A variable precision rough set approach for FMEA: integrating risk factor interdependencies with uncertainty and cognitive fusion

A multi-granularity decision tree algorithm based on variable precision rough sets and Zentropy

Optimal scale combination selection based on a monotonic variable precision multi-scale rough set model

Combined variable precision fuzzy rough set and its application in medical diagnosis

This paper proposes a variable selection method that combines instrumental variables and adaptive Elastic Net penalty for the spatial panel quantile autoregressive (SPQAR) model with fixed effects. This method can effectively identify key variables, estimate spatial effects, and address collinearity among variables while controlling for individual fixed effects. This paper gives the variable selection algorithm and establishes the large-sample properties of the penalized estimators. Numerical simulation results show that the adaptive Elastic Net method outperforms existing approaches in terms of estimation accuracy and variable selection precision, particularly under conditions of high collinearity and non-normal disturbances. Finally, this method is applied to analyze the impact of 13 explanatory variables on agricultural carbon emissions in China at different quantile levels. Both simulation and empirical results demonstrate the feasibility and effectiveness of the proposed method.

Efficient variable precision reduction in chaotic climate models: Analysis of the NEMO case in the destination earth project

Feature selection based on information entropy with variable precision fuzzy mixed granularity

Matrix-based approach for knowledge structure construction using variable precision models

Abstract In-memory computing (IMC) has emerged as a promising approach for accelerating deep neural network (DNN) inference by relocating computations to memory arrays. However, the efficacy of analog IMC diminishes when higher computational precision is required due to inherent device non-idealities. In this paper, we present a reconfigurable heterogeneous architecture that integrates a digital computing unit (DCU) with an analog IMC unit (AIMCU). The computational data is partitioned into most significant bits (MSBs) and least significant bits (LSBs); the sparse MSBs are processed by the DCU with lossless precision, and the dense LSBs are computed by the AIMCU for high energy efficiency, thereby enhancing inference accuracy and optimizing area efficiency. The architecture also features multiple modes that support variable-precision input splitting and weight splitting computation. Additionally, by leveraging hardware characteristics, we have developed several optimization strategies for neural network deployment, including parameter splitting, shifting algorithms, and sparse weight mapping. The experimental results show that the perceptual evaluation of speech quality (PESQ) of the deep complex convolution recurrent network (DCCRN) improved by 28.98%, while the peak signal-to-noise ratio (PSNR) of the super-resolution network (SRN) increased by 17.27%. Compared to previous state-of-the-art (SOTA) work, the reconfigurable heterogeneous-IMC-based system on a chip (SoC) demonstrates a significant improvement in energy efficiency while achieving accuracy close to that of pure digital computing.

This study investigates the computational mechanisms underlying visual working memory (VWM) deficits in schizophrenia (SZ) under distraction. Combining 60 SZ patients and 61 demographically matched healthy controls (HC), we employed a modified delayed-estimation task with varying set sizes (1/3) and distractor numbers (0/2). Results showed universally impaired VWM performance in SZ across conditions, though distraction did not disproportionately worsen their deficits. Using the variable precision model, we found that distractors significantly increased resource allocation variability (reflecting heterogeneity in attentional resource distribution) in HC, but not in SZ. This counterintuitive pattern suggests SZ patients’ VWM processes are less perturbed by external distractions, potentially linked to reduced flexibility in cognitive control. Our findings highlight the nonlinear interplay of multiple cognitive dysfunctions in SZ, where their combined effects exceed simple additive models, offering new insights into the mechanistic complexity of cognitive deficits in the disorder.

Abstract In recent years, the organic integration of soft sets and rough set prototypes has turned into a vital expansion direction. Among them, the correlative on variable precision soft rough sets has drawn extensive attention from scholars. In the actual process of data processing and decision analysis, some fusion prototypes are vulnerable to erroneously tagged data or only take relative errors into account. In order to make up for the deficiencies, this thesis presents a variable precision fuzzy soft rough set prototype anchored in absolute error. Besides presenting the definition of this prototype, we also conduct an in-depth exploration of its key computational properties. Finally, through a comparative analysis of the degenerated prototype, we conclude that the novel prototype predicated on absolute error put forth in this article is efficacious augmentation of the variable precision soft rough ensemble prototype.

Image Multi-Threshold Segmentation Based on Variable Precision Rough Set and K-L Roughness Particle Swarm Optimization

This study examined the computational cognitive mechanisms of visual working memory (VWM) in MDD, focusing on memory precision while exploring potential sex differences. 159 Major Depressive Disorder (MDD) patients and 67 healthy controls (HC) completed the color delay estimation task to measure their VWM. The mainstream models of VWM were compared, and the variable-precision (VP) model was the best fit for our data. The Bayesian ANCOVA was used to compare the differences between groups (MDD & HC) and sexes (male & female). Results revealed that MDD had worse memory precision than HC (BF10 = 103.872, decisive evidence for H1). Specifically, they had larger resource allocation variability (BF10 = 19.421, strong evidence for H1), indicating that they distributed memory resources more unevenly across different items than HC. In addition, females had better memory precision than males (BF10 = 10.548, strong evidence for H1). More specifically, they had more initial resources during the color delay estimation task (BF10 = 6.003, substantial evidence for H1) than males. These findings highlight the critical role of diminished precision, specifically, larger resource allocation variability, in impaired VWM in MDD. Meanwhile, these findings highlight sex differences in memory precision and initial resources of VWM.

A novel hybrid multi-criteria decision-making approach for solar photovoltaic power plant site selection based on the quantity and quality matching of resource-demand

Image thresholding segmentation method based on adaptive granulation and reciprocal rough entropy

Considering the characteristics of imprecise, incomplete and fuzzy data in emergency environment, a novel emergency decision-making method based on coverage-based variable precision (I, PSO)-fuzzy rough set model is proposed. First, an improved (I, PSO)-fuzzy rough set model is proposed, which combines the covering-based fuzzy rough set (CFRS) and the variable precision fuzzy rough set (VPFRS). Second, inspired by the idea of attribute reduction, a novel method for determining attribute weights is introduced to optimize weight assignment in emergency decision-making. Last but not least, to illustrate the feasibility and effectiveness of the proposed method, an example of post-flood rescue force allocation in urban areas is demonstrated. Finally, the stability and superiority of the method are verified through sensitivity analysis and comparative evaluation.

Application of a Novel Multi-granularity Variable Precision Fuzzy Rough Set in Attribute Reduction

An Improvement of β-Reduct and its Computation in Variable Precision Rough Set Model

Variable rate precision application of feedlot cattle manure mitigates soil greenhouse gas emissions

ABSTRACTLinear solvers and eigensolvers are the heart of high‐performance computing scientific applications. Among them, iterative projection methods are preferred to direct algorithms for large problems because of their lower memory usage, but they are prone to roundoff errors. Using an enhanced working precision inside the linear computing kernels mitigates this issue and accelerates convergence. Today, to go beyond 80 bits of precision, the only option is to use software libraries which are very slow. We introduce the variable and extended precision accelerator (VRP), a RISC‐V accelerator implemented on a system‐on‐chip (SoC) using GF22FDX technology. The VRP supports floating point computations with a range of significand bits from 2 to 512. This accelerator delivers an average 19.25 application speedup compared to the well‐known MPFR software library running on a 2400 MHz Intel Xeon processor. Additionally, extended precision facilitates the convergence of linear solvers for problems that would otherwise fail to converge and reduces energy‐to‐solution.