Equipment Errors Research Articles

Microvascular reactivity in anesthetized cats: feasibility and proof of concept study

Abstract OBJECTIVE Assessment of feasibility and proof of concept study for microvascular reactivity in anesthetized cats. METHODS 12, adult, healthy, purpose-bred cats were included. Cats were anesthetized and instrumented with a perivascular ultrasound transit time flow probe around the main pulmonary artery, a femoral arterial catheter, a central venous catheter, and a laser Doppler flowmeter with a heating unit. After a 20-minute stabilization period, microvascular reactivity, hemodynamic variables, and arterial and central venous blood gases were measured during 3 phases: (1) baseline, (2) hemorrhage, and (3) isotonic crystalloid bolus. Hemorrhage was induced by atraumatic blood loss of 20 mL/kg over 20 minutes. An isotonic crystalloid bolus at 20 mL/kg was administered IV over 20 minutes. RESULTS 6 cats were excluded from the analysis due to equipment errors or surgical complications. Microvascular reactivity decreased significantly after hemorrhage and remained below baseline after isotonic crystalloid bolus. CONCLUSIONS Microvascular reactivity assessment is feasible in healthy anesthetized cats. Hemorrhage decreases microvascular reactivity, and the administration of an isotonic crystalloid bolus did not reverse these changes. However, the small sample size may limit the external validity of the results. CLINICAL RELEVANCE This study demonstrated the feasibility of microvascular reactivity assessment in anesthetized cats. However, proper validation is warranted before clinical application.

An Unusual Cause of Failure of Fiberoptic Intubation due to Continuous Suctioning: A Case Report

ABSTRACTFlexible fiberoptic intubation is the gold standard technique during difficult airway management. Failure of fiberoptic intubation could be due to patient factors, equipment factors, and operator expertise. We present a case where fiberoptic intubation was unsuccessful because of equipment error following continuous suction pressure application.

Can we reliably assess spine movement quality in clinics? A comparison of systems to evaluate reliability of spine movement quality in a healthy population

Inertial measurement units (IMUs) have the potential to facilitate a large influx of spine movement and neuromuscular control data to stratify low back pain (LBP) diagnosis and care; however, uncertainties related to validity and reliability are preventing widespread use and acceptance. This study evaluated the concurrent validity of Xsens DOT IMUs relative to gold-standard optical motion capture equipment, and compared within- and between-day reliability of both systems to track spine range of motion (ROM) and movement quality (MQ) by evaluating intraclass correlation coefficients (ICC), standard error of measurement (SEM), coefficient of variation (CV), and minimum detectable difference (MDD). ROM was evaluated during planar ROM movements, and local dynamic stability (LDS; λmax), mean absolute relative phase (MARP) and deviation phase (DP) were estimated from repetitive trunk flexion at 3 speeds, in 15 healthy controls. Results show no statistically significant differences between systems for all metrics, and ICCs ≥ 0.86; therefore, validity was confirmed for tracking primary axis ROM and MQ. IMU data revealed that absolute (C7, T12, and S1) and relative (thoracic, lumbar, and total) ROM was the most reliable metric, followed by λmax, DP, and MARP. Reliability was similar between systems, suggesting that the poorer between-day reliability (higher SEM and CV, lower ICC) observed is attributable to movement variability and sensor placement rather than equipment error. MDDs provide thresholds to researchers and clinicians for identifying change in MQ. Further standardization of evaluated movements/metrics, and patient subgrouping are suggested to improve reliability assessments and refine MDDs in future work.

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

Evaluation of the accuracy of remote emission sensing measurements via real-world vehicle dynamic tests

To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorithm (ISPA) for vertical remote sensing spectral data was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO2, CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO2, CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications.

Robotic urologic applications of the hinotori™ Surgical Robot System

ObjectiveTo assess the safety and effectiveness of urological tumor surgeries using the hinotori™ Surgical Robot System (hinotori) in a real-world clinical setting. MethodsAll surgeries including procedures of robot-assisted radical prostatectomy (RARP), robot-assisted partial nephrectomy (RAPN), robot-assisted radical nephrectomy (RARN), robot-assisted nephroureterectomy (RANU), robot-assisted adrenalectomy (RAA), and robot-assisted radical cystectomy with intracorporeal urinary diversion (RARC+ICUD) for urological tumors with hinotori and da Vinci surgical system (da Vinci) from January 2022 through September 2023 were enrolled. We evaluated the safety and effectiveness of surgery using hinotori compared using da Vinci. ResultsRobotic surgeries using hinotori were performed in a total of 91 cases, comprising 42 cases of RARP, 18 cases of RAPN, six cases of RARN, 10 cases of RANU, 13 cases of RAA, and two cases of RARC+ICUD. No major intraoperative complications were observed in any cases using hinotori. No major postoperative complications occurred in any of the cases. No case experienced unrecoverable equipment error during surgery. Meanwhile, robotic surgeries using da Vinci were performed in a total of 277 cases that included 126 cases of RARP, 94 cases of RAPN, 12 cases of RARN, 10 cases of RANU, 20 cases of RAA and 15 cases of RARC+ICUD. Major intraoperative complications occurred two cases. Major postoperative complications occurred in seven cases. Seven cases required transfusion. One case underwent conversion to open surgery. During the study period, no case experienced an unrecoverable equipment error. Surgical outcomes for cases with hinotori were comparable to those with da Vinci. ConclusionsThis study demonstrated that hinotori is a safe and feasible tool for robotic surgeries in the field of urology.

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Abstract Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

Advancements in the application of cardiopulmonary exercise testing for overweight and obese populations

AbstractObjectiveTo evaluate the advancements in cardiopulmonary exercise testing (CPET) among overweight or obese patients.MethodsReviewed the recent studies on clinical trials of CPET in overweight and obese patients, thought about the CPET advantage in obesity, while CPET equipment use for elucidating the principles underlying obesity and using CPET kinematic scheme to explore fat metabolism.ResultsThe exercise program developed by CPET is scientific, sustainable and advantageous. With this trial, we can develop a relatively effective, fast and measurable amount of exercise, on the basis of the exercise test in obese people, CPET can provide scientific and digital motion reducing weight for overweight patients. At the same time, the trial has become widely applied in research into drug and surgical treatments for overweight patients. It is possible to quantify the efficacy of a trial and ultimately assess the merits of CPET.ConclusionCPET is considered as a fundamental criterion for designing motion schemes. Despite limitations of equipment errors and patient endurance, the benefits of CPET in program formulation have been gradually recognized, which made it a promising tool in the field of obesity management.

Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection

Background and ObjectiveStroke has become a major disease threatening the health of people around the world. It has the characteristics of high incidence, high fatality, and a high recurrence rate. At this stage, problems such as poor recognition accuracy of stroke screening based on electronic medical records and insufficient recognition of stroke risk levels exist. These problems occur because of the systematic errors of medical equipment and the characteristics of the collectors during the process of electronic medical record collection. Errors can also occur due to misreporting or underreporting by the collection personnel and the strong subjectivity of the evaluation indicators. MethodsThis paper proposes an isolation forest-voting fusion-multioutput algorithm model. First, the screening data are collected for numerical processing and normalization. The composite feature score index of this paper is used to analyze the importance of risk factors, and then, the isolation forest is used. The algorithm detects abnormal samples, uses the voting fusion algorithm proposed in this article to perform decision fusion prediction classification, and outputs multidimensional (risk factor importance score, abnormal sample label, risk level classification, and stroke prediction) results that can be used as auxiliary decision information by doctors and medical staff. ResultsThe isolation forest-voting fusion-multioutput algorithm proposed in this article has five categories (zero risk, low risk, high risk, ischemic stroke (TIA), and hemorrhagic stroke (HE)). The average accuracy rate of stroke prediction reached 79.59 %. ConclusionsThe isolation forest-voting fusion-multioutput algorithm model proposed in this paper can not only accurately identify the various categories of stroke risk levels and stroke prediction but can also output multidimensional auxiliary decision-making information to help medical staff make decisions, thereby greatly improving the screening efficiency.

‘The left behind syndrome’—surgical equipment errors in plastic surgery

The author discusses surgical items accidentally left behind in patients after plastic surgery, things that increase the risk of this happening and ways to reduce the risk.

Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors

Large-sized step shafts are important devices for supporting and transferring heavy parts, and online inspection equipment for runout errors is affected by the environment and is subject to coaxiality errors and center-position errors, leading to problems such as reduced measurement accuracy in imaging systems. In view of the above problems, this paper proposes an improved optical imaging system design for runout error detection based on the plane-mirror-group correction method. Zemax was used to optimize the structure and simulate the optical path of the optical imaging system. The total length of the structure was 50 mm, and the MTF function for each field of view was greater than 0.3 at the spatial level up to a frequency of 42 lp/mm. The system was applied to a test platform for runout error detection, achieving the detection of runout errors of a large size in the radial direction and at the end face with a diameter range of 500–700 mm. The measurement repeatability was less than 30 μm, and the system corrected the coaxiality error of the stepped-shaft online inspection equipment considered in this paper.

Bacteriological Profile of Common Lab Contaminants Responsible for Spoilage in Biological Laboratory

In biological laboratories, contamination can arise from various sources, including airborne particles, equipment, reagents, and human error. Common contaminants responsible for spoilage in biological laboratories include bacteria, fungi, virus etc. Bacteria are ubiquitous in the environment and can contaminate laboratory samples, cultures, and media. There is a possibility of transferring microorganisms from one laboratory to the other during the course of activities that involves moving of materials. In this present work we had been identify three bacterial species like Staphylococcus aureus, Klebsiella pneumonia, Enterococcus Sp. which responsible for lab spoilage. Therefore measures like cleaning of lab, using protective gears, autoclaving of glassware needed to minimize lab contamination.

Itemized determination of As, Sb, Bi, Se, and Ge in graphite sample by HG-AFS with high-temperature microwave digestion.

In this paper, we optimized a method for fast and accurate determination of five impurity elements (As, Sb, Bi, Se, and Ge) in graphite samples to overcome the shortcomings of existing methods, such as complicated equipment, cumbersome process, multiple-time preparation, separate determination, and large error in results. Graphite samples were digested with HNO3-H2SO4-HClO4-HF in a high-temperature and high-pressure microwave digestion apparatus, and the elements were extracted and determined separately by AFS (atomic fluorescence spectrometry). There is no element loss during the processing and analysis of this method. The spike recoveries (As: 90.30%-102.3%, Sb: 90.73%-110.0%, Bi: 90.00%-99.67%, Se: 93.33%-110.0%, Ge: 92.26%-104.2%) and precision (RSD%; As: 1.34%-8.96%, Sb: 2.67%-7.10%, Bi: 1.83%-4.58%, Se: 0.36%-3.25%, Ge: 4.41%-8.65%) meet the requirements of the corresponding quality specifications. The method has some advantages (such as no elemental loss, fast testing, strong element targeting, and accurate results), and thus can achieve batch determination of graphite samples. The optimized method for graphite sample and final solution preparations can be used for diverse spectrometric technologies, and that for spectrometer conditions have reference value for HG-AFS instruments.

State primary special standard of acceleration unit in the field of gravimetry GET 190-2023: reproduction and transmission of the unit under the influence of geophysical factors

The relevance of research on the reproduction and transmission of the acceleration unit in gravimetry is determined by the development of measuring instruments for the absolute value of the acceleration of free fall and its changes. Qualitative and quantitative changes in the instrument base are due to the requirements of applied tasks solved using gravimetric data in such fields as geodesy, navigation, geodynamics, as well as the expansion of the field of practical application of absolute gravimeters. At the same time, in order to solve applied problems, along with accuracy requirements at a level close to the maximum achievable at the current level of technology development, maximum territorial coverage of measurement sites within the entire territory of the Russian Federation is often necessary. The accuracy of the results obtained with the help of measuring instruments is determined by the level of their metrological support, the main stages of which are the reproduction of the corresponding unit by the standard and its transfer to the measuring instrument. An analysis of possible sources of errors in gravimetric equipment has shown that when reproducing and transmitting the acceleration unit in gravimetry, it is necessary to take into account the influence of geophysical factors that manifest themselves as additional accelerations of a gravitational or inertial nature. The distribution of the gravitational field within a gravimetric point can manifest itself as an additional constant acceleration. Seismic processes and lunar and solar tides manifest themselves as variable accelerations. For various stages of metrological support of gravimetric devices, the mechanisms of the effects of such accelerations have been studied, as well as methods for accounting and reducing their influence using additional equipment have been developed. An additional gravimetric point with a cryogenic relative gravimeter and a broadband seismometer, as well as transported absolute ballistic and relative quartz gravimeters, were introduced into the State primary special standard of acceleration units in the field of gravimetry GET 190-2023.

Active Compensation Technology for the Target Measurement Error of Two-Axis Electro-Optical Measurement Equipment.

For two-axis electro-optical measurement equipment, there are many error sources in parts manufacturing, assembly, sensors, calibration, and so on, which cause some random errors in the final measurement results of the target. In order to eliminate the random measurement error as much as possible and improve the measurement accuracy, an active compensation technique for target measurement error is proposed in this paper. Firstly, the error formation mechanism and error transfer model establishment of the two-axis electro-optical measurement equipment were studied, and based on that, three error compensation and correction methods were proposed: the least square (LS)-based error compensation method, adaptive Kalman filter(AKF)-based error correction method, and radial basis function neural network (RBFNN)-based error compensation method. According to the theoretical analysis and numerical simulation comparison, the proposed RBFNN-based error compensation method was identified as the optimal error compensation method, which can approximate the random error space surface more precisely, so that a more accurate error compensation value can be obtained, and in order to improve the measurement accuracy with higher precision. Finally, the experimental results proved that the proposed active compensation technology was valid in engineering applicability and could efficiently enhance the measurement accuracy of the two-axis electro-optical measurement equipment.

An Improved Filtering Method and Application of Landslide Deformation Monitoring

Real-time early warning based on deformation monitoring is an important measure to reduce the threat related to landslides. However, due to the complexity of landslide deformation, equipment accuracy, and data transmission interference, the displacement obtained by existing monitoring equipment has random errors, resulting in inaccurate landslide warning. In this paper, a tangent angle model based on the improved filtering method is proposed for the early warning of landslides. An improved filtering method is proposed based on the least-square method to smooth the measurement errors of monitoring equipment, considering both “data smoothing effect” and “accelerated deformation real-time determination”. By comparing the least-squares method and the improved filtering method, the results demonstrate that the improved filtering method can more effectively smooth the fluctuation of the deformation rate. The amplitude of the error-induced tangent angle warning value fluctuation can be reduced, which provides correct early warning. The improved method provides a new approach for the real-time filtering of subsequent landslide deformation data.

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

Uticaj solarnih panela i skladišta električne energije na gubitke u niskonaponskoj mreži

The largest part of technical losses of electricity in electricity distribution are technical losses in low-voltage lines. Technical losses of electricity are losses that occur in the distribution of electricity from transformers to consumers in electrical distribution equipment. Commercial losses include unauthorized use of electricity and losses due to errors in metering equipment. The main source of technical losses of electricity is caused by the flow of electric current through conductors. Solar panels installed by consumers can affect the reduction of losses by reducing the flow of electric current, i.e. of electricity through low-voltage lines. The introduction of electricity storage (batteries) can have an additional impact on the reduction of electricity losses by using the stored electricity in batteries at times of peak loads. The paper presents the assumed daily diagram of the symmetrical load of the low-voltage line with the calculation of hourly losses without the influence of solar panels and with the influence of solar panels without electricity storage and with electricity storage. The impacts of solar panels without and with electricity storage are also shown in a daily diagram, as well as the voltage values at the end of the low-voltage line. In the calculation, an equivalent line with half the length and full hourly load is used for the low-voltage line.

Implementation and use of electronic patient records in the Brazilian Air Force: a cross-sectional study1.

The objective was to analyze the implementation and use of the electronic patient record in the health services of the Brazilian Air Force. This is a cross-sectional study carried out with 234 physicians, between March and May 2021. The data collection instrument was sent by email. The electronic patient record was implemented in the Air Force approximately 3 years ago (64.5%), and about 81% of the physicians received training to operate it. The most common records involve data related to consultations (90.1%) and interviews with physical examination (67.1%). Physicians cited that information storage (75.6%), agility, and feasibility of recording (55.1%) were the main advantages of the electronic patient record. As disadvantages, problems in electronic equipment (69.7%) and system errors (65%) were reported. Most participants considered that the implementation had a positive impact on work dynamics (75.6%) and productivity (66.7%), mainly regarding the components "Work processes" (57.3%) and "Amount of carried out activities" (21.4%). Keeping records was significantly associated with the job position (p<0.001), type of unit (p=0.008), time of implementation (p<0.001), and participation in training (p=0.028). The implementation of the electronic patient record in the Air Force was recently done, and just over half of the physicians were trained prior to the implementation. The tool is considered compatible with work processes and has a positive effect on productivity.

Partial Multiview Representation Learning With Cross-View Generation.

Multiview learning has made significant progress in recent years. However, an implicit assumption is that multiview data are complete, which is often contrary to practical applications. Due to human or data acquisition equipment errors, what we actually get is partial multiview data, which existing multiview algorithms are limited to processing. Modeling complex dependencies between views in terms of consistency and complementarity remains challenging, especially in partial multiview data scenarios. To address the above issues, this article proposes a deep Gaussian cross-view generation model (named PMvCG), which aims to model views according to the principles of consistency and complementarity and eventually learn the comprehensive representation of partial multiview data. PMvCG can discover cross-view associations by learning view-sharing and view-specific features of different views in the representation space. The missing views can be reconstructed and are applied in turn to further optimize the model. The estimated uncertainty in the model is also considered and integrated into the representation to improve the performance. We design a variational inference and iterative optimization algorithm to solve PMvCG effectively. We conduct comprehensive experiments on multiple real-world datasets to validate the performance of PMvCG. We compare the PMvCG with various methods by applying the learned representation to clustering and classification. We also provide more insightful analysis to explore the PMvCG, such as convergence analysis, parameter sensitivity analysis, and the effect of uncertainty in the representation. The experimental results indicate that PMvCG obtains promising results and surpasses other comparative methods under different experimental settings.