Detection Of Imbalance Research Articles

Functionalized α-Cyanostilbene Derivatives for Detection of Hypoxia or Proteostasis Imbalance in Live Cells.

α-Cyanostilbene represents one of the easily functionalized aggregation-induced emission (AIE) scaffolds. It has been widely adopted for the construction of fluorescent materials for broad applications. Here, we further expanded the utilization of α-cyanostilbene derivatives for the detection of hypoxia or proteostasis imbalance in live cells. Four different amine containing donors were introduced to construct α-cyanostilbene derivatives (R-ASC) with donor-acceptor scaffolds. Equipped with the cysteine (Cys) reactive group, maleimide (MI), R-ASC-MI shows fluorescence turn-on property upon binding with unfolded proteins in vitro and in live cells under proteostatic stress. By virtue of R-ASC-MI, the level of unfolded protein loads in cells can be quantified by flow cytometry, or visualized under microscope. Furthermore, we also characterized the performance of R-ASC-NO2, synthetic precursors of R-ASC-MI, in cellular hypoxia. R-ASC-NO2 revealed upregulated activities of nitroreductase, as well as increased hydrophobicity in live cells, under either chemical (NaN3) induced or atmospheric (1% O2) hypoxia. Together, the advantages of easy modification and high signal-to-noise ratio of new α-cyanostilbene derivatives reported in this work highlight the great potential of α-cyanostilbene in constructing functional biosensors and many other domains.

Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions

Abstract For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc., rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, this research effectively tackles CNN's inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN's hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with adversarial discriminative domain adaptation. The proposed methodology's efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor mass imbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.

Development of an On-Shaft Vibration Sensing Module for Machine Wearable Rotor Imbalance Monitoring

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; it may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices.

Multicenter evaluation of an automated, multiplex, RNA-based molecular assay for detection of ALK, ROS1, RET fusions and MET exon 14 skipping in NSCLC.

The current study assessed the performance of the fully automated RT-PCR-based Idylla™ GeneFusion Assay, which simultaneously covers the advanced non-small cell lung carcinoma (aNSCLC) actionable ALK, ROS1, RET, and MET exon 14 rearrangements, in a routine clinical setting involving 12 European clinical centers. The Idylla™ GeneFusion Assay detects fusions using fusion-specific as well as expression imbalance detection, the latter enabling detection of uncommon fusions not covered by fusion-specific assays. In total, 326 archival aNSCLC formalin-fixed paraffin-embedded (FFPE) samples were included of which 44% were resected specimen, 46% tissue biopsies, and 9% cytological specimen. With a total of 179 biomarker-positive cases (i.e., 85 ALK, 33 ROS1, 20 RET fusions and 41 MET exon 14 skipping), this is one of the largest fusion-positive datasets ever tested. The results of the Idylla™ GeneFusion Assay were compared with earlier results of routine reference technologies including fluorescence in situ hybridization, immunohistochemistry, reverse-transcription polymerase chain reaction, and next-generation sequencing, establishing a high sensitivity/specificity of 96.1%/99.6% for ALK, 96.7%/99.0% for ROS1, 100%/99.3% for RET fusion, and 92.5%/99.6% for MET exon 14 skipping, and a low failure rate (0.9%). The Idylla™ GeneFusion Assay was found to be a reliable, sensitive, and specific tool for routine detection of ALK, ROS1, RET fusions and MET exon 14 skipping. Given its short turnaround time of about 3 h, it is a time-efficient upfront screening tool in FFPE samples, supporting rapid clinical decision making. Moreover, expression-imbalance-based detection of potentially novel fusions may be easily verified with other routine technologies without delaying treatment initiation.

Comparative analysis between data-driven and visual-based approach in drone propeller imbalance detection

Early monitoring of the drone’s critical components, such as the motor, propeller, and electronic speed controller (ESC), can prevent unwanted accidents from happening to the drone. A failure of one of the drone’s components may lead the drone to crash. The fault detection approach can be made by analyzing certain parameters such as vibration, acoustic, current, and speed. In this paper, a comparative analysis between data-driven and visual-based approaches in detecting an imbalanced propeller in the drone is presented. The analysis will focus on vibration and acoustic parameters, with the use of CZN-15 and ADXL335 MEMS sensors to capture vibration and acoustic data, respectively. The Silky EvCam vibration camera will be used for the visual-based approach. Experimental results demonstrated that both data-driven and visual-based approaches could detect the imbalanced UAV propeller. There is a shift of vibration frequency when imbalances occur in the UAV propeller. However, in the ground and thrust modes, the acoustic-based method has an accuracy of 54% and 93%, respectively, which is lower compared to the vibration-based (100% accuracy in both modes) and visual-based techniques.

LSTM Neural Networks Using the SMOTE Algorithm for Wind Turbine Fault Prediction

Abstract This work proposes a method using a long short-term memory neural network as a diagnostic tool to detect wind turbine rotor mass imbalance. The method uses the synthetic minority oversampling technique for data augmentation in an unbalanced dataset. For this purpose, a 1.5 MW three-bladed wind turbine model was simulated at Turbsim, FAST, and Matlab Simulink to generate rotor speed data for different scenarios, simulating different wind speeds and creating a mass imbalance by changing the density of the blades in the software. Features extraction and power spectral density were also used to improve the Neural Network results. The results were compared to nine different classifiers with four different combinations of datasets and demonstrated that the technique is promising for mass imbalance detection.

Detection of Data imbalance in MANET network based on ADSY-AEAMBi-LSTM with DBO Feature selection

<p>A Mobile Ad Hoc Network (MANET) is a temporary wireless network formed by mobile nodes. These nodes cooperate to relay information in a multi-hop fashion, but some malicious nodes can disrupt the network by providing false routing information. Traditional firewalls and encryption methods can’t keep up with the increasing diversity of network threats. To address these issues, Intrusion Detection Systems (IDS) have been developed. In this paper, a new intrusion detection framework named ADSY-AEAMBi-LSTM is introduced. This acronym stands for a bidirectional Long Short-Term Memory (LSTM) model and an adaptive synthetic auto-encoder attention mechanism. The Dung Beetle Optimizer is used to identify optimal features after data preprocessing, followed by classification using ADSY-AEAMBi-LSTM. The study evaluates this model using three datasets: CIC-IDS 2017, UNSW-NB15, and WSN-DS.</p>

Deep learning based prediction of traffic peaks in mobile networks

In mobile networks, it is essential to configure networks more efficiently to provide mobile users with services having better quality. For the adjacent cells, sometimes the mobile traffic concentrates in a single cell, leading to traffic imbalance; in many cases, the detection of imbalance is strongly related to the prediction of traffic peaks, which is extremely difficult because many peaks appear suddenly for no apparent reason. To better predict the peaks and traffic imbalance, we propose two novel mobile traffic predictors. The first is a Mixture of Experts (MoE) model which yields significantly better peak prediction along with excellent interpretability by establishing a cooperation mechanism between different experts, whereas the second predictor is a lightweight Multilayer Perceptron (MLP) which can obtain similar peak forecasting performance but operating more flexibly and consuming less computational power. The obtained predictions are then used to aid the predictive detection of traffic imbalance. To this end, we first perform a large-scale analysis of mobile traffic and then propose different approaches to detect the future traffic imbalance based on the predictions. To evaluate the performance of the proposed approaches, we have conducted extensive experiments on real-world mobile network datasets; all models are implemented in Python 3.8.8 with Pytorch 1.7.0, and they were run on a computer with i7-7700K CPU @ 4.2 GHz running Linux, 64 GB memory and a single Nvidia TITAN X Pascal 12GB GPU. The proposed models are compared with 10 widely used deep learning based predictors, and the results show that our approaches have significantly improved the sensitivity (peak prediction accuracy) from 39.3% to 58.5% (i.e., roughly a 50% increase) along with excellent interpretability. Moreover, the predictions are further used by the predictive detection approach, where the best multi-model approach improves the predictive classification accuracy of congestion (imbalance prediction accuracy) by 10.3% in contrast with the naive approach; this result also verifies the effectiveness of our peak predictions.

Потоки інформації в мережі з обмеженнями кількості потоків, дозволених на кожному вузлі

This paper explores a classic problem in transport network research: the analysis of networks with specified total traffic values for each node. We employ linear algebraic methods to derive a comprehensive set of solutions, ensuring statistical reliability and enabling robust analysis of the results. A mathematical model is presented for determining solution sets in fully connected, loop-free networks with three and four nodes. Based on this model, we developed software to calculate the statistical distribution of entropy values within the network. Furthermore, we investigate the statistical properties of information flow entropy for networks with and without constraints that permit uniform flow distribution. This characteristic holds practical significance for analyzing network dynamics and predicting flow redistribution processes from initial unbalanced states, which inherently proceed towards higher entropy. The findings presented in this paper hold additional practical implications for network imbalance detection and adaptability to diverse network topologies. The results can serve as a foundation for algorithms designed to quantify the degree of network imbalance induced by substantial external influences that do not significantly alter the overall network load. This capability proves particularly valuable in identifying covert DDoS (Distributed Denial of Service) attacks that aim to reduce network bandwidth by supplanting legitimate traffic. While the proposed method has been demonstrated on fully connected networks, it demonstrates potential for adaptation to networks with a wide range of topological structures. This includes networks with partial connectivity or loosely connected networks, which constitute a significant proportion of real-world networks. The significance of the method is further amplified by advancements in cloud computing technologies, which offer substantial computational power and enable the accumulation of extensive statistics regarding information flow distributions across networks of diverse purposes. Such advancements create opportunities for the integration of the developed network analysis technique with machine learning and artificial intelligence technologies, fostering enhanced automation, scalability, and adaptability.

Telemetric long-term assessment of autonomic function in experimental heart failure

Despite medical advances in the treatment of heart failure (HF), mortality remains high. It has been shown that alterations of the autonomic-nervous-system (ANS) are associated with HF progression and increased mortality. Preclinical models are required to evaluate the effectiveness of novel treatments modulating the autonomic imbalance. However, there are neither standard models nor diagnostic methods established to measure sympathetic and parasympathetic outflow continuously. Digital technologies might be a reliable tool for continuous assessment of autonomic function within experimental HF models.Telemetry devices and pacemakers were implanted in beagle dogs (n = 6). HF was induced by ventricular pacing. Cardiac hemodynamics, plasma catecholamines and parameter describing the ANS ((heart rate variability (HRV), deceleration capacity (DC), and baroreflex sensitivity (BRS)) were continuously measured at baseline, during HF conditions and during recovery phase.The pacing regime led to the expected depression in cardiac hemodynamics. Telemetric assessment of the ANS function showed a significant decrease in Total power, DC, and Heart rate recovery, whereas BRS was not significantly affected. In contrast, plasma catecholamines, revealing sympathetic activity, showed only a significant increase in the recovery phase.A precise diagnostic of the ANS in the context of HF is becoming increasingly important in experimental models. Up to now, these models have shown many limitations. Here we present the continuous assessment of the autonomic function in the progression of HF. We could demonstrate the advantage of highly resolved ANS measurement by HR and BP derived parameters due to early detection of an autonomic imbalance in the progression of HF.

Detection of chirality imbalance in photonic Weyl metamaterials with mirror symmetry-breaking

In condensed systems, Weyl quasiparticles have a massless nature and exhibit various relativistic chiral phenomena such as Klein tunneling, chiral anomaly, and Fermi arc surface states. However, in photonic systems, Weyl points (WPs) are protected by the D2d symmetry, often leading to multiple chiral WPs at the same energy level, which makes generating chirality-related effects challenging. To overcome this hinderance, a perturbation that breaks mirror symmetry in the metallic saddle structure was introduced. This perturbation effectively separates the energies of distinct chiral WPs, enabling the experimental measurement of the spectral intensity for each Weyl band and the assessment of chirality imbalance among the WPs. By maintaining time-reversal symmetry, the present study offers an approach for investigating the imbalance in the chirality of pseudo-fermionic fields in photonic materials.

A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft.

Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem's escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.

Prevalence of Electrolyte Imbalance in Patients With Acute Stroke: A Systematic Review.

Electrolyte abnormalities are common in acute stroke patients and have a substantial impact on the course and prognosis of the disease. Electrolyte imbalances such as hyponatremia, hypokalemia, hypocalcemia, hypomagnesemia, and phosphate abnormalities are frequently seen in this patient population. The incidence, root causes, and medical ramifications of electrolyte abnormalities in acute stroke patients are investigated in this comprehensive study. According to our research, hyponatremia is the most prevalent electrolyte imbalance. The most common reason for hyponatremia in stroke patients is the syndrome of inappropriate antidiuretic hormone secretion (SIADH). Higher mortality rates, longer hospital admissions, and less favorable functional outcomes are all linked to hyponatremia. Acute stroke patients also typically experience hypokalemia, which affects the severity of the stroke and the recovery of functional abilities. The review furthermore emphasizes the incidence and clinical consequences of hypercalcemia, hypomagnesemia, hypophosphatemia, and hypocalcemia in patients with acute stroke. The results highlight the significance of early electrolyte imbalance detection and treatment in acute stroke patients. To better comprehend therapeutic approaches, evaluate their influence on stroke outcomes, and analyze prognostic implications, more research is required.

Initial study on an expert system for spine diseases screening using inertial measurement unit

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

Association of Long Noncoding RNA Expression Signatures with Stress-Induced Myocardial Perfusion Defects.

Stress-induced myocardial perfusion defects found in dipyridamole-thallium-201 single-photon emission computed tomography imaging may indicate vascular perfusion abnormalities and risk of obstructive or nonobstructive coronary heart disease. Besides nuclear imaging and subsequent coronary angiography (CAG), no blood test can indicate whether dysregulated homeostasis is associated with stress-induced myocardial perfusion defects. This study investigated the expression signature of long noncoding RNAs (lncRNAs) and genes involved in vascular inflammation and stress response in the blood of patients with stress-induced myocardial perfusion abnormalities (n = 27). The results revealed an expression signature consisting of the upregulation of RMRP (p < 0.01) and downregulations of THRIL (p < 0.01) and HIF1A (p < 0.01) among patients with a positive thallium stress test and no significant coronary artery stenosis within 6 months after baseline treatment. We developed a scoring system based on the expression signatures of RMRP, MIAT, NTT, MALAT1, HSPA1A, and NLRP3 to predict the need for further CAG among patients with moderate-to-significant stress-induced myocardial perfusion defects (area under the receiver operating characteristic curve = 0.963). Therefore, we identified a dysregulated expression profile of lncRNA-based genes in the blood that could be valuable for the early detection of vascular homeostasis imbalance and personalized therapy.

Blade mass imbalance identification and estimation for three-bladed wind turbine rotor based on modal analysis

Early detection of rotor imbalance is one promising approach to address wind turbine durability. This work proposes a novel blade mass imbalance identification and estimation method for the wind turbine rotor. The method is capable of identifying the presence of a wind turbine rotor mass imbalance. In addition, if a mass imbalance exists in the wind turbine rotor, this method can identify which blade is imbalanced, and then quantify the imbalance severity. The remediation to balance the wind turbine rotor is also provided. The advantages of this method include that only a handful of data is required as input variables (which are the frequencies and modes shapes of all first-order modes) and the method can be applied to a stationary (non-rotating) rotor prior to the first operation. The decision-making process of this method is fast and reliable. The performance of the method is illustrated with the help of both numerical simulation and experimental tests. Model to test correlation and direct blade physical measurements are also conducted to validate the ability of the method to both identify mass imbalance and quantify the degree of mass imbalance. This method was demonstrated to be a high-fidelity tool for identifying the imbalance of the wind turbine rotor and it is also applicable to the wind turbines operating in the field. In addition, this method also shows strong potential to be extended to other wind turbine configurations and even other rotating machinery such as propellors and symmetric, repeated structures.

Rotor imbalance detection and diagnosis in floating wind turbines by means of drivetrain condition monitoring

This paper presents a novel approach for detection and diagnosis of the rotor imbalance types pitch misalignment, yaw misalignment and mass imbalance by monitoring the drivetrain vibration response. Traditionally, only SCADA signals including nacelle accelerations, rotor speed and electrical power are utilized for this purpose, while drivetrain condition monitoring signals are mainly used for fault detection in gears and bearings. A diagnostic method is proposed using statistical change detection methods for fault detection, phase angle estimation for localizing the faulty blade, and physics-based decision criteria for fault classification. The proposed method is tested in a numerical case study with aeroelastic and drivetrain multi-body models of the 10 MW DTU reference wind turbine. The results suggest that drivetrain condition monitoring signals are particularly beneficial for detecting and diagnosing pitch misalignment, since this fault type uniquely induces periodic out-of-plane bending moments that excite drivetrain bending modes. Drivetrain signals improved the detection rate of a 1° pitch error from 19% to near 100% and reduced the standard error in locating the faulty blade from 71.5° to 11.2°. In addition, by using drivetrain vibration amplitudes as a decision criterion, all considered pitch error cases are correctly distinguished from other fault types.

Detection of Sodium Ion Imbalance in Human Body Fluids Using an Improved RF Sensor

A novel radio frequency (RF) sensor is designed, developed, and tested to determine sodium ion imbalance in human body fluids. The proposed sensor consists of a complementary split ring resonators (CSRR)-based structure with the sensing region surrounded by air vias to improve the confinement of the E-field within the sensing region. The proposed RF sensor is designed using the full wave EM simulator, the computer simulation technology (CST) Microwave Studio, and fabricated on Rogers 6002 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\rm{\varepsilon }}}_{\mathrm{r}}$</tex-math></inline-formula> = 2.2) having a thickness of 3.75 mm. The various parameters of the designed sensor are optimized for improved performance, and its equivalent circuit model is developed using the ADS software. The proposed sensor confines the field in the sensing area and the test fluid is injected into the sensing region through a specially designed 1.5 mm fluidic channel made across the width of the substrate. This kind of channel embedded in the substrate limits the quantity of fluid interacting with the sensing region thereby facilitating testing of Simulated Body Fluid (SBF) possessing even high-value permittivity in the range of 80–100. The SBF in the present situation is primarily being used to mimic human blood plasma to detect Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> concentrations in a lab environment. The developed RF sensor prototype is successfully utilized to determine the Sodium ion constituent variation in the body fluid, which is an important aspect as Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> concentration affects the metabolic functioning in the body. The measured sensitivity for Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ions in the body fluid is recorded to be 0.03 dB/(mmol/dm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for the fabricated RF sensor.

Rational Resampling Ratio as Enhancement to Shaft Imbalance Detection.

Trend analysis is one of the most powerful techniques for monitoring the technical condition of individual mechanical components of rotating machinery. It is based on extraction of characteristic signal components according to kinetostatic configuration of the machine drivetrain. It has been used for decades and is well-understood. However, classical trend analysis is based on some assumptions which have resulted from the limited computational power of embedded systems years ago. This paper tries to answer a question on whether the assumption of a single signal resampling path for calculation of signal components generated by shafts with rational transmission ratio is valid. The study was conducted using an extensive imbalance test on a medium-power test rig. The paper originally demonstrates that application of an advanced resampling algorithm does not significantly influence the overall trend increase, but it is of utmost importance when trend variance is of interest.

Implementation of a Regional Oxygen Saturation Thought Algorithm and Association with Clinical Outcomes in Pediatric Patients Following Cardiac Surgery.

Near infrared spectroscopy is routinely used in the noninvasive monitoring of cerebral and somatic regional oxygen saturation (rSO2) in pediatric patients following surgery for congenital heart disease. We sought to evaluate the association of a bedside rSO2 thought algorithm with clinical outcomes in a cohort of pediatric patients following cardiac surgery. This was a single-center retrospective cohort study of patients admitted following cardiac surgery over a 42-month period. The intervention was the implementation of an rSO2 thought algorithm, the primary goal of which was to supply bedside providers with a thought aide to help identify, and guide response to, changes in rSO2 in post-operative cardiac surgical patients. Surgical cases were stratified into two 18-month periods of observation, pre- and post-intervention allowing for a 6-month washout period during implementation of the thought algorithm. Clinical outcomes were compared between pre- and post-intervention periods. There were 434 surgical cases during the period of study. We observed a 27% relative risk reduction in our standardized mortality rate (0.61 to 0.48, p = 0.01) between the pre- and post-intervention periods. We did not observe differences in other post-operative clinical outcomes such as ventilator free days or post-operative ICU length of stay. Providing frontline clinical staff with education and tools, such as a bedside rSO2 thought algorithm, may aide in the earlier detection of imbalance between oxygen delivery and consumption and may contribute to improved patient outcomes.