of a cavity.

We explore the influence of the artificial atomic chain on the input-output relation of the cavity. Specifically, we extend the atom chain to the one-dimensional Su-Schrieffer-Heeger (SSH) chain to check the role of atomic topological non-trivial edge state on the transmission characteristics of the cavity. The superconducting circuits can realize the artificial atomic chain. Our results show that the atom chain is not equivalent to atom gas, and the transmission properties of the cavity containing the atom chain are entirely different from that of the cavity containing atom gas. When the atom chain is arranged in the form of topological non-trivial SSH model, the atom chain can be equivalent to the three-level atom, in which the edge state contributes to the second level and is resonant with the cavity, while the high-energy bulk state contributes to form the third level and is greatly detuned with the cavity. Therefore, the transmission spectrum shows no more than three peaks. This allows us to infer the topological phase of the atomic chain and the coupling strength between the atom and the cavity only from the profile of the transmission spectrum. Our work is helping to understand the role of topology in quantum optics.

Optimizing the removal of calcium and magnesium from synthetic reverse osmosis concentrate using functional polyketones: A combined experimental and machine learning approach

In this study, we synthesized functional polyketones (FPKs) for removing scaling ions (Ca2+ and Mg2+) from synthetic reverse osmosis concentrate (ROC) and optimized the adsorption processes using experimental and machine learning (ML) techniques, including gene expression programming (GEP) and multilayer perceptron (MLP) modeling. PK30 (a copolymer of ethylene and propylene with carbon monoxide) was reacted with four different amines, including 1,2-diaminopropane (DAP), 1-(2-aminoethyl) piperazine (AEP), butylamine (BA), and 1-(3-aminopropyl) imidazole (API), to produce FPKs. The effects of various factors, such as the amount of FPKs, synthetic ROC concentration, and pH, were investigated to optimize the removal of scaling ions from the synthetic ROC using novel GEP and MLP ML models. The experimental results revealed that Ca2+ and Mg2+ ions might be adsorbed primarily via the chelating mechanism. Increasing FPKs amounts and higher pH levels improved the adsorption process, with even low concentrations of FPKs demonstrating enhanced adsorption. The adsorption of the scaling ions followed the order API > AEP > BA > DAP, with removal percentages of 88.9, 87.3, 83.7, and 83.0 %, respectively. The MLP model outperformed the GEP model in optimizing Ca2+ and Mg2+ adsorption from the synthetic ROC and produced favorable results for all selected amines by determining the input-output relationship for the testing dataset with R2 = 0.806, 0.759, 0.760, and 0.878 for AEP, DAP, API, and BA, respectively. Overall, this study provides a viable solution for the pretreatment of ROC to remove scaling ions, which can improve the performance of zero liquid discharge systems by decreasing energy consumption.

Joint modelling of the order-dependent parts supply strategies sequencing, kitting and batch supply for assembly lines: insights from industrial practice

ABSTRACT Parts can be supplied from warehouses to assembly lines via several production-order-independent and -dependent parts supply strategies. Order-dependent parts supply strategies sequencing, kitting and batch supply share enough similarities that allow joint modelling in picking order planning and execution, whereas line stocking, just-in-time, just-in-sequence and just-in-sequence kit supply require separate modelling. Joint modelling is the precondition for setting up a software system in industrial practice, covering multiple parts supply strategies efficiently. With similar input–output relations and process steps, joint algorithms can be used. This work presents insights from a software system for sequencing, kitting and batch supply implemented in the automotive industry. The main process steps modelled are order-dependent part requests determination, bundling of part requests to picking orders, scheduling and release, and picking and transportation execution. Given the prevalence of assembly lines, sharing knowledge about successful modelling of parts supply strategies is crucial both for practitioners and researchers.

A chemometrics-based approach for the chemical prediction of lead (Pb) levels in surface soil, Dammam, Saudi Arabia

High levels of trace metals in top soil may impose serious health problems to humans and the environment. Thus, there is a need to assess the geochemical conditions of surface soils where various human activities are intensified. This study aims to evaluate contaminations associated with trace metals within Dammam, Saudi Arabia. The study also aims to hyphenate the results into a chemometrics-based approach for predicting the concentration of lead (Pb) due to its toxicity. No previous work was found on Pb prediction in top soils using chemometrics-based approach. Surface soil samples were collected from the different zones and analyzed in the laboratory for their trace metals’ constituents. According to the study’s findings, all trace metals were below international allowable metals with the highest mean concentration of Ba (309.7 mg/kg), Zn (7.9 mg/kg), and Cr (10.2 mg/kg) in industrial, agriculture and residential areas, respectively. The prediction steps involve the application of two AI-based techniques; Gaussian process regression (GPR) and Least-square (L-Boost), as well as a linear Step-Wise-Linear Regression (SWLR). The modelling was featured in two scenarios, M1 and M2, based on the input–output relationship designated according to the correlational feature extraction approach. The performance results of the models indicate that the second scenario (M2) showed higher performance skills than the first scenario (M1) in all three approaches. Overall, the performance accuracy of the models showed that the non-linear GPR-M2 showed higher performance accuracy than all the model combinations applied in the current work with 99% accuracy and an MSE of 0.012.

Q-Markov Covariance equivalent realizations for unstable and marginally stable systems

An observer-based q-Markov Covariance equivalent realization (QMC) formulation is presented in the paper. It is shown that by inserting an observer in the input–output relationship associated with the dynamical system, a QMC approach can be developed that is applicable to systems with marginally stable or unstable equilibrium points. A system of equations governing all linear state-space realizations, along with the corresponding observers, is derived from matching a set of Markov and Covariance parameters. The solution to this equation system is shown to parameterize all state-space realizations that match the pre-specified correlation functions. Four numerical examples are used to show the utility of the proposed approach. The numerical results show that the observer-based QMC presented in the paper can identify linear SISO and MIMO systems with stable, marginally stable, and unstable characteristics.

A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction

Methods based on artificial neural network (ANN) or deep neural network (DNN) applications have been proposed to predict job cycle time effectively. However, the predicting mechanism of ANNs (or DNNs) is often difficult to understand and communicate. This problem has hindered their acceptability (and practicability). Furthermore, existing explainable artificial intelligence (XAI) techniques use simpler decision rules to approximate the prediction process and/or outcomes of ANNs (or DNNs). However, these decision rules may not conform to domain knowledge, which limits their usefulness. This study proposes a modified random forest incremental interpretation (RFII) approach to address this issue. In the proposed methodology, the input–output relationship of an ANN (or DNN) is fitted by decision rules using a random forest approach. Then, domain knowledge is extracted, and decision rules are checked against this knowledge. Decision rules with poor conformance to domain knowledge are excluded. Subsequently, the remaining decision rules are aggregated in a way that is convenient for user interpretation. The modified RFII method has been applied to a real case in the literature to demonstrate its applicability.

Import competition and firm energy intensity: Evidence from China

The impact of trade on environmental performance has recently become a hot topic. However, little is known about whether import trade affects the energy use of developing countries. Therefore, we use detailed micro-firm data on China’s manufacturing sector to investigate the impact of import competition on firms’ coal energy intensity. We measure the degree of import competition by import penetration and estimate firm-level data for 2001–2010 using a fixed effects model and 2SLS strategy. The findings suggest that import competition significantly reduces coal energy intensity for Chinese firms, which implies that increasing imports will make it easier for China to meet the objective of “carbon peaking, carbon neutral”. By decomposing import competition, we find that differences in the sources of import competition can explain decline changes in firm energy intensity. The theoretical framework and mechanism tests show that the improvement of production and technology application efficiency are the main channels through which import competition decreases energy intensity. However, there is no evidence that import competition has driven firm green transformation process. Finally, we constructed an industry correlation index using the input–output relationship between industries to confirm that import competition affects the energy intensity of near-neighbor industries.

Bioenergy and Employment. A Regional Economic Impact Evaluation

Estimating green energy’s impact on regional economies of developing countries is challenging, owing both to the lack of disaggregated data of non-conventional energy sources at the subnational level and a method to address its participation in the energy matrix. We develop a methodology to solve both problems and apply it to the case of Santa Fe province, Argentina, an important producer of biofuels (biodiesel from soybean and ethanol from maize). To disaggregate the participation of biofuel sectors we combine aggregated sector information with subsector surveys. Once established the share of biofuels in the economy and their potential to create jobs, it is possible to generate statistics on the input-output relationships. With the latter, we estimate a hybrid input-output model and calculate the effects of shocks on production and employment stemming from the full utilization of existing idle capacity, as well as from new investments in the sector. The instrument allows us to several policy evaluations, for instance, of acceleration of the energy matrix transition to renewables through regulations, to study the effect of changes in relative prices of energy, determine the effect on potential employment creation of subsidies to promote the activity, etc. The sector we analyze empirically had an initial value added of 745 million dollars and employs near to 1200 persons, and an important idle capacity plus delayed projects because of external shocks. In a conservative scenario of full capacity utilization plus ongoing investments, production more than doubles, and employment can grow 414 percent. On the other hand, a 50 percent additional increase in new capacity implies a total value-added increase of 421 million dollars and a 378 percent increase in jobs. Even when the output effect is lower than in the former scenario, the employment effect is proportionally much larger since the latter scenario includes transient jobs in the construction phase.

Power Efficient Design a Compliant Robotic Leg Based on Klann's Linkage

This article presents the analysis and optimization of a compliant robotic leg based on Klann's linkage. This leg is specifically designed to efficiently distribute its power requirement over its complete motion cycle, avoiding large peaks in the energy drawn from the battery. The structural compliance of the leg will be shown to be able to both provide a satisfactory walking motion and a timely energy boost to help with the gait. Klann's linkage is selected here as the basic kinematic structure of the leg to demonstrate the proposed methodology, namely to combine in a single structure both a complex trajectory generation and energy storage/release. This work is first aiming at proposing a thorough kinematic analysis of that mechanism using planar screw theory. The latter will be shown to be able to efficiently provide the velocity equations of the linkage as well as its force input–output relationship and singularity conditions. In a second part of this article, the previous kinetostatic model will be used to design and optimize a compliant version of the leg optimizing the power required for the robot to move. Finally, experiments will be shown to support the proposed approach.

A data-based reduced-order model for dynamic simulation and control of district-heating networks

This study concerns the development of a data-based compact model for the prediction of the fluid temperature evolution in district heating (DH) pipeline networks. This so-called “reduced-order model” (ROM) is obtained from reduction of the conservation law for energy for each pipe segment to a semi-analytical input–output relation between the pipe outlet temperature and the pipe inlet and ground temperatures that can be identified from training data. The ROM basically is valid for generic pipe configurations involving 3D unsteady heat transfer and 3D steady flow as long as heat-transfer mechanisms are linearly dependent on the temperature field. Moreover, the training data can be generated by physics-based computational “full-order” models (FOMs) yet also by (calibration) experiments or field measurements. Performance tests using computational training data for a single-pipe configuration demonstrate that the ROM (i) can be successfully identified and (ii) can accurately describe the response of the outlet temperature to arbitrary input profiles for inlet and ground temperatures. Application of the ROM to two case studies, i.e. fast simulation of a small DH network and design of a controller for user-defined temperature regulation of a DH system, demonstrate its predictive ability and efficiency also for realistic systems. Dedicated cost analyses further reveal that the ROM may significantly reduce the computational costs compared to FOMs by (up to) orders of magnitude for higher-dimensional pipe configurations. These findings advance the proposed ROM as a robust and efficient simulation tool for practical DH systems with a far greater predictive ability than existing compact models.

Differential Elimination for Dynamical Models via Projections with Applications to Structural Identifiability

Elimination of unknowns in a system of differential equations is often required when analyzing (possibly nonlinear) dynamical systems models, where only a subset of variables are observable. One such analysis, identifiability, often relies on computing input-output relations via differential algebraic elimination. Determining identifiability, a natural prerequisite for meaningful parameter estimation, is often prohibitively expensive for medium to large systems due to the computationally expensive task of elimination. We propose an algorithm that computes a description of the set of differential-algebraic relations between the input and output variables of a dynamical system model. The resulting algorithm outperforms general-purpose software for differential elimination on a set of benchmark models from the literature. We use the designed elimination algorithm to build a new randomized algorithm for assessing structural identifiability of a parameter in a parametric model. A parameter is said to be identifiable if its value can be uniquely determined from input-output data assuming the absence of noise and sufficiently exciting inputs. Our new algorithm allows the identification of models that could not be tackled before. Our implementation is publicly available online as a Julia package.

Artificial neural networks for speeding-up the experimental calibration of propulsion systems

Artificial neural networks have proven to be very useful, in different areas of investigation, for the realization of black box models able to represent the relationship between input and output data with high precision. In the automotive sector too, they have found widespread use, particularly for sub-models involving non-linear aspects. Most of these applications are not predictive, but mainly exploit the ability to obtain useful input–output relationships trough learning on scattered data. In contrast, the novelty of this paper is the use of a neural network, trained with experimental data in a portion of the operation area of the system to be calibrated, within a method used for forecasting purposes. The proposed framework consists of an iterative cycle of experimental tests, with control parameters identified by the neural network model, to reduce the time required for calibrating the entire working plan. This type of approach requires knowing the areas where the predictions of the model are sufficiently accurate. Therefore, a study was conducted for three different types of internal combustion engines, to evaluate a general trend of the prediction accuracy of the model, as a function of the distance from the training zone. In particular, the area where the learning was performed (10% of the engine operating plan) was expanded by 200%, with an error of approximately 5 times that of the training phase. Finally, the proposed method was compared with the best fitting surface approach and was found to be significantly reliable and accurate.

Online continual learning with declarative memory

Deep neural networks are enjoying unprecedented attention and success in recent years. However, catastrophic forgetting undermines the performance of deep models when the training data are arrived sequentially in an online multi-task learning fashion. To address this issue, we propose a novel method named continual learning with declarative memory (CLDM) in this paper. Specifically, our idea is inspired by the structure of human memory. Declarative memory is a major component of long-term memory which helps human beings memorize past experiences and facts. In this paper, we propose to formulate declarative memory as task memory and instance memory in neural networks to overcome catastrophic forgetting. Intuitively, the instance memory recalls the input–output relations (fact) in previous tasks, which is implemented by jointly rehearsing previous samples and learning current tasks as replaying-based methods act. In addition, the task memory aims to capture long-term task correlation information across task sequences to regularize the learning of the current task, thus preserving task-specific weight realizations (experience) in high task-specific layers. In this work, we implement a concrete instantiation of the proposed task memory by leveraging a recurrent unit. Extensive experiments on seven continual learning benchmarks verify that our proposed method is able to outperform previous approaches with tremendous improvements by retaining the information of both samples and tasks.

SK and Kv4 Channels Limit Spike Timing Perturbations in Pacemaking Dopamine Neurons.

Midbrain dopamine (DA) neurons are among the best characterized pacemaker neurons, having intrinsic, rhythmic firing activity even in the absence of synaptic input. However, the mechanisms of DA neuron pacemaking have not been systematically related to how these cells respond to synaptic input. The input-output properties of pacemaking neurons can be characterized by the phase-resetting curve (PRC), which describes the sensitivity of interspike interval (ISI) length to inputs arriving at different phases of the firing cycle. Here we determined PRCs of putative DA neurons in the substantia nigra pars compacta in brain slices from male and female mice using gramicidin-perforated current-clamp recordings with electrical noise stimuli applied through the patch pipette. On average, and compared with nearby putative GABA neurons, DA neurons showed a low, nearly constant level of sensitivity across most of the ISI, but individual cells had PRCs showing relatively greater sensitivity at early or late phases. Pharmacological experiments showed that DA neuron PRCs are shaped by small-conductance calcium-activated potassium and Kv4 channels, which limit input sensitivity across early and late phases of the ISI. Our results establish the PRC as a tractable experimental measurement of individual DA neuron input-output relationships and identify two of the major ionic conductances that limit perturbations to rhythmic firing. These findings have applications in modeling and for identifying biophysical changes in response to disease or environmental manipulations.

Identifying the Self-Sustaining Mechanisms of Transonic Airfoil Buffet with Resolvent Analysis

The self-sustaining mechanisms of shock oscillations in transonic airfoil buffet are studied by wall-resolved large-eddy simulation (LES) and resolvent analysis. The LES successfully reproduces the large-scale shock oscillations over a transonic OAT15A airfoil, and the resolvent analysis using the high-fidelity LES data reveals the input–output relations in the self-sustained shock oscillations. Based on the input–output relations revealed by the resolvent analysis, this study identifies two mechanisms of the self-sustained shock oscillations. The first mechanism is closely tied to the periodic variations of the shock-induced separation height, whereas the second mechanism is related to the pressure dynamics around the shock wave. The mechanisms of the self-sustained shock oscillations identified by the input–output relations are examined in detail along with the high-fidelity LES data to aid the understanding of the feedback formulations in transonic airfoil buffet.

Adaptive PI control of ultrasonic motor using iterative learning methods

PI (proportional integral) controller is widely utilized in ultrasonic motor control systems. However, PI controllers which use fixed parameters is often difficult to ensure that the control performance meets the expectations. Based on the idea of iterative learning control (ILC), a new online adaptive adjustment method for PI control parameters is proposed. Simple P (proportional) type and PD (proportional differential) type iterative learning controllers are designed to adjust the proportional and integral coefficients of PI controller online. Using this method, the coefficients of PI controller are adaptively adjusted with the change of motor characteristics to ensure the control performance meets expectations. This method can improve the nonlinear expression ability of the controller, so that its input–output relationship can better match the characteristics of the controlled object, thereby improving the control performance. Results of comparative experiments indicate the validity of the proposed control strategy.

Using Particle Swarm Optimization and Artificial Intelligence to Select the Appropriate Characteristics to Determine Volume Fraction in Two-Phase Flows

Global demand for fossil fuels has increased the importance of flow measurement in the oil sector. As a result, a new submarket in the flowmeter business has opened up. To improve the accuracy of gamma-based two-phase flowmeters, this study employs time-feature extraction methods, a particle swarm optimization (PSO) based feature selection system, and an artificial neural network. This article proposes a fraction detection system that uses a 137Cs gamma source, two NaI detectors for recording the photons, and a Pyrex-glass pipe between them. The Monte Carlo N Particle method was used to simulate the geometry mentioned above. Thirteen time-domain features were extracted from the raw data recorded by both detectors. Optimal characteristics were identified with the help of PSO. This procedure resulted in the identification of eight efficient features. The input-output relationship was approximated using a Multi-Layer Perceptron (MLP) neural network. The innovation of the present research is in the use of a feature extraction technique based on the PSO algorithm to determine volume percentages, with results such as: (1) introducing eight appropriate time characteristics in determining volume percentages; (2) achieving an accuracy of less than 0.37 in root mean square error (RMSE) and 0.14 in mean square error (MSE) while predicting the volume fraction of components in a gas-liquid two-phase flow; and (3) reducing the calculation load. Utilizing optimization-based feature selection techniques has allowed for the selection of meaningful inputs, which has decreased the volume of computations while boosting the precision of the presented system.

Transfer learning Gaussian process regression surrogate model with explainability for structural reliability analysis under variation in uncertainties

In this paper, a Gaussian process regression surrogate model with transfer learning (TL-GPRSM) is introduced to reduce the computational cost of structural reliability analysis by using the input–output relationship of the source analysis having similarities with that of the target analysis. In addition, automatic relevance determination (ARD) is introduced for providing the explainability of the constructed model and confidence assurance of transfer learning. Two verifications were conducted: (i) the surrogate modeling of the live-load performance analysis of a steel bridge with corrosion by applying the source analysis in the undamaged condition and (ii) seismic performance analysis of a bridge pier, which required nonlinear time-history analyses for various earthquake loads. The results showed that TL-GPRSM was especially effective in surrogate modeling of the performance analysis with linear numerical calculations. Moreover, it was shown that the effectiveness of transfer learning in each modeling and explainability of the constructed model could be discussed based on the contributions of the model parameters estimated through ARD.

Coenzyme Q10 supplementation improves the motor function of middle-aged mice by restoring the neuronal activity of the motor cortex

Physiological aging causes motor function decline and anatomical and biochemical changes in the motor cortex. We confirmed that middle-aged mice at 15–18 months old show motor function decline, which can be restored to the young adult level by supplementing with mitochondrial electron transporter coenzyme Q10 (CoQ10) as a water-soluble nanoformula by drinking water for 1 week. CoQ10 supplementation concurrently improved brain mitochondrial respiration but not muscle strength. Notably, we identified an age-related decline in field excitatory postsynaptic potential (fEPSP) amplitude in the pathway from layers II/III to V of the primary motor area of middle-aged mice, which was restored to the young adult level by supplementing with CoQ10 for 1 week but not by administering CoQ10 acutely to brain slices. Interestingly, CoQ10 with high-frequency stimulation induced NMDA receptor-dependent long-term potentiation (LTP) in layer V of the primary motor cortex of middle-aged mice. Importantly, the fEPSP amplitude showed a larger input‒output relationship after CoQ10-dependent LTP expression. These data suggest that CoQ10 restores the motor function of middle-aged mice by improving brain mitochondrial function and the basal fEPSP level of the motor cortex, potentially by enhancing synaptic plasticity efficacy. Thus, CoQ10 supplementation may ameliorate the age-related decline in motor function in humans.