Number Of Variables Research Articles

EMSA-IK: a real-time evolutionary multi-objective semi-analytical inverse kinematics algorithm for redundant manipulators

Purpose This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators. Design/methodology/approach Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints. Findings Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application. Originality/value The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

Machine learning for predicting device-associated infection and 30-day survival outcomes after invasive device procedure in intensive care unit patients

This study aimed to preliminarily develop machine learning (ML) models capable of predicting the risk of device-associated infection and 30-day outcomes following invasive device procedures in intensive care unit (ICU) patients. The study utilized data from 8574 ICU patients who underwent invasive procedures, sourced from the Medical Information Mart for Intensive Care (MIMIC)-IV version 2.2 database. Patients were allocated into training and validation datasets in a 7:3 ratio. Seven ML models were employed for predicting device-associated infections, while five models were used for predicting 30-day survival outcomes. Model performance was primarily evaluated using the receiver operating characteristic (ROC) curve for infection prediction and the survival model’s concordance index (C-index). Top-performing models progressively reduced the number of variables based on their importance, thereby optimizing practical utility. The inclusion of all variables demonstrated that extreme gradient boosting (XGBoost) and extra survival trees (EST) models yielded superior discriminatory performance. Notably, when restricted to the top 10 variables, both models maintained performance levels comparable to when all variables were included. In the validation cohort, the XGBoost model, with the top 10 variables, achieved an area under the curve (AUC) of 0.810 (95% CI 0.808–0.812), an area under the precision-recall curve (AUPRC) of 0.226 (95% CI 0.222–0.230), and a Brier score (BS) of 0.053 (95% CI 0.053–0.054). The EST model, with the top 10 variables, reported a C-index of 0.756 (95% CI 0.754–0.757), a time-dependent AUC of 0.759 (95% CI 0.763–0.775), and an integrated Brier score (IBS) of 0.087 (95% CI 0.087–0.087). Both models are accessible via a web application. The internally evaluated XGBoost and EST models demonstrated exceptional predictive accuracy for device-associated infection risks and 30-day survival outcomes post-invasive procedures in ICU patients. Further validation is required to confirm the clinical utility of these two models in future studies.

On nonnegative invariant quartics in type A

The equivariant nonnegativity versus sums of squares question has been solved for any infinite series of essential reflection groups but type A. As a first step to a classification, we analyse An-invariant quartics. We prove that the cones of invariant sums of squares and nonnegative forms are equal if and only if the number of variables is at most 3 or odd.

Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization

Surrogate-based optimization (SBO) approach is becoming more and more popular in the expensive aerodynamic design of aircraft. However, with increasing number of design variables required for parameterizing a complex shape, SBO is suffering from the serious difficulty of the curse of dimensionality. To ameliorate this issue, a supervised nonlinear dimensionality-reduction surrogate modelling method was proposed. Such a method combines the supervised kernel principal component analysis (SKPCA) and polynomial chaos-Kriging (PCK) techniques into the jointly surrogate modelling process and adaptively establishes the accurate mapping from the high-dimensional inputs to the output of the system. This SKPCA-PCK method, which fully considers the effect of inputs on outputs and adaptively trains these hyper-parameters in the surrogate modelling process, escapes from the low prediction accuracy and instability of the surrogate model in conjunction with current linear or unsupervised dimensionality-reduction methods. Further, an efficient SKPCA-PCK-based global optimization method for high-dimensional aerodynamic design was developed. The performance of the proposed method is examined by investigating two numerical examples, the transonic RAE2822 airfoil and the wing of the NASA Common Research Model. Results demonstrate that the proposed SKPCA-PCK method significantly improves the modelling efficiency and accuracy compared to the unsupervised linear PCA-Kriging method. More importantly, the proposed SKPCA-PCK-based optimization method provides better performance and an appreciably higher optimization efficiency for expensive single-point and robust aerodynamic design involving high-dimensional design variables compared to the Kriging-based optimization method. These results provide further evidence that the proposed method provides a promising approach for mitigating the curse of dimensionality in SBO.

Demonstration of potential DNA contamination introduced by laboratory consumables using Fluorescein

The development of increasingly efficient DNA extraction and profiling kits has increased the amount of allelic information obtained from trace DNA samples, but also inadvertently, increased the detection of DNA contamination. This study aimed to evaluate the potential of DNA transfer using fluorescein, fluorescent under an alternate light source, in the use of a range of forensically relevant DNA profiling consumables. An evaluation of two pre-lysis methods adopting three different sample tubes, some with deliberate seal damage, showed the PrepFiler™ Automated Forensic DNA Extraction Kit caused leakage and crusting when the rim of the PrepFiler™ LySep column was compromised, but no leakage was observed under the same conditions using the Investigator STAR Lyse&Prep kit. The AutoLys tube showed minimal leakage using the PrepFiler™ chemistry. A DNA extract tube with an external thread, similar to the AutoLys tube, showed no leakage after fridge or freezer storage. However, it highlighted that a centrifugal spin does not guarantee all the DNA will pool at the base of the tube. A comparison of adhesive plate sealing films to 8-well strip caps for sealing 96-well PCR plates showed the adhesive plate sealing films presented a lower risk of DNA transfer, largely due to the adhesion of dispersed liquid on the sticky surface of the film. Overall, this study highlighted a number of variables that may be considered in the development of more refined contamination minimisation protocols in respect to increased sensitivities of DNA profiling.

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer

Mathematical models of C-type and N-type inactivating heteromeric voltage gated potassium channels.

Voltage gated potassium channels can be composed of either four identical, or different, pore-forming protein subunits. While the voltage gated channels with identical subunits have been extensively studied both physiologically and mathematically, those with multiple subunit types, termed heteromeric channels, have not been. Here we construct, and explore the predictive outputs of, mechanistic models for heteromeric voltage gated potassium channels that possess either N-type or C-type inactivation kinetics. For both types of inactivation, we first build Markov models of four identical pore-forming inactivating subunits. Combining this with previous results regarding non-inactivating heteromeric channels, we are able to define models for heteromeric channels containing both non-inactivating and inactivating subunits of any ratio. We simulate each model through three unique voltage clamp protocols to identify steady state properties. In doing so, we generate predictions about the impact of adding additional inactivating subunits on a total channel's kinetics. We show that while N-type inactivating subunits appear to have a non-linear impact on the level of inactivation the channel experiences, the effect of C-type inactivating subunits is almost linear. Finally, to combat the computational issues of working with a large number of state variables we define model reductions for both types of heteromeric channels. For the N-type heteromers we derive a quasi-steady-state approximation and indicate where the approximation is appropriate. With the C-type heteromers we are able to write an explicit model reduction bringing models of greater than 10 dimensions down to 2.

Rational Expectations Models with Higher-Order Beliefs

Abstract We develop a method of solving rational expectations models with dispersed information and dynamic strategic complementarities. In these types of models, the equilibrium outcome hinges on an infinite number of higher-order expectations which require an increasing number of state variables to keep track of. Despite this complication, we prove that the equilibrium outcome always admits a finite-state representation when the signals follow finite ARMA processes. We also show that such a finite-state result may not hold with endogenous information aggregation. We further illustrate how to use the method to derive comparative statics, characterize equilibrium outcomes in HANK-type network games, reconcile with empirical evidence on expectations, and integrate incomplete information with bounded rationality in general equilibrium.

THE EFFECT OF SERVICE QUALITY AND PRICE ON PURCHASE DECISIONS AT D'CREPES PONDOK INDAH MALL SOUTH JAKARTA

This study aims to analyze the effect of service quality and price on consumer purchase decisions at D'Crepes Pondok Indah Mall, South Jakarta. The research employs a quantitative method with an associative approach, where service quality and price serve as independent variables, and purchase decisions act as the dependent variable. Data was collected through questionnaires distributed to 100 respondents. The findings indicate that both service quality and price significantly influence consumer purchase decisions, both partially and simultaneously. Simple linear regression analysis reveals that service quality has a strong impact on purchase decisions, while price also exerts a considerable influence. Additionally, multiple linear regression confirms that service quality and price together have a very strong correlation with purchase decisions, accounting for 76.4% of the variation in consumer behavior. This research emphasizes the importance of providing high-quality service and setting appropriate prices to improve consumer purchase decisions. However, the study acknowledges several limitations, including the restricted number of variables analyzed and the sole use of questionnaires for data collection. Further studies are recommended to explore additional factors such as location, promotion, and brand image, and to expand the sample size for broader insights.

Productivity in the Insurance Company: A Meta-Regression Analysis

Purpose –This study attempts to examine the studies using Malmquist Productivity Index (MPI) in calculating the productivity of insurance sector and to identify the factors influencing their total factor productivity change through meta-regression analysis. Design/Methodology/Approach –This study employed 29 empirical articles which were published between 1977-2023 and which met the inclusion criteria. Findings –The results on the meta-regression analysis revealed that the publication year of the studies and the income groups of the countries did not have an impact on the total factor productivity. The number of observations, number of variables, and branch of theinsurance company were statistically significant on the estimation of total factor productivity change.Discussion –In this study, a meta-regression analysis systematically highlights the effects of methodological assumptions on the productivity of insurance companies and synthesize empirical findings. The study serves to make the literature more accessible to researchers who will use the MPI method for measuring the productivity of insurance companies. This study is expected to guide managers in enhancing the productivity of insurance companies, considering national requirements, making and implementing legal regulations, and identifying policies

Reliability of a new stabilization device for measurement of muscle strength using a hand-held dynamometer.

The hand-held dynamometer (HHD) is a reliable and portable tool for quantitatively assessing muscle strength. However, a number of variables, including the strength of the tester, the adequacy of stabilization, and the selected testing position, can affect the reliability of the device. The objective of the present study was to examine the reproducibility and inter-rater reliability of strength assessments using HHD with and without a stabilization device. Strength assessments were conducted with and without the stabilization device by two independent raters. Healthy volunteers with no history of musculoskeletal disorders were included in the study. The strength of the shoulder flexion, scapula elevation, knee extension, and hip abduction muscle groups was evaluated using HHD. The reliability of the measurements was evaluated using intra-class correlation (ICC), standard error of measurement, and minimal detectable change. Fifty-two healthy volunteers (age 21.51 ± 2.02 years) participated in the study. The reproducibility was found to be excellent (ICC = 0.89-0.99). While reliability between the assessors was good to excellent (ICC = 0.85-0.93), reliability between assessors and device was found moderate to good (ICC = 0.48-0.78). The strength values obtained for all directions of movement with the stabilization device were found to be significantly higher than those obtained without fixation of the HHD. It can be concluded that the data obtained from the assessors and HHD with stabilization device are not comparable. Moreover, the utilization of a stabilization device may serve to enhance the reliability of the measurements by eliminating the rater effect.

Investigation the yarn to yarn static and kinetic friction based on the response surface method

One of the most significant physical characteristics of textiles is friction, which has a direct bearing on how they behave mechanically. This characteristic has been researched in a variety of methods thus far, demonstrating its significance. The static and kinetic coefficients of friction in yarn to yarn interaction have been examined in this paper by considering a number of variables, including yarn count (33, 50, 57, and 65 Tex), yarn movement speed (30, 45, and 60 mm/min), angle of yarn placement (0, 45, and 90 degrees), and surface reaction force (5, 10 and 15 g). To this aim, 32 different experiments were designed using the central composite design approach in order find the static and kinetic coefficients of friction. The outcomes were then statistically analyzed. The relationship between the friction coefficients and the aforementioned parameters was investigated as well. Next, a model with 93% and 85% accuracy for the static and kinetic coefficients of friction, respectively, was obtained using the response surface method. Additionally, the relationship between the friction coefficients and each of the aforementioned parameters was assessed.

A staged fuzzy evolutionary algorithm for constrained large-scale multiobjective optimization

Constrained multiobjective optimization problems (CMOPs) are prevalent in practical applications, where constraints play a significant role. Building on techniques from constrained single-objective optimization, classical methods such as the constrained dominance principle have been extended to CMOPs. However, these methods struggle with CMOPs characterized by complex infeasible regions. Furthermore, as the number of decision variables increases, the search efficiency of algorithms deteriorates dramatically. To solve those issues, we propose a staged fuzzy evolutionary algorithm (i.e., SFEA) for constrained large-scale problems. To balance exploration and exploitation, a fuzzy stage adjustment strategy based on the sigmoid function is proposed. Furthermore, this article develops an improved fuzzy operator to perform fuzzy operations on various vectors (e.g., solutions or constraint violations). Computational experiments were conducted on CMOP test suites with up to 500 decision variables and a series of real-world applications. The experimental results demonstrate that, compared to existing peer algorithms, our algorithm exhibits superior or competitive performance.

An efficient single-loop strategy for time-variant reliability sensitivity analysis based on Bayes’ theorem

Time-variant reliability sensitivity analysis aims to measure the effect of input variables on the time-variant failure probability, which can then be used to guide structural design and optimization. The traditional methods for calculating the sensitivity index require a nested sampling procedure and their computational complexity depends on the number of input variables, making them computationally the most expensive. To address the above limitations, an efficient single-loop strategy is developed for time-variant reliability sensitivity (TRS) analysis. Based on Bayes' theorem, the TRS index is represented by the difference between the probability density function (PDF) and failure-conditional PDF of the variable. This derivation enables TRS analysis to be implemented using only a single set of samples, with computational costs that don’t depend on the number of input variables. Then, the sensitivity index is normalized to identify influential variables effectively. Several case studies involving numerical and engineering problems are employed to verify the feasibility and effectiveness of the proposed strategy. The direct Monte Carlo simulation is introduced to assess the accuracy of the obtained results. The results indicate that the proposed strategy provides acceptable sensitivity analysis for time-variant events, while greatly conserving computational resources and enhancing efficiency.

Inversion problem for Radon transforms defined on pseudoconvex sets

This paper is devoted to some questions of inversion for the classical and generalized integral Radon transform. The main question is to determine information about the integrand functions if the values of some integrals are known. A feature of the work of the authors of this message is an analysis of the case when the function is integrated according to hyperplanes in finite-dimensional Euclidean space, and the integrands depend not only on the variables of integration, but also on some of the variables characterizing the hyperplanes. At the same time, the number of independent variables describing known integrals are smaller than those of the unknown integrand. We consider discontinuous integrands defined specifically introduced pseudo-convex sets. A Stefan-type problem is posed about finding surfaces discontinuities of the integrand function. The work provides formulas based on the application special integro-differential operators to known data and allowing you to solve the assigned tasks.

Hybrid quantum annealing decomposition framework for unit commitment

Quantum computing is an emerging and promising technology that has overwhelming quantum advantages compared to its classical counterparts. Unit commitment (UC) is a critical issue in the power system, and it becomes more challenging with the integration of intermittent renewable energy. Therefore, this paper proposes an innovative decomposition and coordination optimization framework to accelerate the solution of UC, in which the interaction between an adiabatic quantum computer and a classical computer is designed to harness the immense computational power of quantum computers effectively. First, decomposition methods considering the requirements of quantum computers are introduced to decompose UC into small-scale models. Then, the paper presents a quadratic unconstrained binary optimization modeling method to transform UC problems into the form of quantum computing. Furthermore, due to the limitations of quantum computing resources, a reductive variable technique is proposed to reduce the number of slack variables in the optimization model and ensure that it remains feasible for quantum computers. Case studies conducted in test systems with a quantum annealing simulator and a real quantum annealing computer illustrate the feasibility and effectiveness of the method and demonstrate its potential in the era of quantum computing.

A logistic model for precise tomato fruit-growth prediction based on diameter-time evolution

Monitoring tomato fruit growth in large greenhouse environments is crucial for estimating harvest volume and maximising labour efficiency. Mathematical modelling is commonly used to characterise crop and fruit growth and to understand crop responses to environmental influences. This study presents a logistic approach for estimating tomato fruit maturity based on fruit diameter. A set of logistic models were evaluated, and their precision and ability to forecast tomato fruit growth were analysed. Non-linear least square regression was used to fit each model to individual tomato fruits from a sample of over 600 fruits of two species (small and large fruit-bearing tomatoes) grown within one year. The focus was on fruit diameter and the necessary measurement precision for fruit maturity estimation. Low-dispersion variables were identified within our logistic model functions, and fruit species-specific model parameters were determined. Furthermore, we reduced the number of regression variables by identifying parameters of low variance. This allowed for high prediction precision (>80%) at the early fruit maturity stages (<30%). The fruit diameter data were collected manually using a caliper. Our results establish an upper limit for the measurement precision of automated fruit size estimation approaches utilising photogrammetry, with an optical range of 98% for 3 mm and 90% for 5 mm precision.

Predicting herbage biomass on small‐scale farms by combining sward height with different aggregations of weather data

AbstractAccurate predictions of herbage biomass are important for efficient grazing management. Small‐scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning‐based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on‐farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha−1, where DM is dry matter) and increased the R2 by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R2 = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH‐based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH‐based models for small‐scale farms.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1hour data to cover time frequencies ranging from 1hour to 1month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Worst-case analysis of restarted primal-dual hybrid gradient on totally unimodular linear programs

We analyze restarted PDHG on totally unimodular linear programs. In particular, we show that restarted PDHG finds an ϵ-optimal solution in O(Hm12.5nnz(A)log⁡(Hm2/ϵ)) matrix-vector multiplies where m1 is the number of constraints, m2 the number of variables, nnz(A) is the number of nonzeros in the constraint matrix, H is the largest absolute coefficient in the right hand side or objective vector, and ϵ is the distance to optimality of the outputted solution.