Variable Assignment Research Articles

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected. The dataset underwent thorough cleaning and variable assignment procedures. Subsequently, variables were selected using one-way logistic regression and lasso logistic regression methods, followed by multifactorial logistic regression to construct nomograms. The model's performance was assessed using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Statistical significance was set at P < 0.05. The predictive variables for ESRD development in SLE patients included anti-GP210 antibody presence, urinary occult blood, proteinuria, white blood cell count, complement 4 levels, uric acid, creatinine, total protein, globulin, glomerular filtration rate, pH, specific gravity, very low-density lipoprotein, homocysteine, apolipoprotein B, and absolute counts of cytotoxic T cells. The nomogram exhibited a broad predictive range. The ROC area under the curve (AUC) was 0.886 (0.858-0.913) for the training set and 0.840 (0.783-0.897) for the testing set, indicating good model performance. The model demonstrated both applicability and significant clinical benefits. The developed model presents strong predictive capabilities and considerable clinical utility in estimating the risk of ESRD in patients with SLE.

How Tarskian are Carnap's Semantics?

It is a commonplace of the history of analytic philosophy that Carnap swiftly adopted Tarskian semantics in the mid-1930s. There is no doubt that, in a very general sense, this is true. But to what extent are the innovative technical details characteristic of Tarski's method, specifically the handling of quantification by way of a satisfaction relation between formulas and variable assignments, reflected in Carnap's writings on semantics? Curiously enough, their essentials are in place just before Carnap took the purported Tarskian turn, namely in the definition of analyticity he formulated some time between September 1932 and April 1933, but are missing from his publications on semantics after 1935 until the final chapter of Meaning and Necessity of 1947, at which point we find Carnap reverting to the use of specifically Tarskian techniques in the context of his novel semantics for quantified modal languages.

Bayesian hidden Markov models for latent variable labeling assignments in conflict research: Application to the role ceasefires play in conflict dynamics

Bayesian hidden Markov models for latent variable labeling assignments in conflict research: Application to the role ceasefires play in conflict dynamics

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Application of an ensemble CatBoost model over complex dataset for vehicle classification.

The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis

AbstractApplying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making.

Fungal Mycobiome of Mature Strawberry Fruits (Fragaria x ananassa Variety 'Monterey') Suggests a Potential Market Site Contamination with Harmful Yeasts.

An amplicon metagenomic approach based on the ITS2 region of fungal rDNA was used to investigate the diversity of fungi associated with mature strawberries collected from a volcanic orchard and open-air market stands. Based on the Kruskal-Wallis test, no statistically significant differences were observed in both non-phylogenetic and phylogenetic alpha diversity indices. According to beta diversity analyses, significant differences in fungal communities were found between groups (orchard vs. market). Taxonomic assignment of amplicon sequence variables (ASVs) revealed 7 phyla and 31 classes. The prevalent fungal phyla were Basidiomycota (29.59-84.58%), Ascomycota (15.33-70.40%), and Fungi-phy-Insertae-sedis (0.45-2.89%). The most predominant classes among the groups were Saccharomycetes in the market group, and Microbotryomycetes and Tremellomycetes in the orchard group. Based on the analysis of microbiome composition (ANCOM), we found that the most differentially fungal genera were Hanseniaspora, Kurtzmaniella, and Phyllozyma. Endophytic yeasts Curvibasidium cygneicollum were prevalent in both groups, while Candida railenensis was detected in fruits originating only from the market. In addition, Rhodotorula graminis (relative abundance varying from 1.7% to 21.18%) and Papiliotrema flavescens (relative abundance varying from 1.58% to 16.55%) were detected in all samples regardless of origin, while Debaryomyces prosopidis was detected in samples from the market only, their relative abundance varying with the sample (from 0.80% to 19.23%). Their role in fruit quality and safety has not been yet documented. Moreover, several clinically related yeasts, such as Meyerozyma guilliermondii and Candida parapsilosis, were detected in samples only from the market. Understanding the variety and makeup of the mycobiome in ripe fruits during the transition from the orchard to the market is crucial for fruit safety after harvest.

The phase transition in random regular exact (3s + s, k)-SAT problem

A CNF formula with each clause of length k and each variable occurring 4s times, where positive occurrences are 3s and negative occurrences are s, is a regular (3s + s, k)-CNF formula (F3s+s,k formula). The random regular exact (3s + s, k)-SAT problem is whether there exists a set of Boolean variable assignments such that exactly one literal is true for each clause in the F3s+s,k formula. By introducing a random instance generation model, the satisfiability phase transition of the solution is analyzed by using the first moment method, the second moment method, and the small subgraph conditioning method, which gives the phase transition point s* of the random regular exact (3s + s, k)-SAT problem for k≥3. When s &lt; s*, F3s+s,k formula is satisfiable with high probability; when s &gt; s*, F3s+s,k formula is unsatisfiable with high probability. Finally, through the experimental verification, the results show that the theoretical proofs are consistent with the experimental results.

3D suitability evaluation of urban underground space using a variable weight method and considering ground restrictions

The evaluation of urban underground space (UUS) suitability involves multiple indicators. Assigning weight to these indicators is crucial for accurate assessment. This paper presents a method for spatially variable weight assignment of indicators using the order relation analysis method (G1-method), the entropy weight method, an improved grey relational analysis (GRA) and a set of spatial weight adjustment coefficients. First, the subjective and objective weights of indicators for engineering geological and hydrogeological conditions were determined by the G1-method and entropy weight method, respectively, and their combined weights were then obtained using the principle of minimum discriminatory information. This study highlighted the impact of surface restrictions, such as buildings, on UUS, and the degree of the influence of these buildings gradually decreased with the increase in depth of the rock and soil mass in UUS, which resulted in changes in weights of indicators with depth. To address this issue, a coefficient was defined as the standardized value of the ratio of additional stress applied by restrictions to the self-weight stress of soil at the same depth to modify the combined weights so that all weights of indicators could vary in space. Finally, an improved GRA was used to determine the suitability level of each evaluation cell using the maximum correlation criterion. This method was applied to the 3D suitability evaluation of UUS in Sanlong Bay, Foshan City, Guangdong Province, China, including 16 evaluation indexes. This study comprehensively considered the influence of multiple factors, thereby providing reference for evaluating the suitability of UUS in big cities.

Relations Between Rape Myths and Behavioral and Personality Characteristics in Males Convicted of Rape

To date the relationships between rape myths and other psychological constructs within males who have committed rape have not been explored sufficiently. Considered as a risk factor for the perpetration of rape it seems significant to examine their association to individual behavioral and personality characteristics more in detail. Therefore, we analyzed the relations between self-reported rape myth acceptance and the self-evaluation of aggressiveness, assertiveness, hypersexuality, social anxiety, sexual anxiety, SCID personality characteristics and the external assessment of psychopathy within a sample of N=569 males convicted of rape. The results showed significant correlations with all constructs except sexual anxiety, antisocial personality and psychopathy. Furthermore, findings indicated the assignment of the relevant variables to two principal components: rape myths, aggressiveness, hypersexuality, paranoid and narcissistic personality (K1) on the one hand and psychopathy and antisocial personality (K2) on the other hand. Following the results, rape myths are contextualized within the investigated psychological constructs and their potential differentiation from psychopathy and antisocial personality is discussed.

Partial plurality inferences of plural pronouns and dynamic pragmatic enrichment

I explore the semantics/pragmatics of plural pronouns by discussing the partial plurality inference that arises under quantificational subordination. I propose an anti-presupposition account coupled with Sudo’s (2023) dynamic implicature approach to plurality inferences based on plural information states, i.e. sets of variable assignments (van den Berg 1996). I further discuss the implications of the proposed analysis to the locality of anti-presupposition calculation and difference between animate instances and inanimate instances of plural pronouns in English.

Variable rate power-controlled batch-based channel assignment for enhanced throughput in cognitive radio networks

The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.

Amplitude amplification-inspired QAOA: improving the success probability for solving 3SAT

The Boolean satisfiability problem (SAT), in particular 3SAT with its bounded clause size, is a well-studied problem since a wide range of decision problems can be reduced to it. The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate for solving 3SAT for Noisy Intermediate-Scale Quantum devices in the near future due to its simple quantum ansatz. However, although QAOA generally exhibits a high approximation ratio, there are 3SAT problem instances where the algorithm’s success probability when obtaining a satisfying variable assignment from the approximated solution drops sharply compared to the approximation ratio. To address this problem, in this paper, we present variants of the algorithm that are inspired by the amplitude amplification algorithm to improve the success probability for 3SAT. For this, (i) three amplitude amplification-inspired QAOA variants are introduced and implemented, (ii) the variants are experimentally compared with a standard QAOA implementation, and (iii) the impact on the success probability and ansatz complexity is analyzed. The experiment results show that an improvement in the success probability can be achieved with only a moderate increase in circuit complexity.

Routine 4D Cardiac CT to Identify Concomitant Transthyretin Amyloid Cardiomyopathy in Older Adults with Severe Aortic Stenosis.

Background Transthyretin amyloid cardiomyopathy (ATTR-CM) often coexists with severe aortic stenosis (AS). Although strain analysis from cardiac MRI and echocardiography was demonstrated to predict coexisting ATTR-CM, comparable data from four-dimensional (4D) cardiac CT are lacking despite wide availability. Purpose To evaluate the diagnostic performance of 4D cardiac CT-derived parameters in identifying ATTR-CM in older adults considered for transcatheter aortic valve implantation (TAVI). Materials and Methods This prospective single-center screening study for ATTR-CM included consecutive patients with severe AS considered for TAVI who underwent 4D cardiac CT between August 2019 and August 2021 approximately 1 day before technetium 99m (99mTc) 3,3-diphosphono-1,2-propanodicarboxylic-acid (DPD) scintigraphy. The diagnostic performance of CT-based left ventricular (LV), right ventricular, and left atrial dimensions, ejection fraction (EF), and myocardial strain were evaluated against 99mTc-DPD scintigraphy as the reference standard to identify ATTR-CM. Predictors and an unweighted cardiac CT score were validated with internal bootstrapping. The assignment of variables to the score was based on cutoff values achieving the highest Youden index J. Results Among 263 participants (mean age, 83 years ± 4.6 [SD]; 149 male and 114 female participants), 99mTc-DPD scintigraphy (Perugini grade 2 or 3) confirmed coexisting ATTR-CM in 27 (10.3%). CT-derived LV mass index, LV and LA global longitudinal strain (GLS), and relative apical longitudinal strain each predicted the presence of ATTR-CM with an area under the curve (AUC) of at least 0.70. Implementing these parameters with cutoff values of 81 g/m2 or higher, -14.9% or higher, less than 11.5%, and 1.7 or higher in the CT score, respectively, yielded high diagnostic performance (AUC = 0.89; 95% CI: 0.81, 0.94; P < .001) robust to internal bootstrapping validation (AUC = 0.88; 95% CI: 0.82, 0.94). If two criteria were fulfilled, the sensitivity and specificity in the detection of ATTR-CM were 96.3% (95% CI: 81.0, 99.9) and 58.9% (95% CI: 52.3, 65.2), respectively. Conclusion When compared against 99mTc-DPD scintigraphy as the reference standard, routine 4D cardiac CT in older adults considered for TAVI provided high diagnostic performance in the detection of concomitant ATTR-CM by assessing LV and left atrial GLS, relative apical longitudinal strain, and LV mass index. ClinicalTrials.gov registration no.: NCT04061213 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tavakoli and Onder in this issue.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables.

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Integrated lithium niobate optical mode (de)interleaver based on an asymmetric Y-junction.

Lithium niobate on insulator (LNOI) platforms promise unique advantages in realizing high-speed, large-capacity, and large-scale photonic integrated circuits (PICs) by leveraging lithium niobate's attractive material properties, which include electro-optic and nonlinear optic properties, low material loss, and a wide transparency window. Optical mode interleavers can increase the functionality of future PICs in LNOI by enabling optical mode division multiplexing (MDM) systems, allowing variable mode assignment while maintaining high channel utilization and capacity. In this Letter, we experimentally demonstrate an optical mode interleaver based on an asymmetric Y-junction on the LNOI platform, which exhibits an insertion loss of below 0.46 dB and modal cross talk of below -13.0 dB over a wavelength range of 1500-1600 nm. The demonstrated mode interleaver will be an attractive circuit component in future high-speed and large-capacity PICs due to its simple structure, scalability, and capacity for efficient and flexible mode manipulation on the LNOI platform.

Improved bit-based filtering algorithm for regular constraint

In this paper, we propose an improved filtering algorithm for regular constraint, which demand that the sequence of all assignments for variables can be accepted by a Deterministic Finite Automaton (DFA) or a regular language. Regular constraint is a type of global constraint in Constraint Satisfaction Problems (CSPs) and a natural expression of deterministic patterns in planning and decision making, and it utilizes the properties of DFA to pre-specify the optional paths for multiple variables. Thus, it is sensible to improve the filtering algorithm that enforces Generalized Arc Consistency (GAC) for regular constraint in order to minimize memory and time consumption. With the goal in mind, we encode DFA using bit vectors and then devise a bit operations algorithm to maintain the domain consistency for regular constraint. Lastly, we carry out an experiment to compare to the state-of-the-art filtering algorithm. The experimental results demonstrates that the proposed algorithm significantly improves both memory and time.

Using Machine Learning Classifiers in SAT Branching [Extended Abstract

The Boolean Satisfiability Problem (SAT) can be framed as a binary classification task. Recently, numerous machine and deep learning techniques have been successfully deployed to predict whether a CNF has a solution. However, these approaches do not provide a variables assignment when the instance is satisfiable and have not been used as part of SAT solvers. In this work, we investigate the possibility of using a machine-learning SAT/UNSAT classifier to assign a truth value to a variable. A heuristic solver can be created by iteratively assigning one variable to the value that leads to higher predicted satisfiability. We test our approach with and without probing features and compare it to a heuristic assignment based on the variable's purity. We consider as objective the maximisation of the number of literals fixed before making the CNF unsatisfiable. The preliminary results show that this iterative procedure can consistently fix variables without compromising the formula's satisfiability, finding a complete assignment in almost all test instances.

Generalized Confidence Constraints

In robust optimization, finding a solution that solely respects the constraints is not enough. Usually, the uncertainty and unknown parameters of the model are represented by random variables. In such conditions, a good solution is a solution robust to most-likely assignments of these random variables. Recently, the Confidence constraint has been introduced by Mercier-Aubin et al. in order to enforce this type of robustness in constraint programming. Unfortunately, it is restricted to a conjunction of binary inequalities In this paper, we generalize the Confidence constraint to any constraint and propose an implementation based on Multi-valued Decision Diagrams (MDDs). The Confidence constraint is defined over a vector of random variables. For a given constraint C, and given a threshold, the Confidence constraint ensures that the probability for C to be satisfied by a sample of the random variables is greater than the threshold. We propose to use MDDs to represent the constraints on the random variables. MDDs are an efficient tool for representing combinatorial constraints, thanks to their exponential compression power. Here, both random and decision variables are stored in the MDD, and propagation rules are proposed for removing values of decision variables that cannot lead to robust solutions. Furthermore, for several constraints, we show that decision variables can be omitted from the MDD because lighter filtering algorithms are sufficient. This leads to gain an exponential factor in the MDD size. The experimental results obtained on a chemical deliveries problem in factories – where the chemicals consumption are uncertain – shows the efficiency of the proposed approach.

Feasibility Jump: an LP-free Lagrangian MIP heuristic

We present Feasibility Jump (FJ), a primal heuristic for mixed-integer linear programs (MIP) using stochastic guided local search over a Lagrangian relaxation. The method is incomplete: it does not necessarily produce solutions to all feasible problems, the solutions it produces are not in general optimal, and it cannot detect infeasibility. It does, however, very quickly produce feasible solutions to many hard MIP problem instances. Starting from any variable assignment, Feasibility Jump repeatedly selects a variable and sets its value to minimize a weighted sum of constraint violations. These weights (which correspond to the Lagrangian multipliers) are adjusted for constraints that remain violated in local minima. Contrary to many other primal heuristics, Feasibility Jump does not require a solution of the continuous relaxation, which can be time-consuming for some problems. We compare FJ against FICO Xpress Solver 8.14 and we show that this heuristic is effective on a range of problems from the MIPLIB 2017 benchmark set, significantly improving the average time to find a first feasible solution. We also show that providing these quick solutions to Xpress produces a modest reduction in the average time to optimality in the same benchmark set. Our entry based on FJ to the MIP 2022 Computational Competition (which challenged participants to write LP-free MIP heuristics) won 1st place. Moreover, an implementation of Feasibility Jump now runs by default on FICO Xpress Solver 9.0, where similar results to the ones presented here could be observed.