Error Reduction Research Articles

Optimising Processes in a Severe Burn Intensive Care Unit through the Implementation of a Digital Management System

The treatment of severely burned patients is demanding and necessitates specialised centres capable of providing adequate therapy over several months. The establishment of digital management systems in intensive care units signifies a substantial advancement in modern healthcare. Introducing such a system in a specialised intensive care unit for severe burn patients presents opportunities for optimisation but also potential obstacles. This study aims to provide insights into the perception of change from the perspective of staff and discuss the implementation of digital systems in the field of intensive care medicine. After a selective sample was established, the impacts of the digital management system were examined across various categories. The data collected through a questionnaire and brief interviews were evaluated in terms of average values within each category, with interpretations taking into account characteristics such as professional group and work experience. Overall, the digital management system is considered suitable for use in the intensive care unit for severe burn patients by both medical and nursing staff. The continuous monitoring of vital parameters and the reduction of errors in medication administration are highlighted as positive aspects. However, negative points include the inferior documentation of burn wounds and specialised documentation for burn patients. In due consideration of various factors such as experience, team size, and patient clientele, which impact the usability of the program, some aspects in need of improvement were identified. In summary, however, it can be said that there was a positive and favourable consensus regarding the introduction of such a system in the intensive care unit. Additionally, it can be concluded that the system is described as significantly more effective for a general surgical intensive care unit than for a specialised intensive care unit, e. g. an intensive care unit for severe burn patients.

Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework

AbstractWe propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.

Analysis of models of measurement and correction of volumetric error of three-axis metal-cutting machine tool

The paper considers the improvement of the quality of products of machined production in connection with the quality of machining of various products and, accordingly, with the accuracy of the equipment used. It is noted that geometrical errors, which account for up to 40 % of the total error of a multi-axis, especially three-axis, metal-cutting machine tool, have the greatest influence on the accuracy of product machining. In order to improve the quality of product machining, it is necessary to correct its parameters according to the measurement information received during machine tool operation, for which the methods of mathematical modelling are used. Models of the volumetric error of a three-coordinate metal-cutting machine tool are developed, taking into account the summands of the first (linear) and second (quadratic) order of smallness. A comparative analysis of the developed models is carried out. The results of experiments on measurement of volumetric errors of the STAN S500 complex and their correction on the basis of the nonlinear theoretical model are given. It is found that in the range of angular errors 0–2.5′ the consideration of quadratic terms of the model in addition to linear ones does not lead to a significant reduction of the volumetric error. It is shown that when processing measurement information of multi-axis metal-cutting machines it is sufficient to limit the consideration of components of the volumetric error not higher than the first order of smallness. The research results are useful for the acceptance and periodic control of the volumetric error of metal-cutting machines, as well as for the programme reduction of the volumetric error.

Assessing the Effectiveness of Telepharmacy Services in Rural and Underserved Areas

This study evaluates the effectiveness of telepharmacy services in improving healthcare delivery in rural and underserved areas. Utilizing a mixed-methods design, both quantitative and qualitative data were collected over a 12-month period. The study involved 200 patients who used telepharmacy services, providing remote access to pharmacists for medication dispensing, consultations, and patient education. Quantitative data included patient surveys on medication adherence and satisfaction, along with medication records to assess dispensing errors. Qualitative data were obtained through interviews with patients and pharmacists to explore their experiences. Results indicated a significant increase in medication adherence rates from 60.0% to 85.0% post-telepharmacy implementation. Patient satisfaction scores across various domains improved notably, with mean scores for access to pharmacists, quality of consultations, and overall satisfaction showing significant enhancements (p < 0.001). Additionally, there was a substantial reduction in medication errors, with dispensing errors decreasing from 15.0% to 5.0% (p < 0.01) and prescription errors from 10.0% to 4.0% (p < 0.05). Economic analysis revealed a decrease in healthcare costs per patient from $2000 to $1500, along with a reduction in hospitalizations per 1000 patients from 50 to 30 (p < 0.01). These findings are consistent with previous studies and underscore the potential of telepharmacy as a cost-effective and efficient solution to healthcare disparities in rural and underserved regions. Despite technological and regulatory challenges, telepharmacy represents a promising approach to enhancing healthcare access, improving clinical outcomes, and achieving significant cost savings in rural healthcare systems.

Knowing where to go: Spatial memory guides eye and body movements in a naturalistic visual search task.

Most research on visual search has used simple tasks presented on a computer screen. However, in natural situations visual search almost always involves eye, head, and body movements in a three-dimensional (3D) environment. The different constraints imposed by these two types of search tasks might explain some of the discrepancies in our understanding concerning the use of memory resources and the role of contextual objects during search. To explore this issue, we analyzed a visual search task performed in an immersive virtual reality apartment. Participants searched for a series of geometric 3D objects while eye movements and head coordinates were recorded. Participants explored the apartment to locate target objects whose location and visibility were manipulated. For objects with reliable locations, we found that repeated searches led to a decrease in search time and number of fixations and to a reduction of errors. Searching for those objects that had been visible in previous trials but were only tested at the end of the experiment was also easier than finding objects for the first time, indicating incidental learning of context. More importantly, we found that body movements showed changes that reflected memory for target location: trajectories were shorter and movement velocities were higher, but only for those objects that had been searched for multiple times. We conclude that memory of 3D space and target location is a critical component of visual search and also modifies movement kinematics. In natural search, memory is used to optimize movement control and reduce energetic costs.

Plain Capping for Improved Accuracy of Approximate One- and Two-Electron Densities at Two-Particle Coalescence Points.

The values of the one-electron and intracule densities at two-particle coalescence points that enter the expressions for relativistic corrections to energies of Coulombic systems cannot be efficiently computed with sufficient accuracy from approximate wave functions expressed in terms of cuspless basis functions such as the explicitly correlated Gaussians. A new approach to alleviation of this problem, called plain capping, is proposed. Unlike those offered by the previously published formalisms, such as the expectation value identities and integral transforms, the accuracy improvements effected by the plain capping are attained with negligible computational effort and minimum programming. In the case of the on-top two-electron densities, whose accurate computation is particularly costly, the plain capping constitutes the only viable means of error reduction available at present.

DIB‐BOT: An Open‐Source Hardware Approach for Plate‐Integrated Droplet Interface Bilayer Deposition

AbstractDroplet interface bilayers (DIBs) offer a controlled lipid environment for studying membrane‐bound processes, with applications in artificial cells, biosensing, and biophysics. Current DIB fabrication faces challenges due to time‐consuming processes and specialized equipment, limiting scale‐up and hindering statistical significance in single‐molecule assays. This research introduces “DIB‐BOT,” an open‐source solution combining a nanoinjector and a 3D printer. DIB‐BOT enables rapid, reproducible DIB fabrication, overcoming manual limitations. Using off‐the‐shelf components, DIB‐BOT ensures high spatial reproducibility, minimal user input, and scalable experiments. The system's utility is demonstrated through pairwise droplet assembly and a fluorescence plate‐reader assay. Compared to manual fabrication, DIB‐BOT shows a 10‐fold reduction in droplet volume error, a threefold reduction in positional error, and 100% droplet yield. This method lowers entry barriers to DIB research, expanding its applications and enhancing data quality.

Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.

Exploiting the Electrochemical Impedance Spectroscopy Frequency Profiles for State-of-Health Predication of Lithium-Ion Battery

Battery Management Systems (BMS) are essential for optimizing battery performance and extending lifespan through continuous monitoring and decision-making via control sensors. The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. However, one of the main challenges in the BMS domain development is finding accurate and effective algorithms for battery SOH prediction, especially for electric vehicles and grid-connected energy storage systems. This study introduces a new SOH prediction method using wavelet-convolutional neural regression networks (CNRN) algorithms. The methodology involves extracting detailed frequency profiles from Electrochemical Impedance Spectroscopy (EIS) data, which are processed through wavelet transformation to capture both time and frequency domain features. These transformed profiles are then input into the CNRN model for SOH prediction. The results demonstrate improved SOH prediction accuracy with EIS frequency profiles, evidenced by a reduction in root mean square error (RMSE) compared to the standard EIS profile. This improvement is due to the fact that the wavelet-CNRN algorithm efficiently captures both the time and frequency features of the battery impedance. Moreover, the performance of the proposed algorithm demonstrated robustness in early end-of-life (EOL) prediction, thereby enhancing the reliability and safety of BMS functions.

Effects of Web-Based Survey Tool and Public Health Services for Low-Cost Detection of Vulnerable Medication Errors Using Telepharmacy

Telepharmacy, in which prescription requests are reviewed and approved online by a pharmacist in a different geography, is an effective way to lower the number of mistakes made when giving medicines. However, not enough studies have been done on the benefits of error reduction and the possible cost savings that come with these teleservices. This paper discusses a study examining what happened when a web-based survey tool and telepharmacy solutions were used to find Vulnerable Medication (VM) errors and low-cost Public Health (PH) services. It is very important to get each patient's drug records when they come into the Emergency Room (ER) so that any mistakes in their current medication record can be found. The trained pharmacists were interviewed after using a safe online tool to keep track of drug information. Details about each patient were gathered, along with the amount and type of VM and any PH errors found during the data-gathering procedure. From May 2022 to November 2022, 190 patient files were successfully filled up utilizing the survey instrument throughout the experimental period. Among the 1090 drugs documented, 41.38% were classified as drugs of high risk. 42.33% of possible prescription mistakes were categorized as VM errors. This online survey tool has enhanced the caliber and effectiveness of identifying possible errors during the gathering of medication records by pharmacists. This data may be readily accessed and contribute to debates about the reconciliation of drugs at the management level. It can also positively affect patient care outcomes by facilitating the development of virtual procedures that may reduce drug-related incidents.

Representation error reduction in compressive sensing using generative models with pivotal tuning

Reducing the transmitted data volume is essential for implementing networks with resource limited and energy-constrained devices. In this sense, compressive sensing becomes a powerful alternative as it moves the most computationally complex task to the central server node, in contrast to the traditional compression scheme. Recently a combination of compressive sensing and generative models has appeared, giving rise to Compressive Sensing using Generative Models (CSGM). Although CSGM reduce reconstruction errors, they introduce the so-called representation error. This paper proposes CSGM-Pivotal Tuning Inversion (CSGM-PTI), a technique based on model retraining in decompression time to reduce the representation error in CSGM. The idea of CSGM-PTI is to expand the scope of the generative model to include the desired signal. The results show robustness to noise and performance gains in signal reconstruction of up to 20% compared with Deep Image Prior (DIP), standalone CSGM, and Wavelet Thresholding (WT) techniques, all traditionally used in the related literature.

An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17–40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Time-dependent reliability analysis of aerospace electromagnetic relay considering hybrid uncertainties quantification of probabilistic and interval variables

Reliability is a crucial metric in aerospace engineering. The results of reliability assessments for components like aerospace electromagnetic relays directly impact the development and operational reliability of aerospace engineering systems. Current methods for analyzing the reliability of aerospace electromagnetic relays have limitations, such as neglecting the combined effects of multiple uncertain factors, degradation of key component properties, and the influence of fluctuations in aerospace environments. Additionally, these methods often assume a single-type uncertainty in the manufacturing process, leading to significant deviations between the analysis results and actual measurement results. To address these issues, this study proposes an efficient time-dependent reliability analysis method based on the HL-RF algorithm, considering a hybrid of probabilistic and interval uncertainty that accounts for degradation and environmental conditions. The proposed method is applied to the reliability analysis of actual aerospace electromagnetic relay products and compared with traditional methods, demonstrating significant advantages. The proposed method has been applied to the time-dependent reliability analysis of actual aerospace electromagnetic relay products under different environmental conditions. The analysis results exhibit an error margin within 5.12% compared to actual measurement results. Compared to analysis methods solely based on probabilistic uncertainty quantification or interval uncertainty quantification, this method reduces the analysis error by 52% and 67% respectively. When compared to two other state-of-the-art methods that integrate probabilistic and interval uncertainty quantification, the error reduction is 23%. These demonstrate the superiority of the proposed method and validates its effectiveness. The presented approach has the potential to be extended for reliability analysis in other aerospace electromechanical systems.

Improving Depth Perception in Immersive Media Devices by Addressing Vergence-Accommodation Conflict.

Recently, immersive media devices have seen a boost in popularity. However, many problems still remain. Depth perception is a crucial part of how humans behave and interact with their environment. Convergence and accommodation are two physiological mechanisms that provide important depth cues. However, when humans are immersed in virtual environments, they experience a mismatch between these cues. This mismatch causes users to feel discomfort while also hindering their ability to fully perceive object distances. To address the conflict, we have developed a technique that encompasses inverse blurring into immersive media devices. For the inverse blurring, we utilize the classical Wiener deconvolution approach by proposing a novel technique that is applied without the need for an eye-tracker and implemented in a commercial immersive media device. The technique's ability to compensate for the vergence-accommodation conflict was verified through two user studies aimed at reaching and spatial awareness, respectively. The two studies yielded a statistically significant 36% and 48% error reduction in user performance to estimate distances, respectively. Overall, the work done demonstrates how visual stimuli can be modified to allow users to achieve a more natural perception and interaction with the virtual environment.

DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification

<p>In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.</p>

Kinetic analysis and calculation correction methods for moisture evaporation rate in pine lignocellulosic biomass

This study investigates the kinetics of moisture evaporation in pine wood during the drying stage, focusing on analyzing thermogravimetric (TG) curves. The research is based on the kinetic rate model and TG curves obtained at various heating rates, specifically 5, 10, 15, and 20 K/min for the heat release rate, within a temperature range from room temperature to 473 K. Utilizing thermogravimetric experiments and the kinetic rate model, the apparent activation energy and pre-exponential factor are calculated. Further refinement of these calculations is achieved using the Nelder-Mead algorithm. The results show good agreement between the average values of the apparent activation energy (37.88 kJ/mol) and pre-exponential factor (4458.20 s−1) and experimental measurements in the initial and later drying stages, with discrepancies noted in the middle stage. Establishing a relationship between the correction coefficients of these parameters and the heating rate leads to a reduction in calculation errors. Notably, the pre-exponential factor is found to be influenced by heating rates, while the apparent activation energy remains relatively stable. Moreover, the errors are significantly reduced after correction, and the error curve of 10 K/min is the most significant. Overall, the proposed correction method holds promise for accurately calculating moisture evaporation rates in lignocellulosic biomass, offering valuable insights for biomass drying applications.

Fast flow field prediction based on E(2)-equivariant steerable convolutional neural networks

In the field of flow field reconstruction, traditional deep learning models predominantly rely on standard convolutions, but their predictive accuracy remains limited. To address this issue, we explore the potential of E(2)-equivariant convolutions to enhance the predictive accuracy of deep learning models for fast flow field prediction. Unlike conventional convolutions, E(2)-equivariant convolutions offer a richer representation capability by better capturing geometric and structural information. Our neural network integrates an attention mechanism that leverages the signed distance function (SDF) to encode geometric details and an indicator matrix to incorporate boundary conditions. The model predicts velocity and pressure fields as outputs. We conducted experiments specifically targeting non-uniform steady laminar flows, and the results show a 16.1% reduction in overall error compared to models based on traditional convolutions while maintaining high efficiency. These findings indicate that E(2)-equivariant convolution, coupled with an attention mechanism, significantly improves flow field prediction by focusing on critical information and better representing complex geometries.

Prediction of mine water quality by the Seq2Seq model based on attention mechanism

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and Sequence to Sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction.

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes.