Parametric Approach Research Articles

Evaluating Producer Welfare Benefits of Whole-Farm Revenue Insurance

Agricultural insurance is by far the most popular risk management tool used in Iran. Despite many years of experience, Iran’s current insurance policy has not managed to protect all producers in the sector. The basic principle of whole-farm insurance consists of pooling all the insurable risks of a farm into a single policy and overcoming most of the major impediments to existing policies. This study aimed to evaluate the benefits of whole-farm insurance (WFI) in Zanjan province of Iran. This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. The premium rates were then calculated using the PQH simulation and Cholesky decomposition and compared under three scenarios: the single-crop, double-crop, and triple-crop options. Finally, farmers’ welfare benefits were compared under the three scenarios with the no-insurance case. The results demonstrate that WFI provides lower loss ratios compared to yield insurance and crop-specific insurance. Furthermore, producer welfare can be improved when they insure at least one crop compared to no-insurance. For example, the welfare benefits of insuring wheat, barley, alfalfa, wheat–barley, wheat–alfalfa, barley–alfalfa, and barley–alfalfa in terms of cost reduction to producers at 75% coverage are 8.8, 1.8, 2.9, 1.2, 0.9, and 1.8, respectively. Therefore, we recommend that the Iranian Agricultural Insurance Fund adopts WFI as a new risk management tool. This policy has the potential to decrease insurance premiums and administrative costs while improving the certainty equivalents and benefits to farmers through crop insurance.

Bayesian Approach to Equipartition Estimation of Magnetic Field Strength

Abstract Magnetic fields, together with cosmic rays (CRs), play an important role in the dynamics and evolution of galaxies, but are difficult to estimate. Energy equipartition between magnetic fields and CRs provides a convenient way to approximate magnetic field strength from radio observations. We present a new approach for calculating the equipartition magnetic field strength based on Bayesian methods. In this approach, the magnetic field is a random variable that is distributed according to a posterior distribution conditional on synchrotron emission and the size of the emitting region. It allows for the direct application of the general formulas for total and polarized synchrotron radiation without the need to invert these formulas, which has limited the equipartition method to highly simplified cases. We have derived the equipartition condition for the case of different low-energy breaks, slopes, and high-energy cutoffs of power-law spectra of the CR proton and electron distributions. The derived formalism was applied in the general case of a magnetic field consisting of both uniform and randomly oriented field components. The applied Bayesian approach naturally provides the uncertainties in the estimated magnetic field strengths resulting from the uncertainties in the observables and the assumed values of the unknown physical parameters. In the examples presented, we used two different Markov Chain Monte Carlo methods to generate the posterior distribution of the magnetic field. We have also developed a web application called BMAG that implements the described approach for different models and observational parameters of real sources.

Skillful subseasonal ensemble predictions of heat wave onsets through better representation of land surface uncertainties

Uncertainties in land surface processes notably limit subseasonal heat wave (HW) onset predictions. A better representation of the uncertainties in land surface processes using ensemble prediction methods may be an important way to improve HW onset predictions. However, generating ensemble members that adequately represent land surface process uncertainties, particularly those related to land surface parameters, remains challenging. In this study, a conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach was employed to generate ensemble members for representing the uncertainties in land surface processes resulting from parameters. Via six strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR), HW onset ensemble forecast experiments were conducted with the Weather Research and Forecasting (WRF) model. The performance of the CNOP-P approach and the traditional random parameter perturbation ensemble prediction method was evaluated. The results demonstrate that the deterministic and probabilistic skills of HW onset predictions show greater excellence using the CNOP-P approach, leading to much better predictions of extreme air temperatures than those using the traditional method. This occurred because the ensemble members generated by the CNOP-P method better represented the uncertainties in important land physical processes determining HW onsets over the MLYR, notably vegetation process uncertainties, whereas the ensemble members generated by the random parameter perturbation method could not. This finding suggests that the CNOP-P method is suitable for producing ensemble members that more appropriately represent model uncertainties through more reasonable parameter error characterization.

Automated Volumetric Milling Area Planning for Acoustic Neuroma Surgery via Evolutionary Multi-Objective Optimization

Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g., blood vessels and nerves) complicate this task. In order to determine the bone area to mill using preoperative CT images automatically, we propose an automated planning method using evolutionary multi-objective optimization for safer and more efficient milling plans. High-resolution segmentation of the adjacent risk structures is performed on preoperative CT images with a template-based approach. The maximum milling area is defined based on constraints from the risk structures and tool dimensions. Deformation fields are used to simplify the volumetric area into limited continuous parameters suitable for optimization. Finally, a multi-objective optimization algorithm is used to achieve a Pareto-optimal design. Compared with manual planning on six volumes, our method reduced the potential damage to the scala vestibuli by 29.8%, improved the milling boundary smoothness by 78.3%, and increased target accessibility by 26.4%. Assessment by surgeons confirmed the clinical feasibility of the generated plans. In summary, this study presents a parameterization approach to irregular volumetric regions, enabling automated milling area planning through optimization techniques that ensure safety and feasibility. This method is also adaptable to various volumetric planning scenarios.

CONSUMER PRICE INDEX MODELING USING A MIXED TRUNCATED SPLINE AND KERNEL SEMIPARAMETRIC REGRESSION APPROACH

Some semiparametric regression model approaches include spline, kernel, Fourier series, and wavelet. Semiparametric regression modelling can involve more than one independent variable (multivariable), a parametric approach is usually combined with one of the nonparametric approaches, such as combining a parametric approach with a nonparametric kernel. If a consumer price index model can be built based on the variables that influence it, predictions of consumer price percentages can be made, which it is hoped will help the government determine policies to control consumer price inflation, especially in NTB Province. The data used in this research includes the consumer price index and the factors that influence it according to districts/cities in NTB Province from 2022 to April 2024. The data source was obtained from secondary data at BPS NTB Province. This research design uses a mixed semiparametric approach of truncated spline and kernel regression. Based on calculations, the predicted results of the consumer price index in NTB Province show that the predicted data graph is very close to the actual data . Modelling the consumer price index in NTB Province is a model with 2 knot points, where the model efficiency has the smallest GCV value of 0.001507. The model goodness value is 0.99, meaning that the variables used can explain 99% of the model variability.

A parametric approach of computing Taguchi signal-to-noise ratio from incomplete life data

A parametric approach of computing Taguchi signal-to-noise ratio from incomplete life data

OPTIMIZING HEATED AIR CIRCULATION IN SILO/BIN DESIGN: A PARAMETRIC APPROACH

Aeration is one of the most essential processes after harvest in guaranteeing food security since it is often employed for grain cooling while the physical qualities of the stored grains are sustained. Aiming to address issues of storage inefficiencies, the research reviewed “Optimization of Heated Air Circulation in Silo/Bin Design: A Parametric Approach.” The method adopted for the study was systematic review of journal articles, conference proceedings, books and other relevant materials. Furthermore, the main method used to find relevant publications for the study was keyword searches in electronic databases such as Research Gate, Web of Science, Google Scholar and Scopus. Hence, the study ascertained that at an optimal temperature range between 10 - 15°C, grain storage facilities can effectively mitigate the risks of moisture accumulation, insect infestation, and microbial growth. More so, the study revealed that the rate of airflow ranging from 0.05 to 0.1 m3 min-1 t-1 are usually utilized in tall steel bins and concrete silos containing small grains, again measurements ranges from 0.1 to 0.2 meter cubed per minute per tonne were suggested for horizontal storage for huge grains. However, the study suggests that SSR model seems to be the most suitable model for optimization of heated air circulation in silo/bin among all the reviewed models because it is capable of describing nearly all the systems’ dynamics.

Innovative architectural design practices enabled by AI-powered parametric and computational approaches

The intersection of artificial intelligence and architectural design represents a significant technological advancement that fundamentally reshapes spatial creation and design methodologies. This research explores how computational approaches, specifically machine learning and parametric design algorithms, are transforming architectural practice by introducing novel capabilities for spatial exploration, performance optimization, and generative design. Artificial intelligence emerges as a collaborative tool that expands architectural design possibilities. Advanced neural networks and generative design algorithms enable computational systems to analyze extensive datasets, generate complex design iterations, and optimize architectural solutions across multiple dimensions of structural integrity, environmental performance, and contextual responsiveness. Empirical evidence from case studies of sustainable urban housing in Singapore and adaptive architectural systems in Madrid illustrates how computational technologies can produce design solutions that surpass traditional methodologies, addressing critical challenges in environmental sustainability and urban resilience. The research critically examines the technological capabilities and limitations of AI in architectural design. It highlights the potential for algorithmic bias, challenges in capturing phenomenological experiences, and ethical considerations surrounding creative agency. By emphasizing the need for interdisciplinary collaboration and robust ethical frameworks, the study provides a nuanced approach that balances human intuition with technological innovation.

Dynamic modeling and analysis of wind turbine gear-bearing coupling system considering tooth root crack and slicing coupling effect

Tooth root cracks would change the meshing stiffness excitation of the gears, increasing the vibration and noise of wind turbine gearbox. Currently, there is relatively little literature that focuses on the slicing coupling effect of cracked gears, leading to incomplete accuracy in solving the time-varying mesh stiffness (TVMS) of gears under cracked conditions, making it difficult to accurately reflect the impact of root crack excitations on the dynamic behavior of wind turbine gear-bearing coupled systems. In this work, The improved analytical calculation method is proposed for the TVMS and system vibration coupling solution of the cracked gear pair, considering the slicing coupling effect. The dynamic model of the wind turbine high-speed stage gear set is developed using the lumped parameter approach, and the rolling bearing is modeled using the Hertz contact theory. Then, the excitations of the gear-bearing coupling system are developed using an improved slicing method. Lastly, the dynamic model of the wind turbine gear-bearing coupling system that considers tooth root cracks and the slicing coupling effect is established and validated. The results indicate that The TVMS modification algorithm considering the slicing coupling effect can better depict the dynamic change of the gear meshing stiffness under the crack failure. Cracks could reduce the TVMS of the gear pair, neglecting the coupling effect between the sliced gear tooth gains the potential risks of overestimating the influence of the gear crack depth on the TVMS. In addition, Gear cracks excitation may alter the dynamic contact load amplitude of the bearing rolling elements, resulting in a tendency for the load-bearing area to decrease. Moreover, When the cracked teeth are involved in meshing, The decrease in mesh stiffness will result in a larger amplitude periodic shock response to the system vibration displacement and a sideband phenomenon near the mesh frequency, neglecting the coupling effect between the sliced gear tooth or simplifying the bearing to a linear support stiffness matrix gain the potential risks of underestimating the influence of the gear crack depth on the vibration displacement of the gear-bearing coupling system.

Modeling product degradation with heterogeneity: A general random-effects Wiener process approach

Degradation of many products in practical applications is often subject to unit-to-unit heterogeneity. Such heterogeneity can be attributed to the heterogeneous quality of the raw materials and the fluctuation of the manufacturing process, as well as the heterogeneous usage conditions and environments. The heterogeneity leads to the scattering of the degradation rates and diffusion intensities in the population. To model this phenomenon, this study proposes a general random-effects Wiener process model that accounts for the unit-to-unit heterogeneity in the degradation drift and the volatility simultaneously. In particular, the drift of the Wiener process is characterized by a normal distribution and the diffusion parameter is characterized by an independent inverse Gaussian distribution. The proposed model is flexible for characterization of heterogeneous degradation, and permits an analytically tractable model inference. An EM algorithm incorporating the variational Bayesian method is developed to estimate the model parameters, and a parametric bootstrap approach is proposed to construct confidence intervals. The performance of the proposed model and the estimation algorithm is validated by Monte Carlo simulations. The degradation data of an infrared LED device and the wear data of the magnetic head of a hard disk drive are studied based on the proposed model. With comprehensive comparative studies, the good performance of the proposed model in fitting the real degradation data is validated.

Disk Kinematics at High Redshift: DysmalPy’s Extension to 3D Modeling and Comparison with Different Approaches

Spatially resolved emission-line kinematics are invaluable for investigating fundamental galaxy properties and have become increasingly accessible for galaxies at z ≳0.5 through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high z present challenges due to the lower signal-to-noise ratio (S/N) and resolution compared to the data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers the intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of z ∼ 1−3 star-forming galaxies. We also compare DysmalPy’s recovery performance to that of two other commonly used codes, GalPak 3D and 3D Barolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the nonparametric tool can lead to differences of up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of (i) the choice of priors (parametric approaches); and (ii) the masking (all approaches); and (iii), more generally, the evaluating of the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy.

The role of bank diversity in the banking development-income inequality nexus. Regional evidence from Italy

Purpose The function of banking development in reducing income inequality is critical because financial institutions can grant loans, stimulating prospective productive investments. Based on this promise, the aim of this study is to fill the vacuum by particularly evaluating the influence of banking development, as proxied by bank cost efficiency estimated using a parametric approach, on income inequality. Design/methodology/approach To evaluate the impact of banking development on income inequality, the authors use data from 20 Italian regions from 2004 to 2017. Particular attention will be made to the consequences that the varied composition of the Italian banking structure, namely, the presence of cooperative and non-cooperative banks, may have on income inequality. To do this, the authors use a generalized method of moments (GMM) regression on panel data to address the endogeneity problem that exists between banking development and income inequality. Findings Evidence reveals that increasing bank development plays an important impact in reducing income inequality, with cooperative banks faring best. A set of robustness tests generally validates our empirical findings and brings relevant policy implications. Originality/value A “qualitative” measure, such as cost efficiency, which is computed using a parametric technique, has been used as a proxy for banking development to analyse the relationship between banking development and income inequality. The contribution, in particular, focuses on how bank diversity influences the nexus between banking development and income inequality in a homogenous context.

Socio-economic evaluation and technical efficiency of soybean (Glycine max L. Merrill) production in North-West Nigeria: A parametric approach

This aim of study examined socioeconomic evaluation and technical efficiency (TE) of soybean (Glycine max) production with a parametric approach in North West, Nigeria. The multistage sampling method was used to select 160 soybean farmers. Primary data were used. Data were analyzed using descriptive statistics and the stochastic production efficiency frontier model (SPEFM). The result shows that the mean age of a soybean farmer was 46 years. Soybean production was profitable with an estimated Gross Margin and Net Farm Income of 899,237.80 and 824, 468.22 Naira respectively. The Gross Margin Ratio and Rate of Return on Investment was calculated at 0.583 and 1.15 respectively. The significant socio-economic factors that increase the TE of soybean production include household size, age, years of experience, level of education, members of cooperatives, and number of extension contacts. The mean TE scores of soybean farmers was 53.77%, leaving a gap of 46.23% for improvement. This study recommends that credit with single-digit interest rates should be made available to soybean farmers.

Model of interaction between laser radiation and metal powder composition during direct laser growth

This paper presents a model for analyzing the interaction of laser radiation and a metal-powder composition in the process of direct laser growing of large-sized combustion chambers of gas turbine engines. The metal-powder composition is fed into the melting zone coaxially with laser radiation; the task is to completely melt the powder with laser radiation before it enters the melt bath on the construction platform. The laser radiation is absorbed as it passes through the gas-powder jet, and its energy is also used to melt the construction platform or the previous layer. Thus, in order to determine the parameters of the operating conditions that provide the possibility of melting powder particles, it is necessary to determine the boundaries of the parameters at which each particle of the metal-powder composition completely melts in a gas-powder jet. To simulate heat transfer inside a particle, the Beer – Lambert laser radiation absorption law was used using the lumped parameter approach. The required energy for melting the powder material was determined through enthalpy. The resulting one-dimensional differential equation of enthalpy increment is solved numerically by the Euler method. Using this model, the distance from the point of origin of the interaction of the laser beam with a metal-powder composition to the zone of its complete melting was determined and the effect of the velocity of the gas-powder jet, the power of laser radiation, the bulk density of the metal-powder composition and the average radius of the powder particles on the distance to the zone of complete melting was studied.

Assessment of Land Productivity Index Based on Parametric Approach Using GIS and Remote Sensing in Nubian Nasr Area, Aswan, Egypt

Assessment of Land Productivity Index Based on Parametric Approach Using GIS and Remote Sensing in Nubian Nasr Area, Aswan, Egypt

A Bayesian latent class approach to causal inference with longitudinal data.

Bayesian methods are becoming increasingly in demand in clinical and public health comparative effectiveness research. Limited literature has explored parametric Bayesian causal approaches to handle time-dependent treatment and time-dependent covariates. In this article, building on to the work on Bayesian g-computation, we propose a fully Bayesian causal approach, implemented using latent confounder classes which represent the patient's disease and health status. Our setting is suitable when the latent class represents a true disease state that the physician is able to infer without misclassification based on manifest variables. We consider a causal effect that is confounded by the visit-specific latent class in a longitudinal setting and formulate the joint likelihood of the treatment, outcome and latent class models conditionally on the class indicators. The proposed causal structure with latent classes features dimension reduction of time-dependent confounders. We examine the performance of the proposed method using simulation studies and compare the proposed method to other causal methods for longitudinal data with time-dependent treatment and time-dependent confounding. Our approach is illustrated through a study of the effectiveness of intravenous immunoglobulin in treating newly diagnosed juvenile dermatomyositis.

The Application of Grasshopper to Redesign Ship Hulls with a Parametric Approach Method Based on Delftship Reference

The Application of Grasshopper to Redesign Ship Hulls with a Parametric Approach Method Based on Delftship Reference

Flexible Spline Models for Blinded Sample Size Reestimation in Event-Driven Clinical Trials.

In event-driven trials, the target power under a certain treatment effect is maintained as long as the required number of events is obtained. The misspecification of the survival function in the planning phase does not result in a loss of power. However, the trial might take longer than planned if the event rate is lower than assumed. Blinded sample size reestimation (BSSR) uses non-comparative interim data to adjust the sample size if some planning assumptions are wrong. In the setting of an event-driven trial, the sample size may be adjusted to maintain the chances to obtain the required number of events within the planned time frame. For the purpose of BSSR, the survival function is estimated based on the interim data and often needs to be extrapolated. The current practice is to fit standard parametric models, which may however not always be suitable. Here we propose a flexible spline-based BSSR method. Specifically, we propose to carry out the extrapolation based on the Royston-Parmar spline model. To compare the proposed procedure with parametric approaches, we carried out a simulation study. Although parametric approaches might seriously over- or underestimate the expected number of events, the proposed flexible approach avoided such undesirable behavior. This is also observed in an application to a secondary progressive multiple sclerosis trial. Overall, if planning assumptions are wrong this more robust flexible BSSR method could help event-driven designs to more accurately adjust recruitment numbers and to finish on time.

Design and Evaluation of 3D-Printed Lattice Structures as High Flow Rate Aerosol Filters.

Aerosol contamination presents significant challenges across various industries, ranging from healthcare to manufacturing. Over the past few years, open foam filters have gained prominence for their ability to efficiently capture particles while allowing reasonable airflow. In this work, we present the use of 3D-printed idealized open foam-like lattice structures as aerosol filtration media, leveraging advances in additive manufacturing to generate these highly tunable and modular filters. Using parametric design approaches, we fabricated lattice filters with four different unit cell geometries (Cubic, Kelvin, Octahedron, and Weaire-Phelan) via Digital Light Synthesis 3D printing and characterized these structures with X-ray microcomputed tomography. We compared the aerosol filtration performance of the different lattice unit cell geometries using 1 μm polystyrene latex (PSL) aerosol particles, finding the filtration performance to be positively correlated with the single-unit-cell specific surface area. We then expanded our evaluation of deposition efficiency in Kelvin cell lattice structures of varied porosities, again finding a correlation between the specific surface area and deposition performance. Experimental analysis confirmed that deposition primarily occurs through impaction and electrostatic mechanisms within the parameter space. Overall, our findings demonstrate that unit-cell-based lattices can achieve a wide range of aerosol filtration efficiencies (∼10-100%) across various operating conditions (1-4 m/s superficial velocity), offering a highly tunable in-line filtration medium capable of maintaining high efficiency even at elevated airflow rates. This work not only provides essential guidelines for designing and manufacturing 3D-printed lattices as customizable aerosol filters but also highlights the current limitations and challenges in producing these structures.

Modelling and Optimization of Banana/Plantain Fiber Extraction Systems through Dimensional Analysis

This study presents a comprehensive parametric modelling approach for a locally produced plantain and banana fiber extractor, utilizing the Buckingham Pi theorem to analyze the complex interactions among various operational parameters. Plantain and banana fibers are increasingly recognized for their diverse industrial applications, including textiles, paper, packaging, and food supplements. The mechanical extraction of these fibers is prevalent, necessitating a well-designed process that conserves the natural properties of the fibers while maximizing cost-effectiveness. This research aims to optimize the design and operational parameters of the extractor to enhance its extraction capacity and overall efficiency. Employing the Buckingham Pi theorem, the study constructs dimensionless Pi terms from key parameters impacting the extractor's performance. The analysis reveals a coefficient of correlation (R²) of 0.9923 for extraction efficiency and 0.9867 for extraction capacity, indicating a strong predictive capability of the developed model. These findings provide valuable insights into optimizing the performance of the plantain and banana fiber extractor, ultimately contributing to more efficient and sustainable fiber extraction processes. This model serves as a critical tool for designers and manufacturers aiming to improve the economic viability of fiber extraction systems.