Error Cost Research Articles

Determining Medication Errors in an Adult Intensive Care Unit.

Research addressing the costs of Medication errors (MEs) is still scarce despite issues related to patient safety having significant economic and health impacts, making it imperative to analyze the costs and adverse events related to MEs for a better patient, professional, and institutional safety. To identify the number of medication errors and verify whether this number was associated with increased hospitalization costs for patients in an Intensive Care Unit (ICU). This retrospective cross-sectional cohort study evaluated secondary data from patients' electronic medical records to compile variables, create a model, and survey hospitalization costs. The statistical analysis included calculating medication error rates, descriptive analysis, and simple and multivariate regression. The omission error rate showed the highest number of errors per drug dose (59.8%) and total errors observed in the sample (55.31%), followed by the time error rate (26.97%; 24.95%). The omission error had the highest average when analyzing the entire hospitalization (170.40) and day of hospitalization (13.79). Hospitalization costs were significantly and positively correlated with scheduling errors, with an increase of BRL 121.92 (about USD $25.00) (95% CI 43.09; 200.74), and to prescription errors, with an increase of BRL 63.51 (about USD $3.00) (95% CI 29.93; 97.09). We observed an association between two types of medication errors and increased hospitalization costs in an adult ICU (scheduling and prescription errors).

Post-stimulus encoding of decision confidence in EEG: toward a brain–computer interface for decision making

Objective. When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain–computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. Approach. We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. Main results. We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Significance. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Optimization of the thermal management system of battery thermal network model based on coupled liquid cooling of phase change materials

Battery optimization models can extend battery life, improve safety, and maximize battery performance. Previous optimization methods focus more on (Computational Fluid Dynamics) CFD and data mining. However, the two optimization methods apply only to specific battery module structures. In addition, previous studies have focused more on improving optimization algorithms to improve performance, but the computational design of large-scale battery thermal management systems still requires large loads. This paper presents a new optimization method based on the thermal equivalent circuit model (TECM) of battery thermal management system (BTMS) for the efficient design of liquid-cold coupled phase change material BTMS (PCM). The method can easily change and optimize the battery module structure according to different application scenarios. Compared with the reference structure, the TECM Parto solution optimized combined with multi-objective particle swarm optimization (MOPSO) saved 39.51 % of the liquid cooling energy consumption, reduced the maximum temperature rise of BTMS by 0.38 °C, and reduced the maximum temperature difference by 19.81 %. The method adopted can improve the efficiency and quality of BTMS design, reduce the cost of trial and error, and speed up the design process.

IMPROVING CAMERA POSE ESTIMATION USING SWARM PARTICLE ALGORITHMS

Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.

Goal-Oriented Policies for Cost of Actuation Error Minimization in Wireless Autonomous Systems

We consider the minimization of the cost of actuation error under resource constraints for real-time tracking in wireless autonomous systems. A transmitter monitors the state of a discrete random process and sends updates to the receiver over an unreliable wireless channel. The receiver takes actions according to the estimated state of the source. For each discrepancy between the real state of the source and the estimated one, we consider a different cost of actuation error. This models the case where some states, and consequently the corresponding actions to be taken, are more important than others. We provide two algorithms: one reaching an optimal solution but of high complexity, and one providing a suboptimal solution but with low complexity. The performance of the two algorithms are quite close as shown by the simulations.

Advanced Flowrate Control of Petroleum Products in Transportation: An Optimized Modified Model Reference PID Approach

Efficient flowrate control is paramount for the seamless operation and reliability of petroleum transportation systems, where precise control of fluid movement ensures not only operational efficiency but also safety and cost-effectiveness. The main aim of this paper is to develop a highly effective modified model reference PID controller, tailored to ensure optimal flowrate control of petroleum products throughout their transportation. Initially, the petrol transportation process is analyzed to establish a suitable mathematical model based on vital factors like pipeline diameter, length, and pump attributes. However, using a basic first-order time delay model for petrol transportation systems is limiting due to inaccuracies, variable delay issues, safety oversights, and real-time control complexities. To improve this, the delay portion is approximated as a third-order transfer function to better reflect complex physical conditions. Subsequently, the PID controller is synthesized by modifying its structure to address flowrate control issues. These modifications primarily focus on the controller’s derivative component, involving the addition of a first-order filter and alterations to its structure. To optimize the proposed controller, the genetic, black hole, and zebra optimization techniques are employed, aiming to minimize an integral time absolute error cost function and ensure that the outlet flow of the controlled system closely follows the response of an appropriate reference model. They are chosen for their proficiency in complex optimization to enhance the controller's effectiveness by optimizing parameters within constraints, adapting to system dynamics, and ensuring optimal conditions. Through simulations, it is demonstrated that the proposed controller significantly enhances the stability and efficiency of the control system, while maintaining practical control signals. Moreover, the proposed modifications and intelligent tuning of the PID controller yield remarkable improvements compared to previous related work, resulting in a 36% reduction in rise time, a 63% reduction in settling time, an 80% reduction in overshoot, and a 98% reduction in cost value.

Decision making in child protection emergency cases in Norway

BackgroundResearch on decision-making factors in child protection emergency cases is scarce, and even less is known about factors that can avert emergency placement. ObjectiveThis study aims to explore factors that distinguish emergency cases that conclude in emergency placement (EP) from emergency cases that conclude in averted emergency placement (AEP).Participants and settingThe study is based on data from an electronic survey on child protection emergency work, completed by a national sample of child protection leaders (N = 154). MethodThe survey included questions on external and organisational conditions of the Child Protection Service (CPS), as well as descriptions, activities and assessments related to selected emergency cases. Sixty-three of the selected cases were concluded with EP and ninety-one cases were concluded with AEP. Group differences between EP and AEP cases were analysed using descriptive statistics and logistic regression analyses. ResultsThe findings indicate that the case factor “violence at home/child abuse” and the CPS’s activity of “considering out-of-home measures” at initial assessment phase were significantly associated with EP. Activities like “home visits” and “contact with the network” were significantly associated with AEP. Crucial factors for decision-making according to the leaders were “parents’ opinion” and “resources in the network”, which were both significantly associated with AEP. ConclusionsThe study has identified several potential important factors for averting emergency placements. Building averting capability in CPS is important to avoid the huge relational and social costs of errors relating to EP decisions.

Conspiracy theories explained by a cheating detection mechanism

AbstractResearch on conspiracy theories sometimes tends to pathologise this phenomenon with a focus on the impact of (sub)pathological predictors. However, socio‐political factors also play a significant role in predicting belief in specific conspiracy theories. The aim of this article is to bridge these two perspectives through a unified cognitive mechanism. Based on an overlap between cheating and conspiracy concepts, we assume a cheating detection mechanism likely to underlie belief in conspiracy theories. Starting from the adaptive challenges of cheating detection, we explore the workings of this mechanism using signal detection theory and error management theory. The probability of cheating and decision bias according to the asymmetry of error costs in cheating detection could lead individuals to infer conspiracy theories. This functional mechanism not only explains the links between socio‐political predictors and adherence to conspiracy theories but also helps us deduce alterations that may foster a stable inclination towards believing in conspiracy theories. These alterations, in turn, offer an explanation for the links between (sub)pathological predictors and conspiracy mentality. By integrating existing literature, our proposed model sheds light on the mechanisms underlying belief in conspiracy theories and presents new predictions to guide future research.

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials.

Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

Longitudinal Aerodynamic Parameter Estimation Using Neural Network and Gauss-Newton Method

A new parameter estimation method based upon Feed Forward Neural Network is proposed. The proposed method utilizes the universal mapping capability of Feed Forward Neural Network to develop flight dynamic model of aircraft. Gauss-Newton method is used to obtain values of aerodynamic parameters by minimizing a chosen error cost function. The method has then been validated using flight data pertaining to longitudinal dynamics of aircraft. Proof of match approach has been followed to verify the estimated model by the proposed method. The results obtained using the proposed method have also been compared with those obtained using wind tunnel tests, and Filter Error method. Unlike, most of the conventional methods, the proposed method does not require a priori description of the model. It also bypasses the requirement of solving the equations of motion. This feature may have special significance in handling flight data of an unstable aircraft.

Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English

Feature selection is a widely utilized technique in educational data mining that aims to simplify and reduce the computational burden associated with data analysis. However, previous studies have overlooked the high costs involved in acquiring certain types of educational data. In this study, we investigate the application of a multi-objective gray wolf optimizer (GWO) with cost-sensitive feature selection to predict students’ academic performance in college English, while minimizing both prediction error and feature cost. To improve the performance of the multi-objective binary GWO, a novel position update method and a selection mechanism for a, b, and d are proposed. Additionally, the adaptive mutation of Pareto optimal solutions improves convergence and avoids falling into local traps. The repairing technique of duplicate solutions expands population diversity and reduces feature cost. Experiments using UCI datasets demonstrate that the proposed algorithm outperforms existing state-of-the-art algorithms in hypervolume (HV), inverted generational distance (IGD), and Pareto optimal solutions. Finally, when predicting the academic performance of students in college English, the superiority of the proposed algorithm is again confirmed, as well as its acquisition of key features that impact cost-sensitive feature selection.

Assessment of Cost and Time of Errors in Construction Documents on Selected Building Projects in Nigeria

The construction industry has generally been tagged with poor project performance. Cost and time overruns are the leading causes of poor performance. Design and construction errors are equally leading causes of cost and time overruns. The objectives of this study are to investigate the prevalent types of errors in construction documents, the cost implication of the errors, the time implication of the errors, and strategies adopted to mitigate the errors. The study involves an interview of construction professionals on 51 selected building projects across the southwestern States of Nigeria through the non-probabilistic convenience sampling technique. The findings of the study indicated that the Nigerian construction industry is largely dominated by the use of traditional procurement method and many construction projects lack complete construction documents. Also, errors in construction documents added about 9% to the contract sum and 31% to cost overrun. It also added about 21% to the agreed duration and 40% to the total time overrun. The study concluded that errors in construction documents affect project duration more than the project cost and can triple the agreed duration of building projects in some cases. The study recommended that based on the projects investigated, an additional 10% should be added to the agreed cost and 20% to the scheduled duration of building projects.

Machine learning in design of broadband terahertz absorbers based on composite structures

Metasurface absorbers have gained increasing attention in the field of electromagnetic (EM) absorption in recent years. The conventional design method often involves designing various structures and comparing absorption bandwidth and intensity to determine the absorber's structural parameters. The absorption of EM waves by absorbers involves a complicated process of electric field excitation and impedance matching. Although design experience can reduce the number of design iterations, the selection of artificial parameters is often imprecise and can result in design bias. Fortunately, machine learning (ML) algorithms offer the possibility of automating the design of critical parameters for metasurfaces, which can effectively reduce the laborious design process and the high cost of trial and error. In this study, a pyramid-based metasurface absorber (PMA) and an inverted pyramid-based metasurface absorber (IPMA) were designed and optimized using the random forest (RF) algorithm. The results show that the absorption bandwidths of the two absorber models are 2.68 THz and 2.42 THz, respectively, and the mean absolute percentage error (MAPE) is only 0.60%, which is significantly better than other classical ML and deep learning (DL) algorithms. This study offers a new approach for designing complex systems related to EM wave absorption, reflection, and transmission propagation.

Surface modeling and influencing factors for microlens array by slow tool servo machining

Microlens arrays (MLAs) are widely used in a variety of important fields due to their unique optical properties. Slow tool servo machining (STSM)technology is a manufacturing technology for machining non-rotationally symmetric surfaces. In order to solve the problems of many influencing factors of STSM machining quality, high experimental trial and error cost, and mutual limitation of accuracy and efficiency, on the basis of analyzing and determining reasonable tool trajectories, a microlens array machining surface morphology prediction model is established from the theoretical point of view, and the prediction model is optimized by combining the two-dimensional Gaussian filtering method, and the surface accuracy and roughness are used to evaluate the processing quality. The prediction model is used to analyze the influence, like feed per revolution, radius of tool tip, and tool setting error on the surface quality. After the Microlens machining experiments, the results are matched with the simulation, which shows us the model is correct, and the MLAs were machined by the optimal parameters with the surface shape error PV = 0.741 μm, surface roughness Sa = 22 nm, Sq = 28 nm, and Sz = 59 nm.

Artificial hummingbird optimizer as a novel adaptive algorithm for identifying optimal coefficients of digital IIR filtering systems

ABSTRACT This paper introduces the artificial hummingbird algorithm (AHA) as a novel and efficient algorithm for designing digital infinite impulse response (IIR) filtering systems. IIR filters are commonly used in various applications, and their design often involves complex error surfaces and coefficients. The AHA aims to address these challenges by optimizing the IIR model using mean squared error (MSE) cost function. Four benchmark examples are considered for evaluation by comparing both the same and reduced order cases of the IIR model. The results are compared against recent and efficient algorithms named prairie dog optimization, whale optimization algorithm, and artificial bee colony algorithm. The evaluation includes an analysis of the obtained coefficients, statistical measures, and convergence profiles. The findings show that the AHA outperforms the competing algorithms, achieving more accurate models and providing better statistical performance. Additionally, the AHA consistently converges to the lowest MSE values for each case. Further assessments are conducted using various examples from the literature, which validate the AHA's superior ability in digital IIR filtering design. The algorithm demonstrates more accurate identification and achieves lower MSE values compared to alternative methods which highlight the promising potential of the AHA for efficient and precise digital IIR filtering system design.

Wave‐based inversion at scale on graphical processing units with randomized trace estimation

AbstractThanks to continued performance improvements in software and hardware, wave‐equation‐based imaging technologies, such as full‐waveform inversion and reverse‐time migration, are becoming more commonplace. However, widespread adaptation of these advanced imaging modalities has not yet materialized because current implementations are not able to reap the full benefits from accelerators, in particular those offered by memory‐scarce graphics processing units. Through the use of randomized trace estimation, we overcome the memory bottleneck of this type of hardware. At the cost of limited computational overhead and controllable incoherent errors in the gradient, the memory footprint of adjoint‐state methods is reduced drastically. Thanks to this relatively simple to implement memory reduction via an approximate imaging condition, we are able to benefit from graphics processing units without memory offloading. We demonstrate the performance of the proposed algorithm on acoustic two‐ and three‐dimensional full‐waveform inversion examples and on the formation of image gathers in transverse tilted isotropic media.

Saturation and duty cycle tolerant self-sensing for active magnetic dampers

Traditional active magnetic damper (AMD) systems rely on external position sensors to measure the mover’s radial position, though these sensors add cost, complexity, failure modes, and potential sources of error due to the axial non-colocation of actuator and sensor. An alternate, so-called “self-sensing” approach seeks to exploit displacement-induced changes in electromagnet inductance to determine mover position; such techniques have been described in active magnetic bearing (AMB) and AMD literature since the 1980s. This paper details a novel, magnetic-saturation tolerant self-sensing approach for AMDs. The theoretical basis is presented; it is validated both experimentally and via a transient magnetic finite-element model. A position estimation error of 4.17 µm (0.164 mils) RMS is experimentally demonstrated over a typical mover position range and a theoretical bandwidth of 250 Hz is achieved.

Rate-distortion theory of neural coding and its implications for working memory.

Rate-distortion theory provides a powerful framework for understanding the nature of human memory by formalizing the relationship between information rate (the average number of bits per stimulus transmitted across the memory channel) and distortion (the cost of memory errors). Here we show how this abstract computational-level framework can be realized by a model of neural population coding. The model reproduces key regularities of visual working memory, including some that were not previously explained by population coding models. We verify a novel prediction of the model by reanalyzing recordings of monkey prefrontal neurons during an oculomotor delayed response task.

Deep Learning for Continuous-Time Leader Synchronization in Graphical Games Using Sampling and Deep Neural Networks

Abstract We propose a novel deep learning-based approach for the problem of continuous-time leader synchronization in graphical games on large networks. The problem setup is to deploy a distributed and coordinated swarm to track the trajectory of a leader while minimizing local neighborhood tracking error and control costs for each agent. The goal of our work is to develop optimal control policies for continuous-time leader synchronization in graphical games using deep neural networks. We discretize the agents model using sampling to facilitate the modification of gradient descent methods for learning optimal control policies. The distributed swarm is deployed for a certain amount of time while keeping the control input of each agent constant during each sampling period. After collecting state and input data at each sampling time during one iteration, we update the weights of a deep neural network for each agent using collected data to minimize a loss function that characterizes the agents' local neighborhood tracking error and the control cost. A modified gradient descent method is presented to overcome existing limitations. The performance of the proposed method is compared with two reinforcement learning-based methods in terms of robustness to initial neural network weights and initial local neighborhood tracking errors and the scalability to networks with a large number of agents. Our approach has been shown to achieve superior performance compared with the other two methods.

The graded predictive pre-activation in Chinese sentence reading: evidence from eye movements.

Previous research has revealed that graded pre-activation rather than specific lexical prediction is more likely to be the mechanism for the word predictability effect in English. However, whether graded pre-activation underlies the predictability effect in Chinese reading is unknown. Accordingly, the present study tested the generality of the graded pre-activation account in Chinese reading. We manipulated the contextual constraint of sentences and the predictability of target words as independent variables. Readers' eye movement behaviors were recorded via an eye tracker. We examined whether processing an unpredictable word in a solid constraining context incurs a prediction error cost when this unpredictable word has a predictable alternative. The results showed no cues of prediction error cost on the early eye movement measures, supported by the Bayes Factor analyses. The current research indicates that graded predictive pre-activation underlies the predictability effect in Chinese reading.