Adjustment Coefficient Research Articles

Design and damping performance analysis of a multistage meandering hybrid valved magnetorheological damper

Abstract The current valved magnetorheological damper has a low space utilization of the piston head, which results in insufficient output damping force, and this paper proposed a multistage meandering hybrid valved magnetorheological damper. The three-dimensional structure of the damper is established and its mathematical model is derived. The electromagnetic field finite element analysis was used to simulate the damper structure, and a comparison was made between the results of traditional annular valve structures and those of multistage meandering hybrid valve. The damping performance of a multistage meandering hybrid valved damper was experimentally studied. The results indicate that the maximum output damping force of the multistage meandering hybrid valved structure is increased by 62.2% over the traditional annular valved structure at a current of 2.4 A and a coil turn count of 350 turns. The structure can effectively utilize the piston head space and improve the output damping force of the damper. The output damping force of the damper reaches 486.4N and the adjustable coefficient K reaches 8.6. The numerical simulation results are the same as the actual experimental results.

A novel SCDM algorithm with offset centroid-driven weight adaptation and its application to appearance design of automotive steering wheels

This paper introduces a novel group single-criteria decision-making algorithm that addresses the challenges from personal biases. The algorithm utilizes offset centroid-driven weight adaptation to enhance the fairness and reliability of decisions, providing robust decision-making. The core steps of the new algorithm include splitting the evaluation dataset, calculating the offset centroid, introducing dynamic adjustment coefficients, constructing a comparison matrix, and calculating the weighted mean. To empirically validate the effectiveness and applicability of the proposed algorithm, we conducted a case study involving five appearance design alternatives for automotive steering wheels, and the numerical results demonstrate the algorithm’s substantial improvements on group decision-making outcomes. Remarkably, the algorithm not only facilitates the flourishing of a fair decision-making environment but also can effectively handles biased decision-making scenarios and mitigate the impact of unfairness. By utilizing this algorithm, decision-makers can alleviate individual biases and enable fairer and more reliable decision-making processes. Consequently, this algorithm introduces a novel approach for tackling complex decision problems and exhibits promising prospects for practical applications. Its versatility renders it highly valuable in diverse decision-making processes, empowering decision-makers to achieve fairness and precision in their choices.

A method for intelligent identification of faults in seismic using an attention-based ES-UNet network with model re-training learning

Accurate fault identification provides an important basis for well location deployment, oil and gas resource development. However, obtaining a large number of fault samples through manual labeling or interpretation is difficult. Conventional convolutional neural networks have certain challenges in finely characterizing low-order faults and fault continuity. This paper studies a 3D intelligent fault identification method that integrates deep learning with attention mechanism. Based on 3D-UNet network, the method constructs a dual attention fault identification model with an encoder-decoder structure, which focuses on enhancing low-order fault information and extracting key features. An adaptive hybrid loss function is proposed to address the imbalance problem of fault proportions in seismic amplitude data. The function introduces an adjustment coefficient to adaptively enhance the attention to high-order or low-order faults according to the actual situation of the fault, improving the accuracy and continuity of fault identification. Furthermore, a model re-training learning optimization method is presented to capture the missed fault information in the first training and repair the connectivity of low-order faults. Applying this method to the synthetic data set, the accuracy can be improved to 0.9732, and the loss can be converged to 0.0982. The results of shale reservoirs in the North Sea Basin and Kerry-3D indicate that the method can reduce the situation of fault missing and misidentification, effectively improve fault identification accuracy, and enhance low-order fault identification by obtaining more contextual fault features.

Ranking DMUs by using interval efficiencies in data envelopment analysis

Efficiency is a relative measure that allows assessment across different ranges. Evaluating the performance of decision-making units (DMUs) from an optimistic perspective yields the best relative efficiency (optimistic efficiency), which establishes an efficiency frontier. Conversely, evaluating from a pessimistic perspective produces the worst relative efficiency (pessimistic efficiency) and creates an inefficiency frontier. This study examines the efficiency of DMUs in two scenarios and proposes models for adjustment coefficient. The pessimistic and optimistic efficiencies are adjusted to the lower and upper bounds of the DMUs based on the adjustment coefficient, enabling determination of efficiency intervals for all DMUs, as well as evaluation and ranking. A Hurwicz criterion-based approach is introduced and applied to compare and rank the interval efficiencies of DMUs. Two numerical examples are examined using the proposed DEA adjustment coefficient models to demonstrate its potential application and validity.

Unraveling the Link Between Environment and Economic Growth in Türkiye

This study examines the short and long-run and causative interconnections between carbon dioxide (CO2) emissions, economic growth, energy use, and industrialization in Türkiye using yearly data from 1971 to 2021. The paper employs the cointegration autoregressive distributed lag (ARDL) model, which indicates the presence of a long-run nexus between the variables and the estimated long-run coefficient of economic growth, energy use, and industrialization exhibit a sensitivity of -0.726, 0.563, and 0.548 changes in CO2 emissions respectively. The error-correction term is -0.563 and significant at a 1% significance level under the cointegration-ARDL model, suggesting that the deviations from the long-run equilibrium between the variables will be addressed through correction by almost 56.3% yearly. In other words, the speed of adjustment coefficients indicates that when disequilibrium occurs, adjustments returning to equilibrium take almost two years. The Toda and Yamamoto (1995) causality test indicates a unidirectional causal link from industrialization to CO2 emissions. This finding implies that industrialization harms environmental quality in Türkiye; therefore, Türkiye must prioritize sustainable industrial development and embrace green technologies.

Ag/ATO resistor pastes with adjustable temperature coefficients of resistance

Ag/ATO resistor pastes with adjustable temperature coefficients of resistance

Methodology For Designing Facing Building Materials with Electromagnetic Radiation Shielding Functions

A calculation method for designing facing building materials with the functions of shielding electromagnetic waves has been developed. The method makes it possible to design materials with the required shielding efficiencies and adjustable coefficients of absorption and reflection of electromagnetic waves. The dependence of the shielding efficiency on the mode of pressing the material has been studied. Verification of the calculation apparatus showed satisfactory convergence of theoretical and experimental data.

The Effects of Etchant on via Hole Taper Angle and Selectivity in Selective Laser Etching.

This research focuses on the manufacturing of a glass interposer that has gone through glass via (TGV) connection holes. Glass has unique properties that make it suitable for 3D integrated circuit (IC) interposers, which include low permittivity, high transparency, and adjustable thermal expansion coefficient. To date, various studies have suggested numerous techniques to generate holes in glass. In this study, we adopt the selective laser etching (SLE) technique. SLE consists of two processes: local modification via an ultrashort pulsed laser and chemical etching. In our previous study, we found that the process speed can be enhanced by changing the local modification method. For further enhancement in the process speed, in this study, we focus on the chemical etching process. In particular, we try to find a proper etchant for TGV formation. Here, four different etchants (HF, KOH, NaOH, and NH4F) are compared in order to improve the etching speed. For a quantitative comparison, we adopt the concept of selectivity. The results show that NH4F has the highest selectivity; therefore, we can tentatively claim that it is a promising candidate etchant for generating TGV. In addition, we also observe a taper angle variation according to the etchant used. The results show that the taper angle of the hole is dependent on the concentration of the etchant as well as the etchant itself. These results may be applicable to various industrial fields that aim to adjust the taper angle of holes.

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions.

Why risk attitude differs between macro and micro level? A decoherence perspective

This paper aims to explain and solve the problem of the inconsistency of risk attitude between the micro and macro level, which theoretical models often ignore. There are significant differences between micro and macro level when using Chinese household-level and country-level data. We build a model with heterogeneous agents by introducing an interactive utility function and derive a compatible formula for estimating risk attitude. This formula restores the “Coherence” among micro individuals. In the explicit function form, we obtain an expression of the adjustment coefficient α, and then (1) macro risk attitude; (1) micro risk attitude; (3) the difference between them, and (4) what the difference comes from. From the logarithmic interactive utility function, the estimated RRA value is 5.7036, or approximately, the adjustment parameter. When taking the adjustment parameters into consideration, reserchers can obtain more accurate parameters. This paper inserts a term in the social utility function which summarizes the interaction between households and which leads to a modification of absolute/relative risk aversion. The contribution of the paper lies in an attempt at reconciling the micro and the macro parameter by referring to an insight borrowed from the “theory of decoherence” used in physics and help to set accurate parameters in utility function in the theoretical models.

Multi-sector environmental efficiency and productivity: A general Leontief optimization method

For efficiency evaluation purposes, a multi-sector economy is always regarded as a “black box” in which the linkages among sectors are not considered. This leads to biased efficiency evaluation and failure to identify the sources of inefficiency. To solve these problems, this paper proposes a general Leontief optimization method for environmental performance estimation. The proposed method can assess the environmental benefits that result from the adjustment of input coefficients and environmental factor intensities. Moreover, this study proposes two models under different elasticity of input substitution assumptions. The difference in environmental efficiency between the two assumptions is defined here as the substitution efficiency. Further, a new Luenberger environmental productivity indicator is constructed that contains a new element reflecting the effect of input substitution. To demonstrate the new method, a numerical example is used to compare the estimation results between the general Leontief optimization method and the traditional data envelopment analysis method. Then, the environmental performance associated with CO2 emissions and energy consumption in China is studied over two years, i.e., 2012 and 2017. By fully opening the “black box” of how to improve environmental efficiency, this study provides a reference with regard to cost-effective specific measures for policymakers in China.

ЗАВИСИМОСТЬ ТЕМПЕРАТУРЫ ПЕРЕДНЕЙ ПОВЕРХНОСТИ ТОКАРНОГО РЕЗЦА ОТ ЭВОЛЮЦИОННЫХ ИЗМЕНЕНИЙ ТРИБОДЕФОРМАЦИОННЫХ ПОКАЗАТЕЛЕЙ ПРОЦЕССА ОБРАБОТКИ

The study objective is modeling the temperature distribution on the surface of a heavily loaded tribocontact - front surface of a lathe tool - chips taking into account evolutionary changes in the cutting system.
 The task to which the paper is devoted is to assess the influence of evolutionary changes of tribodeformation parameters of the cutting process on the average and maximum temperature of the front surface of the lathe tool.
 Research methods. Contact temperatures are found out on the basis of mathematical modeling using data obtained during field experiments at longitudinal turning of 15X2NMFA steel blanks without cooling by T15K6 hard alloy plates. During resistance tests, the chip contact length with the front surface and the chip reduction coefficient used for calculation are determined, as well as the average temperature in the cutting zone, the value of which is used to assess the adequacy of the results obtained by digital modeling. 
 The novelty of the work. Forecasting the temperature increase on the front surface of the cutter, taking into account the contribution of evolutionary changes in tribodeformation parameters of cutting.
 The results of the study. In the studied cutting system, according to the results of resistance tests, a decrease in the chip reduction coefficient is recorded over the processing time. The temperature distribution on the front surface of the cutter is modeled in two versions: taking into account the evolutionary adjustment of the chip reduction coefficient and related parameters – sliding velocity and thickness of the plastically deformed layer in the chip, and without taking into account their changes. It is found out that modeling adjusted for changes in tribological indicators allows to obtain calculated values of the temperature of the front surface closest to the experimentally recorded of average temperature in the cutting zone. 
 Conclusions: Forecasting contact temperatures adjusted for evolutionary changes in tribodeformation parameters will allow to determine more accurately the moment in the evolution of the cutting system at which critical temperature values can be reached, and further processing is associated with the risk of critical wear or deterioration of the treated surface.

A Quantitative Analysis on the Coordination of Regional Ecological and Economic Development Based on the Ecosystem Service Evaluation

The coordination between regional ecological and economic development has become a crucial topic in current environmental and development research due to the establishment of sustainable development goals and the acceleration of urbanization. In this study, an improved eco-economy coordination (EEC) model is proposed to evaluate the coordination of regional ecological and economic development. This study focuses on Wuhan, China, and utilizes socioeconomic and remote sensing data from 2000 to 2015 to measure both static and dynamic ecosystem service values (ESV). ESVs are the direct and indirect benefits produced by ecosystems that support human survival and development. The calculated ESVs are then integrated into the ecological–economic coordination (EEC) evaluation. This study aims to conduct a comparative analysis of the ecological-economic coordination across various districts of Wuhan, with a focus on spatiotemporal factors. In addition, this paper highlights the role of environmental adjustment coefficients in enhancing the EEC assessment. The results show that (1) the total static ESV experienced a loss of RMB 1.2 billion (approximately USD 169 million) and showed a decreasing trend, while the dynamically corrected ESV reversed this trend; and (2) EEC based on static ESV showed a low degree of conflict between ecological and economic reconciliation compared with a significant increase in EEC based on dynamic ESV. Based on static ESV, the EEC showed a low degree of conflict between ecological and economic reconciliation compared to a significant increase in EEC based on dynamic ESV. After being corrected by environmental adjustment coefficients, the EEC values showed a more differentiated distribution. Most regions demonstrated an overall upward trend in EEC, with a few, such as Wuchang District and Jiang’an District, being notable examples. However, Jianghan District presented a high conflict status. Finally, a series of decision-making suggestions are given based on relevant study results, and an important policy implication is that the coordination of economic growth and ecological protection, under large downward pressure from EEC values, needs to be paid special attention in policy decision-making.

Bounds for the Ruin Probability in the Sparre–Andersen Model

We obtain the upper and lower bounds for the ruin probability in the Sparre–Andersen model. These bounds are established under various conditions: when the adjustment coefficient exists, when it does not exist, and when the interarrival distribution belongs to certain aging classes. Additionally, we improve the Lundberg upper bound for the ruin probability.

Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

Path planning for mobile robots poses a challenging optimization problem, requiring the discovery of a near-optimal path within diverse constraints. Conventional particle swarm optimization (PSO) algorithms encounter limitations in solving constrained problems, vulnerability to local optima, and premature convergence. To address these challenges, this paper proposes a bi-population PSO algorithm with a random perturbation strategy (BPPSO), which divides particles into two subpopulations. The first subpopulation enhances global search capabilities by considering the quality of particles and the optimal solution of a randomly selected particle when updating velocities. The second subpopulation strengthens local search using a linear cognitive coefficient adjustment strategy. Moreover, a counter tracks iteration without improvement in the global best position. Upon reaching a predefined threshold, random perturbation is added to the positions of all particles in both subpopulations, increasing diversity and enhancing the ability to escape local optima. The performance of BPPSO was experimentally validated across three benchmark functions and four environment models. The results have demonstrated that the proposed BPPSO outperforms existing PSO algorithms and other established path planning algorithms in terms of path quality and running time, highlighting the feasibility of BPPSO in resolving the challenge of mobile robot path planning.

Diffusion coefficients of zirconium (IV) acetylacetonate: Measurements and correlation in both pressurized liquid and supercritical fluid

Limited data exist on the diffusion coefficient of a solute in both pressurized and supercritical fluids, despite its crucial role as an essential transport property. In this study, we present the first-ever determined diffusion coefficients of zirconium acetylacetonate (Zr(acac)4) in two distinct fluids: pressurized ethanol and supercritical carbon dioxide. Using the Taylor dispersion method, we measured the diffusion coefficients in pressurized ethanol at temperatures between 298.15 and 333.15 K, reaching pressures up to 30.00 MPa. In supercritical carbon dioxide, we employed the chromatographic impulse response (CIR) method, conducting the experiments at temperatures from 308.15 to 343.15 K and pressures up to 35.00 MPa. Remarkably, predissolving Zr(acac)4 in supercritical carbon dioxide and injecting it into the diffusion column enabled the accurate determination of diffusion coefficients in supercritical carbon dioxide, which were approximately an order of magnitude higher than those in pressurized ethanol. Our investigation revealed diffusion activation energies, ranging from 26.7 to 10.2 kJ mol−1 at pressures between 10.00 and 30.00 MPa in supercritical carbon dioxide and from 20.4 to 19.6 kJ mol−1 at pressures between 20.00 and 30.00 MPa in pressurized ethanol. All diffusion data, in both liquid and supercritical states, can be effectively correlated using the equation Sc = ∑n=03(Cnηn) with 2 parameters and 4 adjustable coefficients. This correlation demonstrates high accuracy, with an average absolute relative deviation (AARD) of 3.7 % over a wide fluid viscosity range of 21.0 to 1305.0 μPa s. Additionally, it is observed that all diffusion data can also be well-fitted with the equation D12/T = αηβ with 2 parameters and 2 adjustable coefficients, achieving an AARD of 3.5 %.

Phase to intensity modulation and fading compensation using a single ring resonator for radio-over-fiber links.

In this study, we present a simulation-based analysis of radio-over-fiber (ROF) transmission links incorporating both phase modulation (PM) and a single ring resonator (RR) as the modulation transformer (MT). This configuration offers cost-effectiveness, enhanced operational stability, facile reconfiguration, and heightened robustness. The optimization of the RR involves a comprehensive adjustment of the power coupler coupling coefficient (k) and the roundtrip optical phase shift (φ) to attain superior characteristics in terms of power output, bandwidth, dispersion, and nonlinearity, individually. The simulation encompasses the transmission of diverse data formats, including QPSK, 16QAM, and 16QAM-based OFDM, modulated by the PM-RR system. The results reveal a 0.25 dB improvement in nonlinearity tolerance, increased power, and superior fading mitigation compared to the conventional intensity modulation (IM) approach. Furthermore, through careful tuning of the phase response, the Q factor of the PM-RR system exhibits an enhancement exceeding 40% over a 100 km fiber length when compared to the Mach-Zehnder modulator (MZM) system.

Refractive index and related properties for the mixture (1, 4-dioxane + water) at temperature T= (295.15, 298.15, 301.15, 304.15, 307.15, 310.15 and 313.15) K

ABSTRACT The refractive index n D for the mixture (1,4-dioxane (1) + water (2)) was measured over the whole composition range at different temperatures and atmospheric pressure. From measured values of the refractive indices, the molar refraction R m , reduced molar free volumes V m R m , molecular radii r and internal pressure, P int were calculated. On the other hand, some excess parameters were also deduced. The obtained results have been discussed and interpreted in terms of the type and nature of specific intermolecular interactions existed between the components. Furthermore, we have used nine mixing rules for refractive index proposed by different authors to calculate the refractive indices of the present mixture. A comparative study was carried out between the measured and calculated indices. The Redlich–Kister polynomial equation has been used to fit the presented excess parameters to derive the adjustable fitting coefficients and estimate the standard error values.

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

AbstractThe creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10.

Application of the new hybrid model LMAW-G-EDAS multi-criteria decision-making when choosing an assault rifle for the needs of the army

The paper addresses the selection of the most favorable alternative in the form of assault rifles to meet the requirements arising from modern combat operations. The complexity of the problem, conditioned by the different structural elements of automatic rifles and the specific situations of their use, has caused the application of Multi-Criteria Decision-making (MCDM) methods. A hybrid model was developed to guide the selection of the appropriate automatic rifle. The criteria were defined by experts, where the calculation of the weight coefficients of criteria was performed using the LMAW method Employing the grey EDAS method for MCDM, the study identifies an assault rifle that offers the most advantageous capabilities for carrying out firing tasks in the context of daily combat operations, with the aim of equipping the armed forces and raising the operational capability of the units. The validity of this model was verified through sensitivity analysis involving the adjustment of criteria weight coefficients.