Output Response Research Articles

Output performance analysis of a piezoelectric micro nuclear battery powered by radioisotopes

Abstract This paper investigates the output performance (output voltage, electrical energy output, power output) of a piezoelectric micro nuclear battery powered by radioisotopes. The theoretical formulations are base on Euler-Bernoulli beam theory and include the effects of load nonlinearity due to radioactive source. By employing extended Hamilton’s principle, the nonlinear electromechanical Lagrange equations are derived then solved by using Runge-Kutta method to obtain the dynamic output response. The results based on present electromechanical dynamic model are validated through direct comparisons with the results in the open literatures. The effects of initial gap, length of the piezoelectric layer, thickness of the collector and load impedance on the output voltage, energy output and power output of the piezoelectric micro nuclear battery are discussed in detail.

Fiscal Policy, International Spillovers, and Endogenous Productivity

AbstractThe paper presents empirical evidence on the international effects of U.S. fiscal policy from structural vector autoregressions identified through external instruments in a panel setting for the G7 countries. An exogenous increase in U.S. government spending is estimated to produce sizable positive responses of output and consumption in the rest of the G7 countries, both about half as large as their domestic U.S. counterparts, while strongly depreciating the U.S. terms of trade and lowering the U.S. trade balance and short‐run real interest rates. Moreover, fiscal shocks are estimated to have a strongly positive impact on hourly labor productivity in the private sector. We solve a two‐country New Keynesian model in closed form and show that a low cost elasticity of varying technology utilization can simultaneously explain the positive productivity, consumption, and international spillover effects as well as the real depreciation resulting from expansionary U.S. government spending shocks.

Interventricular abnormal septum displacement as contributory mechanism for impaired peak oxygen consumption and exercise cardiac output response in heart failure

Abstract Background In Heart failure (HF), the role of septum position and abnormal right ventricular (RV) geometry in determining exercise performance is unknown. Especially an abnormal interventricular septal (IVS) position is a well-known sign of RV pressure overload which affects left ventricular filling and output due to interdependence. Aim We postulated that an abnormal septum displacement during exercise and RV geometry alteration would affect peak exercise oxygen consumption (VO2) and cardiac output (CO) increase. Methods 17 HF patients with reduced ejection fraction (HFrEF) (mean age 71±12) underwent a cardiopulmonary exercise testing imaging (iCPET) with RV 3D-imaging analysis and were compared with a control population (mean age 67±14). 3D imaging of the RV chamber was examined off-line using the 4D RV TomTec software and obtained 3D mesh of the RV model using custom software to obtain the mean curvature value of IVS in 4 regions of: inflow tract (RVIT), outflow tract (RVOT), apical and body.We acquired measurements of curvature during end-diastole (ED) and at end systole (ES) phases and obtained a parametric curvature map. Results HF patients typically exhibited an abnormal of septal curve, with a more leftward configuration either at rest and under exercise (rest =−0.01±0.004 at ED, and −0.01±0.006 at ES; peak exercise= −0.01±0.004 at ED, and −0.01±0.004 at ES, Figure) compared to controls (rest=−0.02±0.003 at ED, and −0.02±0.005 at ES; peak exercise −0.02±0.008 at ED, and −0.03±0.006 at ES, Figure). Notably, the degree of the IVS curvature was found to linearly correlate with an impaired gas exchange performance as lower peak VO2 and a limited CO in HF (respectively r=0.62, p<0.001 and r=0.5, p<0.001 at ED during exercise; r=0.34, p<0.001 at ES during exercise, Figure). Conclusions In HF, the RV geometrical alterations reflected by an abnormal IVS displacement appear as novel contributory factors to a reduced exercise performance, impaired oxygen uptake and decreased CO.

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors

AbstractThe TEROS 10 and TEROS 12 are widely used capacitance‐based sensors for measuring volumetric water content (), but few systematic studies of the measurement volume and accuracy of these sensors have been published. The objectives of this study were to (1) calibrate and validate these sensors in mineral soils, (2) determine the sensing volume of each sensor, and (3) evaluate the temperature sensitivity of the sensors. Both sensors were calibrated in the laboratory using columns of packed soil at multiple moisture levels from air‐dry to near saturation. Sensors were validated using additional laboratory and field measurements. The sensing volume was determined by quantifying the response of raw sensor outputs while increasing the level of oven‐dry sand, moist sand (0.100 cm3 cm−3), or water around the sensor in the radial and axial directions. Temperature sensitivity was tested in controlled conditions over a range of temperatures from 2°C to 40°C using encapsulated sensors in packed soil columns at constant . Linear calibration models resulted in mean absolute error ≤0.030 cm3 cm−3 for both sensors during laboratory and field validations. In moist sand, the sensing volume contributing 95% of the maximum sensor response was 439 cm3 for the TEROS 10 and 423 cm3 for the TEROS 12. Both sensors exhibited changes in of ≤0.02 cm3 cm−3 when subjected to a temperature change from 2°C to 40°C. For the soils considered here, both sensors demonstrated accuracy that is likely sufficient for a variety of agricultural and hydrological applications.

Green dielectric exploration: spectrographic analysis and technical feasibility of vegetable oils in EDM via PIV, TOPSIS, and MOORA evaluation methods

Achieving a sustainable dielectric medium for Electrical Discharge Machining (EDM) has now drawn the attention of researchers and industries alike. Increased awareness among the Government and NGOs paved the way for the manufacturing industries to move towards a green environment and sustainability in the production process. To search for sustainable dielectric, 5 different types of oils viz., Jatropha, coconut, groundnut, sunflower, and EDM 4 oil (conventional oil) are experimented in this paper and revealed its suitability of potential replacement for the conventional dielectric. The properties such as viscosity, density, flash point, electrical conductivity, and BDV (Break down Voltage) are tested as per the standards. Since the number of output response is more than, Multi Attribute Decision Making (MADM) algorithms namely PIV, TOPSIS, and MOORA are used for the ranking and optimization. Entropy weight method is adopted to endow criteria weight to each of the MADM techniques. Jatropha oil has been ascertained to be rank 1 followed by groundnut, coconut, sunflower, and EDM 4 oil. Subsequently, the Gas Chromatography & Mass Spectrometry (GC–MS) analysis has revealed the quantitative constituents present in Jatropha as well as remaining oils. Furthermore GC–MS analysis reports that the retention time of Jatropha (43.042 min) is relatively longer than other oils except coconut oil, to release Ricinoleic acids. Groundnut and EDM 4 oils took relatively less retention time to release the alkanes. Machining performance was also conducted with the obtained dielectric and compared with conventional type oil (EDM 4 oil).

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Optimization of TIG welding process parameters using Taguchi technique for the joining of dissimilar metals of AA5083 and AA7075

This research paper aims to optimize the TIG welding parameters to join dissimilar metals of AA5083 and AA7075 using the Taguchi technique. The TIG welding current and root gap of butt geometry configuration are considered input parameters, and welding characteristics such as tensile strength, 0.2% of yield strength, hardness, and impact energy are output responses. The base metals are joined according to the Taguchi L-09 design of experiments. The welded samples were inspected using the X-ray radiography method for internal defects. Tensile properties, hardness number, and impact energy of different welding coupons were evaluated by conducting the uni-axial testing, a Brinell hardness test, and an Izod impact test, respectively. A better weld strength of 224 MPa was observed at 210 A welding current, and the root gap of 1.5 mm was maintained. Better hardness and impact energy values were observed when the root gap of 1.5 mm and welding current of 220 A were maintained. The root gap is the primary factor influencing tensile strength enhancement, which accounts for 44.78% of the effect, followed by 40.5% welding current. Root gap is the parameter that affects the tensile properties, hardness number, and impact toughness the most. The findings of this paper suggest the optimal parameters for welding AA5083 and AA7075 dissimilar base metals, which are suitable for complex structures requiring both durability and resistance to harsh environments.

Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN

Recent years have seen the proliferation of fused deposition modeling (FDM) as a means of manufacturing biodegradable products, for different applications such as rigid packaging, agricultural and biomedical. Blends of Polyhydroxyalkanoates (PHA) and polylactic acid (PLA) have been investigated to ascertain their prospective applications through FDM. This paper includes three parameters that affect the build process: layer height, nozzle temperature, and flow rate. 3D printed PLA/PHA can be characterized mechanically, and process parameters can be optimized to maximize design functionality. The experimental setup utilized a Taguchi L9 design, and TOSPIS was employed to optimize the output results. Using TOPSIS analysis, 0.2 mm layer thickness, 195 °C nozzle temperature, and 100 % flow rate were found to be the most optimal initiation parameters. The Taguchi analysis was used to analyze the output responses, and it was found that layer height had the greatest influence on mechanical properties, followed by flow rate and nozzle temperature. The percentage elongation at break has been improved significantly by adding PHA i.e., 170 % compared to PLA (5–10 %). This paper presents a framework for in-depth mechanical characterization of PLA-PHA 3D-printed parts, along with methods for optimizing process parameters to achieve optimal performance, as well as tools for modeling output responses using GA-ANN with an accuracy of 95 %.

Research on the characteristics of a pneumatic proportional valve

Abstract The execution of autonomous driving in heavy-duty trucks necessitates stringent regulation of their braking torque output. Hydraulic retarder serves as a widely auxiliary braking system in heavy-duty vehicles, with the pneumatic proportional valve functioning as the core component of its control system, regulating the output response and control precision of the braking torque of the hydraulic retarder. To improve the accuracy of brake torque control for hydraulic retarders, it is critical to conduct thorough research on the control system, especially the pneumatic proportional valve. Therefore, A detailed analysis of the working mechanism of pneumatic proportional valves was conducted, encompassing the construction of mathematical and simulation models. The efficacy of the simulation model was confirmed via experimental validation. When the control pressure increases, the simulation results are close to the experimental results, with a maximum discrepancy of 18%; when the control pressure decreases, the simulation results show a similar trend to the experimental results. The research can aid in formulating control methodologies for pneumatic proportional valves and offer theoretical guidelines for future exploration into boosting the precision of hydraulic retarder braking torque control.

Chronotropic Incompetence Rather than Stroke Volume Is the Main Driver of Impaired Cardiac Output Response in Patients on Hemodialysis

Chronotropic Incompetence Rather than Stroke Volume Is the Main Driver of Impaired Cardiac Output Response in Patients on Hemodialysis

Impacts of Zirconium Carbide Incorporated Magnesium Alloy Nanocomposite on Wear Properties

In the study, zirconium carbide nanoparticle (ZrC-50 nm) incorporated magnesium alloy (AZ61) nanocomposite was prepared by liquid state stir cast process, and its wear properties were evaluated by different load (U), sliding speed (V), and sliding distance (W). The impacts on wear rate (WR) and coefficient of friction (COF) of AZ61 alloy nanocomposite were studied. With the support of a pin-on-disc wear tester, the dry sliding wear characteristics were measured. Moreover, to find the optimum process parameter on minimum WR and better COF characteristics, the L27 orthogonal array utilized U, V, and W as the input factors, and the WR & COF were treated as output responses. Based on the analysis, load was found to be the most dominant input factor that influences the WR and sliding distance was the factor for fixing the COF.

Territorial Patterns of European Innovation in the Context of Different Innovation Output Proxies: A Spatial MGWR-SAR Approach

AbstractThe paper seeks to explore the drivers of European innovation represented by three innovation outputs (patent, trademark, and design applications), emphasizing spatial autocorrelation and heterogeneity. It includes data from 202 regions from 22 European Union (EU) Member States, along with 18 regions from Switzerland, Norway, and Serbia in 2019, providing a more comprehensive geographic scope. By considering multiple indicators of innovation output, including patents, trademarks, and design applications, the main objective is to examine spatial innovation spillovers and the heterogeneous responses of regional innovation output to innovation inputs in the context of European regions. To achieve this goal, the main instrument of the analysis is a newly proposed methodological framework called Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) models. The analysis suggests that while all innovation inputs (most-cited publications, research and development expenditure in the business sector, human resources in science and technology, and population density) are justified in increasing all innovation outputs, the strength of particular determinants of innovation might vary across regions. Moreover, the analysis reveals valuable insights into how spatial spillovers influence regional innovation. The impact of spatial connections varies across the regions, with patents showing the strongest linkages, affecting about 92.27% of regions. Although trademarks and designs have fewer spatial connections (approximately 50% of regions), they still play a significant role in innovation. Although patents have traditionally dominated discussions of innovation, the findings reveal the importance of incorporating designs and trademarks as complementary indicators. Overall, the study highlights the need for multiple metrics to comprehensively evaluate innovations.

Effect of mixing-ratio and current on Mo-Cu-WC-Si coating via EDC

The present study investigates the influence of the mixing ratio of copper (Cu) and molybdenum (Mo) powders in Mo-Cu-WC-Si composite powder metallurgy (P/M) briquettes on the performance of coatings produced on Al-2014 alloy using electrical discharge coating. Three mixing ratios of Cu and Mo are selected during briquette preparation, by varying Cu in 5 wt.% decrements and Mo in 5 wt.% increments: 50Cu:30Mo, 45Cu:35Mo, and 40Cu:40Mo, with fixed 15WC:5Si. The results indicate that even a 5 wt.% variation of copper significantly affects the coating output responses. At 3 A current the 50Cu:30Mo combination shows lowest roughness of 2.4 μm, followed by 3.2 μm, and 5.4 μm roughness with 45Cu:35Mo and 40Cu:40Mo. Coating with 50Cu:30Mo ratio shows the highest wear reduction, achieving a 3-fold decrease compared to the uncoated Al-2014. Furthermore, coated samples depict a 42% reduction in corrosion rate compared to uncoated sample. Experimental results suggest that the briquette with a higher copper percentage (50 wt.%) provides a more suitable coating.

Development of Second Prototype of Twin-Driven Magnetorheological Fluid Actuator for Haptic Device

Magnetorheological fluids (MRFs) are functional fluids that exhibit rapid and reproducible rheological responses to external magnetic fields. An MRF has been utilized to develop a haptic device with precise haptic feedback for teleoperative surgical systems. To achieve this, we developed several types of compact MRF clutches for haptics (H-MRCs) and integrated them into a twin-driven MRF actuator (TD-MRA). The first TD-MRA prototype was successfully used to generate fine haptic feedback for operators. However, undesirable torque ripples were observed due to shaft misalignment and the low rigidity of the structure. Additionally, the detailed torque control performance was not evaluated from both static and dynamic current inputs. The objective of this study is to develop a second prototype to reduce torque ripple by improving the structure and evaluating its static and dynamic torque performance. Torque performance was measured using both constant and stepwise current inputs. The coefficient of variance of the torque was successfully reduced by half due to the structural redesign. Although the time constants of the H-MRC were less than 10 ms, those of the TD-MRA were less than 20 ms under all conditions. To address the slower downward output response, we implemented an improved input method, which successfully halved the response time.

Machine learning approach for predicting the machining characteristics of additive manufactured ABS material

An Additive manufacturing process offers advantage for the production of intricate designs with increased efficiency and reduced material waste. In this research, an experimental and analysis work has been conducted to identify the significant 3-D printed input parameters effecting the output response characteristics as kerf width. The correlation between experimental and predicted values has been established through the application of Artificial Neural Network (ANN), Support Vector Machine Regression (SVMR), and Response Surface Methodology (RSM) techniques. The validation of predicted models conducted with the help of mean prediction error, maximum error and minimum error. The performance of ANN model was analyzed by feedforward-propagation method using Levenberg- Marquardt algorithm. The maximum prediction error investigated by ANN, SVM, and RSM were noted as 4.28, 5.18, and 5.84 respectively. The optical micrographs have been investigated to understand the hole and crater formation phenomenon.

ANN modeling and optimization of friction stir welding performance for AA6061 and AA5083 alloy joints

This research focused on the characteristics of the AA6061/AA5083 alloy joints which are fabricated by the friction stir welding (FSW) technique. The impact of FSW parameters such as tool tilt angle, pin depth, and tool geometry on the mechanical properties and fractographic characteristics were studied. Ultimate tensile strength (UTS), hardness (HBN), and specific wear rate (SWR) were examined for characterization while fractography analyses were done using scanning electron microscopy. Taguchi-Grey relational analysis (GRA) was employed to execute multicriteria optimization and recognize the best grouping of parameters. The optimized values attained from this analysis were 259.2 MPa for UTS, 94.9 HV for HBN, and 0.0819 m³/Nm for SWR. The analysis of variance (ANOVA) results derived from the GRA revealed that tool geometry exerted the most significant influence (39.79%) on output factors, followed by pin depth (24.90%) and tilt angle (24.26%). To enhance the predictive accuracy of the output responses, an artificial neural network (ANN) model was developed utilizing the Levenberg-Marquardt (LM) algorithm. The optimized ANN architecture, configured as (3–10–3), exhibited robust regression analysis outcomes, showcasing correlation coefficients ( R) of 0.99999, 0.99967, and 0.99994 for the training, validation, and test datasets. The overall R-value, computed at 0.99958, affirmed high conformity between experimental and predicted values.

Government size and the effectiveness of fiscal policy: the bigger the better?

Abstract This study investigates the effect of government size, as measured by the tax revenue to gross domestic product (tax-GDP) ratio, on output responses to increases in government purchases. First, we show that in a standard static neoclassical model, the stimulus effect of fiscal expansion on output increases with the tax-GDP ratio. This finding is quantitatively confirmed using a dynamic neoclassical model with standard functional forms and parameter values. To empirically test the theoretical findings, we analyze the responses of macroeconomic variables to an unanticipated increase in government purchases for 12 Organisation for Economic Cooperation and Development (OECD) countries during 1985–2019 using a state-dependent local projection method. The estimation results reveal that while output responses to an unanticipated fiscal expansion are significantly positive when the tax-GDP ratio is high, they are statistically indistinguishable from zero when the ratio is low. Overall, our findings suggest that fiscal expansion can stimulate output more effectively at high tax rates, unlike the well-known predictions of the traditional Keynesian model.

The effect of size concentration, lubricant and plasticizer agents contents on cold sizing process performance of cotton warp yarns: modelling and optimization studies

Cold sizing is the alternative to conventional hot sizing of cotton warp yarns due to this low-energy consuming character. This work aimed to evaluate the effects of size recipe ingredients on the cold sizing performance of cotton warp yarns. A three factors-three levels Box-Behnken experimental design and a response surface methodology were investigated to examine the effect of size concentration (40, 50, and 60 g/L), lubricant agent content (0%, 4%, and 8%), and plasticizer agent content (0%, 5%, and 10%) on sizing properties. In this work, the effect of experimental parameters was determined by using the analysis of variance (ANOVA) test. Therefore, a multi-objective optimization technique was used to optimize the output responses in terms of the maximum value of size cohesion power, the maximum value of size adhesion to cotton fibers, the maximum values of mechanical properties of sized cotton warp yarns, and the minimum value of hairiness of warp yarn after cold sizing. Finally, the experimental results are in good agreement with those obtained by SEM and FTIR analysis. In addition, desizing efficiencies and wettability results proved that the optimum size recipe was successfully removable from cotton fabrics by the desizing process in hot water.