Exponential Power Research Articles

Approximation of the Pressure-discharge Curve in Inline Drip Irrigation System

The efficiency of drip irrigation systems depends directly on the uniformity of water discharge from emission devices. Ideally, all emitters should discharge equal amounts of water, but variations occur due to hydraulic and manufacturing factors. This study established the pressure-discharge relationship curve and determined emitter flow variation caused by the hydraulic and the manufacturer’s coefficients of variation for 2 and 4 lph inline emitters. The power exponent and constant of the pressure-discharge curve were determined by measuring the emitter flow rates at operating pressures ranging from 0.2 to 3.0 kg/cm². The emitter flow rates of 100 emitters were measured at an operating pressure of 1.0 kg/cm² to determine the manufacturing coefficient (Vm) and emitter flow variation (qvar(m)). The discharge exponent was found to be 0.46 for both emitter flow rates, with proportionality constants of 0.692 for 2 lph and 1.387 for 4 lph emitters respectively. The results showed a strong correlation between pressure and flow rate, with RMSE values of 0.51 and 0.34 lph, and coefficients of determination of 0.988 and 0.991 for 2 and 4 lph emitters, respectively. High manufacturing precision was indicated by low Vm values of 0.0491 for 2 lph and 0.055 for 4 lph emitters, while qvar(m) values were 0.261 for 2 lph and 0.283 for 4 lph emitters. Total coefficient of variation (Vq) values were 0.1 for 2 lph and 0.14 for 4 lph emitters, with total emitter flow variations (qvar) of 0.29 for 2 lph and 0.39 for 4 lph emitters. The study established the pressure-discharge curve for inline drip irrigation systems, emphasizing the critical relationship between pressure and flow rate. The derived chart from pressure discharge relationship is a valuable tool for estimating emitter flow variation due to hydraulic variation within the same subunit. Precise manufacturing and effective management of hydraulic variations are essential for ensuring uniform water distribution, optimizing drip irrigation systems, and ultimately enhancing crop yield and resource utilization.

Investigation of the Penetration Capability of Exponential Bullets into Two Typical TPMS Lattice Structures

The TPMS lattice structure is widely utilized in the military UAV, and effectively striking the TPMS structure holds significant reference value in bullet design. This paper conducts a numerical simulation to study the penetration performance of exponential curve bullets with varying power exponents on the TPMS structures of Gyroid and Diamond. MATLAB is employed to design the TPMS lattice structure, and ABAQUS is used for numerical simulation. The results indicate that the velocity reduction rate of the bullet initially increases and then decreases with an increase in power exponent. Moreover, a larger power exponent leads to a smaller residual velocity and kinetic energy of the bullet. Different cell structures have a substantial impact on residual kinetic energy, with the Gyroid structure exhibiting significantly greater residual kinetic energy compared to the Diamond structure, resulting in an average kinetic energy difference of 15.429[Formula: see text]J. The research shows that the curve of the arc part of the warhead has a great influence on the penetration performance. When designing the bullet used to hit the military UAV, the warhead with a large curvature change within a certain range can be considered.

Predicting the needs of people living with a disability using the two-level logit-skewed exponential power model

Predicting the needs of people living with a disability using the two-level logit-skewed exponential power model

The Afterlife of Energy

In the conclusion to her book The Birth of Energy: Fossil Fuels, Thermodynamics, and the Politics of Work, political scientist Cara Daggett considers “A Post-Work Energy Politics” in which she examines the historical coupling of energy and work—meaning human, waged work—in an invitation to disassociate their values and futures. The exponential power of fossil fuels animated the pipedream that powerful, inorganic slaves could substitute unfree human labor, ideas that have driven European imperialism. Fossil fuel systems did not lead, however, to a world beyond work. Rather, today’s “patriarchal slave states” continue to manage the project of putting the world to work through the maximization of productivity, and the subordination of racialized, immigrant, and gendered bodies—who would work for lower, or for no, wages. “The project of work,” Daggett argues, “is in tension with the project of life.” 1 And the rise of “work–life balance” is a mere tactic of governance in which the enemy is fatigue, exhaustion, and burn-out. She suggests, in turn, an alliance between post-carbon and feminist post-work politics and asks: what might it mean for energy politics to refer to the politics of ensuring public vitality? In order to advance a feminist revaluation of work, Daggett draws on Kathi Weeks’s The Problem with Work to outline a project that makes two utopian demands. One demand articulates a paradoxical relationship between the pragmatism of (present) demands and the speculative seeds of possibility; a second demand outlines a utopian form for such politics: partial, fragmented kin to the genre of the manifesto. Daggett concludes with an invitation that “a radical planet politics, if it seeks to contest ecomodernist claims, needs its own politics of pleasure.” 2 In an echo to Daggett’s invitation, the authors of this Educators’ Roundtable were invited to contribute a short text that picks up on the possibilities of a post-carbon, post-work politics.

Vibration shock disturbance modeling in the rotating machinery fault diagnosis: A generalized mixture Gaussian model

In real-world industrial environments, complex background noises are composed of various components, deviating from a simple Gaussian distribution like shock noise. In this work, a robust noise modeling method based on the mixture of exponential power (MoEP) distributions is developed to address this issue. To proficiently extract the fault characteristics, the signal’s 2-D representation is attained via Fast-SC, both of the fault features’ low-rankness and the complex noise are combined in a signal model. Then, a penalized function of the noise model is combined to further improve the performance of the method. The model is designated as the PMoEP enhanced low-rank model (PMoEP-LR). The Generalized Expectation–Maximization (GEM) algorithm is utilized to estimate the low-rank spectral correlation matrix and deduce all parameters of the PMoEP-LR model. Finally, the enhanced envelope spectrum (EES) is used to detect the defect characteristic. The efficacy of the proposed method is showcased by analyzing both simulated and real data.

Анализ на зависимостта между общия добив и напоителната норма при царевицата

The purpose of this work is to study the influence of reduced irrigation rates on the productivity of corn (FAO group 600) grown for grain in the Plovdiv region and to establish the parameters of the “Total yieldirrigation rate” relationship. The experiment was carried out in the period 2004-2018 in Agricultural University – Plovdiv, with the late hybrid “Knezha-613”. The variants of the experiment are: 1) without irrigation; 5) optimal irrigation (pre-irrigation soil moisture 75% of FC for the layer 0-80 cm); 2, 3, and 4) irrigation with respectively 25, 50 and 75 % of the rate for variant 5. The relationship is determined based on three types of formulas – regression equation, quadratic formula, and power formula. The highest accuracy is achieved by power relationship at power exponent n=1.3 and r=0.725. The quadratic formula is recommended when relative yield data are only under non-irrigated conditions. These two variants of relationship give a sufficiently accurate forecast for the yield when irrigation rates are reduced by up to 25%. The regression is also usable, but it is inferior to the power and quadratic variants.

Anisotropic tomography of eastern Tibet and its uncertainty from hypocentral errors

SUMMARY The mechanism responsible for the lateral expansion and uplift of the eastern Tibetan Plateau remains a topic of ongoing debate, partly due to discrepancies in the results of seismic velocity and anisotropy. In local earthquake tomography, hypocentral uncertainties can cause significant errors in the tomographic model. However, this issue has received limited attention in previous studies. In this work, we employ the weighted least-squares (WLS) method to solve the tomographic inversion problem. A power exponent coefficient, which is called weighting level, is introduced into the weighting matrix to control the relative contribution of the data with different hypocentral errors to the final tomographic result. Our data set contains high-quality Pg, Pn and Sg arrival times of local earthquakes recorded by the dense Chinese seismic network in eastern Tibet during 2008–2022. We comprehensively analyse the inversion results derived from the WLS inversions with different weighting levels to evaluate the robustness of isotropic velocity anomalies and azimuthal anisotropy. The most robust feature of our results is a striking low-velocity (low-Vp) zone surrounded by high-velocity (high-Vp) anomalies and fault parallel fast-velocity directions (FVDs) of azimuthal anisotropy in the lower crust beneath the western side of the Longmenshan fault zone. Taking into account many previous results of the region, we deem that the low-Vp zone reflects hot and wet upwelling flow from the deep asthenosphere, which ascends to the lower crust along the fault zone. At the NE margin of the Tibetan Plateau, significant low-Vp anomalies exist in the lower crust and the FVDs are consistent with the motion direction of the Tibetan block revealed by GPS (Global Positioning System) observations. We think that lower crustal flow exists beneath NE Tibet, which controls the plateau expansion toward the northeast. A low-Vp anomaly appears at 30 km depth beneath the Sichuan Basin. However, as the weighting level increases, the amplitude of this low-Vp anomaly decreases by more than 6 per cent, suggesting that this low-Vp anomaly has a lower accuracy than the other features.

Quantum model regression for generating fuzzy numbers in adiabatic quantum computing

This paper addresses the problem of inherent fuzziness in real-world data, arising from uncertainties, complexities, and limitations of traditional statistical methods. We introduce a pioneering method leveraging Adiabatic Quantum Computing (AQC), based on an adiabatic quantum regression model, to generate fuzzy numbers—ideal tools owing to their extension of real numbers and robust arithmetic properties. This innovative approach overcomes challenges in Quantum Machine Learning (QML) and limitations of Noisy Intermediate-Scale Quantum (NISQ) computers, providing a solution superior to conventional statistical methods and addressing the crucial issue of exponential power increase. Unique to this work, we offer rare closed-form expressions to produce fuzzy numbers through AQC and emphasise the integration of random variables and fuzzy numbers to encapsulate uncertainty fully. We propose a novel transformation of the Cumulative Distribution Function (CDF) into triangular and trapezoidal fuzzy numbers using AQC, enabling a comprehensive description of probability distributions of random variables. Experimental results are detailed, demonstrating the significant applicability and breakthroughs of this research in addressing data fuzziness.

Screening transparent model fluids to emulate the rheological properties of high solid anaerobic digestate of food waste

Due to the opacity of high solid anaerobic digestate, optical visualization techniques (e.g., particle image velocimetry) cannot be used directly for measuring the flow field inside high solid anaerobic digestion (HSAD) systems. This study mainly focused on selecting suitable surrogate fluids for optical visualization techniques by comparing the flow curves, thixotropic properties, viscoelastic behavior and creep recovery properties of HSAD digestate with a total solid from 10 % to 15 % (w/w) and transparent model fluids. Results indicated that the Bingham model outperformed the power-law model and Herschel-Bulkley model in describing the flow curves of HSAD digestate, xanthan gum and laponite. The digestate and xanthan gum had a similar thixotropic kinetic coefficient of 0.003 and 0.004, respectively. Besides, the digestate and xanthan gum had a similar modulus transformation trend with a power function whose power exponents were 0.17 and 0.19, respectively. In general, xanthan gum can be a suitable substitute for HSAD digestate with a total solid from 10 % to 15 %. The results can guide the implementation of optical visualization techniques to understand the flow pattern within HSAD systems, and improve the mass and heat transfer efficiency during food waste disposal processes.

THE STUDY OF DIGITAL TRANSFORMATION STRATEGIES: DRIVING BUSINESS AGILITY FOR COMPETITIVE EDGE

In technological banking (FinTech), there is a new player trying to drive innovation and the performance of the sector by disrupting the current practices and competitive environment on the world stage. The main aspect explored in this dissertation focuses on the strategies used by the leading FinTech firms to manage the dynamics of the space of digital transformation, with a special attention to the issue that aims to enhance business agility for long-term competitive advantage. This study, a detailed evaluation of either global or Indian FinTech ecosystem, observes the effects of digital transformation of organizational structures, processes and cultures and stresses the need of adaptability and resilience in present day dynamic business world. Employing both qualitative and quantitative analysis in carrying out case studies of known frontrunners, such as Stripe, Ant Group, PayPal, PhonePe, and Paytm, to determine their strategic responses to digital disruptions. The variance from past researches like academic literature and empirical research is also factored in to enhance this dissertation insights. A few major concepts, such as customer-centricity, compliance with regulations and technological innovation, are explored, which demonstrate how such different approaches to success are possible in the FinTech area. In addition, the study further analyses the struggles and barriers that limit the success of digital transformation initiatives in organizations, and the research provides some practical recommendations and frameworks that can leverage digital technologies effectively. The dissertation brings together theoretical frameworks with actual examples and empirical data to add to the literature and debate on digital transformation in the FinTech industry. It gives the principles and practice for professionals, decision-makers, and researchers to handle the challenges in digital economy more effectively; besides that, this contributes to the good innovation and sustainable development in global and Indian FinTech areas. Finally, this research reveals that there is an exponential power for digital transformation in reconstructing the financial services and leading to more inclusive future which is less prone to recession.

Model Selection for Exponential Power Mixture Regression Models.

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method.

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.

Damping accumulative NDAGM(1,N, α) power model and its applications

PurposeIn this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.Design/methodology/approachFirstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.FindingsBy altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.Practical implicationsThis paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.Originality/valueThe primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

Load-end shortening curve of stiffener elements with beam-column buckling considering steel grades based on empirical formulae of critical stress

The existing load-end shortening formulae with beam-column buckling in CSR-H were derived from the theoretical buckling formulae of stiffener elements considering the effective width of attached plate. However, due to different yielding stress and plastic flow ability, the critical stress of stiffener elements for elastic-plastic beam-column buckling may be relevant to steel grades. The critical stress of beam-column buckling was analysed by numerical method and compression test under supply supported constraints. Separate seven steel column samples from slender to sturdy were designed to be studied for various grades of steel mainly including 921A, S355 and Q235. After comparison, the empirical formulae of critical stress considering different yield stress were proposed in power exponent expression, and then employed to replace the theoretical buckling formulae in the load-end shortening curves in incremental-iterative method. This new load-end shortening expression of stiffener elements with beam-column buckling mode could more accurately evaluate the progressive collapse behaviours under longitudinal compression.

LES investigations of turbulent heat transfer structures with exponential power escalation

The BORAX-type accident is a key scenario in the safety design of pool-type experimental nuclear reactors. It considers a reactivity insertion large enough to initiate an exponential power escalation with a period as small as tens of milliseconds, necessitating effective heat removal by the coolant system. This study presents a computational analysis of single-phase transient heat transfer under thermal-hydraulic conditions relevant to such reactors, focusing on turbulent channel flow between planar fuel plates with highly subcooled water. Our investigation uses Large Eddy Simulation (LES) performed with TrioCFD, the open-source CFD code developed by the French Atomic Energy Commission (CEA). Several modeling options are tested: incompressible flow with temperature as a passive scalar, incompressible flow with varying viscosity, and quasicompressible flow with temperature-dependent viscosity and mass density. To evaluate the accuracy of our physical models, all simulations were performed under uniform conditions, using the same experimental parameters, mesh, and numerical strategies. The specific examined scenario involves a highly subcooled turbulent channel flow at moderate pressure, experiencing an exponential power increase. We ensure detailed turbulence resolution at the Batchelor scale in the wall normal direction close to the heating wall. This communication presents a comparative study of LES simulations of the single-phase flow under exponential power excursion at the wall. Subsequent analyses focused on the impact of different velocity-temperature couplings on the prediction of wall heat transfer, incorporating comparisons with experimental infrared thermometry measurements. Early in the transient phase, temperature peaks are confined within turbulent streaks, as reported in the existing literature. However, as the exponential power transient unfolds, the viscous layer and streak structures destabilize, leading to a more dispersed distribution of hot spots. Preliminary findings suggest a transition in heat transfer modes from turbulence-driven to conduction during the final stages of the single-phase transient.

Enhancing Performance of Power Allocation for VLC Networks by Non-Orthogonal Multiple Access-MIMO

Visible light communication, or VLC, networks emerged as a viable option for data access, particularly indoors. Their cost-effectiveness, protection to radio frequency (RF) intrusion, and extraordinarily high data speeds make them a desirable option for the upcoming generation of indoor networking technologies. In this paper, we propose the Exponential Gain Ratio Power Allocation (EGRPA), an effective and low-complex power splitting method, to increase the attainable sum amount in Multiple Input Multiple Output (MIMO) VLC downlink networks. Using numerical simulations, we assess the enactment of an indoor 2x2 MIMO VLC downlink model for several users. We also use Non-Orthogonal Multiple Access (NOMA) for enhancing spectrum efficacy. The usefulness and resilience of the suggested power splitting approach are established by the simulation outcomes, especially in scenarios with a high user count. In particular, the EGRPA power allocation algorithm shows a sum rate enhancement of more than 38.7%, 7.86%, and 7.04% for a system with three users when compared to the GRPA method, Normalized Gain Difference Power Allocation (NGDPA) method, and Logarithmic GRPA (NLGRPA) method, correspondingly.

Adaptive Quick Sliding Mode Reaching Law and Disturbance Observer for Robust PMSM Control Systems

The permanent magnet synchronous motor (PMSM) has been of interest to eco-friendly industries on account of its advantages such as high performance, efficiency, and precision control. However, perturbations due to PMSM parameter uncertainty, load torque, and external disturbance interfere with the construction of PMSM precision control systems. Therefore, a robust control system is needed to avoid unnecessary system movement caused by perturbations. In this paper, sliding mode control (SMC) is adopted to implement a robust control system for the PMSM. In order to reduce the reaching time from the initial system state to the sliding surface and the chattering phenomenon that can cause the system to malfunction, the adaptive quick sliding mode reaching law based on an exponential function and power equation is proposed. Although the SMC is robust to disturbance and parameter uncertainty, unexpected disturbances can destabilize the system. To estimate the unmatched disturbance in a short time, the second-order fast terminal sliding mode observer (SFTSMO) is proposed. The results show that the motor control system based on the proposed method has a fast convergence speed to an objective value, position tracking performance, and robustness.

Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight Interpolation

The inverse distance weighting (IDW) interpolation algorithm is used to reconstruct the γ radiation field. The traditional IDW interpolation algorithm is improved. The power exponent of distance P in the IDW for each interpolation point is not fixed and varies from one point to the other point. A fitting expression of P is obtained, which is a function of the coordinates of each point and can minimize the interpolation error when the number of sampling points is specified. Afterward, the improved algorithm is used to reconstruct a γ radiation field of a single source, and the theoretic results are compared with the results from Geant4, yielding an average relative error of 7.50%. The interpolated results from the experimental measurements align well with the actual data, with an average relative error of only 0.12%. The P derived from the interpolated experimental measurement data shows an error of 2.0% compared to the power exponent obtained from the Geant4 data interpolation. Then we set up a double-source γ radiation scene experiment and measured the count rate data at the grid points. At the same time, the experiment scene was simulated by Geant4. The improved IDW algorithm could not reconstruct the double-source γ radiation field well, thus further improvement is needed.

Future Prediction for Tax Complaints to Turkish Ombudsman by Models from Polynomial Regression and Parametric Distribution

The aim of this study is to forecast the amount of tax complaints filed with the Turkish Ombudsman in the future and whether or not policymakers require a specific tax Ombudsman. The polynomial regression for discrete data set is proposed to fit the number of events of tax complaints in the period from years $2013$ to $2021$. The artificial data set is generated by models which are polynomial regression and parametric distribution. The location, scale and shape parameters are determined according to the smallest value between the observed and predicted dependent variable. After determining the smallest value for the tried values of shape parameter and the parameters of polynomial regression, the best value determined by grid search for shape parameter is around $1.07$. Thus, the heavy-tailed from of exponential power distribution is gained. The artificial data sets are generated and sorted from the smallest to biggest ones. The maximum values are around $700$ and $800$ which can be regarded as future prediction because the distance among observations is taken into account by models from polynomial regression and parametric distribution. Since the polynomial regression and the parametric models are used simultaneously for modelling, the distance among observations can also be modelled by parametric model as an alternative approach provided.

Research on Insulation Configuration of 220 kV Bushing in High-Altitude Polluted Areas

The external insulation strength of the power equipment sleeve in the substation and converter station will be reduced with the increase in altitude and atmosphere pollution. The weakness of external insulation seriously threatens the safe operation of electrical equipment. At present, there are few studies and conclusions that focus on the flashover characteristics of power equipment bushing in high-altitude polluted areas. Therefore, this paper takes the 220 kV voltage level bushing as the research object and studies the Alternating Current (AC) pollution flashover characteristics and lightning and switching impulse flashover characteristics at low pressure. The insulation configuration at high-altitude polluted areas is analyzed. Finally, the insulation configuration scheme, which is suitable for high-altitude polluted areas, is proposed. The results show that the AC pollution flashover voltages of the bushing decrease with the decrease in the atmospheric pressure and the increase in the salt density as a power exponent function. The impulse flashover voltages also decrease with the decrease in the atmospheric pressure as a power exponent function. The atmospheric pressure/pollution impact characteristic index is related to the type of voltage, the value of the atmospheric pressure, and the value of the salt density. The test bushing does not meet the insulation configuration requirements in some high-altitude polluted areas. Through the analysis and calculation of the test results, the bushing insulation configuration scheme in high-altitude polluted areas is proposed. The research results can provide a reference for the external insulation design of bushings in substations and converter stations in high-altitude polluted areas.