Oscilloscope Research Articles

A clot waveform analysis-based system for differential diagnosis of prolonged activated partial thromboplastin time in plasma samples.

Prolonged activated partial thromboplastin time (aPTT) in plasma samples requires quick and accurate differential diagnosis. We developed two methods using clot waveform analysis (CWA) for plasma samples with prolonged aPTT, particularly for factor (F)VIII deficiency. One method estimates FVIII activity (FVIII:C) using CWA without measuring FVIII:C by template matching. The other utilizes CWA in the mixing test to quickly differentiate FVIII deficiency (FD), FVIII inhibitor (Inh), and lupus anticoagulant (LA). To establish a more accurate system for differential diagnosis of aPTT prolongation, including FIX deficiency, by combining a CWA-based mixing test and template matching. Samples with FD (n = 96), LA (n = 19), and Inh (n = 28) were incubated with normal plasma. FD, LA, and Inh were differentiated using a mixing test, followed by CWA-based template matching. In the mixing test, sensitivity for FD, Inh, and LA was 100%, 93%, and 100%, and specificity was 96%, 100%, and 100%. Samples with FIX inhibitor (> 0.6 BU/mL) were differentiated as the inhibitor sample. In template matching, almost all severe hemophilia A and B were judged as the respective severe types. This novel CWA-based measurement system could aid in differential diagnosis of prolonged aPTT.

Real-Time On-Site Ground-Motion Prediction Using ResNet

Abstract On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023.

An experimental study on applying spatial TDR to determine bentonite suspension penetration

In slurry shield tunnelling, the penetration of the supporting bentonite suspension must be reduced to a critical value to ensure safety and cost-efficiency during construction. Aiming to measure bentonite suspension penetration, this study adopted the spatial time domain reflectometry (spatial TDR) technique. Although traditional TDR can detect point-wise changes in bentonite suspension concentration of pore fluid, this technique has rarely been extended to spatial profile detection. Spatioscale tests with a flat ribbon cable TDR sensor demonstrated the potential of TDR waveform analysis for determining penetration depth. Relationships between penetration depth and waveform characteristics were established. The travel time specified by the dual tangents method decreased with increasing slurry penetration, and the determined travel time agrees well with that calculated by a newly proposed mixture equation. This novel approach enables the determination of penetration depth without visual observation, providing a powerful measuring solution for laboratory studies and slurry shield tunnelling.

Correlation between antihypertensive drugs and cerebral hemodynamic parameters: insights from observational findings using transcranial Doppler.

Antihypertensives (AHD) can influence cerebral autoregulation (CA) and attenuate hypertrophic concentric remodelling of arterioles. The aim of this study was to examine the associations between AHD, CA and structural and functional properties of cerebral arteries. In this observational, cross-sectional study 115 volunteers were divided in group 1 (non-hypertensive) [n = 30]; group 2 (hypertensive with systolic blood pressure [SBP] < 140 and diastolic blood pressure [DBP] < 90 mmHg) [n = 54]; group 3 (hypertensive with SBP ≥ 140 or DBP ≥ 90 mmHg) [n = 31] and simultaneous measurements of systemic blood pressure (BP) and middle cerebral artery blood flow velocity (CBFV) were obtained from digital plethysmography and transcranial Doppler. Beat-to-beat, critical closing pressure (CrCP), resistance-area product (RAP) and autoregulation index (ARI) values were extracted by linear regression analysis of instantaneous BP and CBFV waveforms using computerised analysis. Pulsatility index (PI) was calculated and CO2 reactivity was assessed by the breath-holding test. Despite their higher RAP (1.7 [±0.7], p < 0.001) compared to groups 1 and 2, uncontrolled hypertensive using diuretics (p = 0.047) and α2-agonists (p = 0.009) had significantly lower PI. Impaired CO2 reactivity was common between the two hypertensive groups (p = 0.008), however ARI, CrCP and CBFV did not differ between them and non-hypertensive individuals and also did not correlate with any AHD used. Unlike the RAP, PI does not seem to reflect the real cerebrovascular resistence resulting from chronic arterial remodelling. Despite impaired CO2 reactivity, hypertensivehavearterial tonus and CA comparable to non-hypertensive. Experimental studies involving an untreated hypertensive control group are required to robustly make definitive conclusions about these questions.

A systematic PAPR analysis of optical 5G waveforms using advanced and deep learning PAPR algorithms: estimation of PAPR, PSD, and BER

A systematic PAPR analysis of optical 5G waveforms using advanced and deep learning PAPR algorithms: estimation of PAPR, PSD, and BER

Mouth Leak is Associated with Sleep Fragmentation During Nasal CPAP Treatment of OSA and May Be Detected by Leak Waveform Analysis.

Mouth air leak is a major cause of low adherence to nasal CPAP in patients with obstructive sleep apnea (OSA). However, CPAP reports do not distinguish mouth from mask leak. We hypothesized that mouth air leak is terminated abruptly by an arousal from sleep and mouth closing that can be detected by CPAP leak waveform analysis. Describe patterns of mouth air leak waveform during polysomnography (PSG) in patients with OSA treated with well-fitted nasal CPAP. PSG recordings with a jaw motion sensor to detect mouth opening were performed in OSA patients treated with nasal CPAP with suspected mouth air leak. Careful mask fitting and visual inspection excluded mask leak. Mouth leak episodes were characterized by an increase (≥ 20%) above the intentional leak. Leak episodes were classified as intermittent (< 5 minutes) or continuous (≥ 5 minutes). Twenty patients (80% men; age: 63±11 years; body mass index: 29.9±6 kg/m2; baseline apnea hypopnea index: 46.9±19 events/h) treated with nasal CPAP and documented mouth leak completed the study. All but one patient experienced an overlap of intermittent and continuous mouth leak. Most mouth leak episodes ended with mouth closure (97.7%) and an arousal (52.7%) or awakening (38.6%). Only 34.9% of the leak episodes were associated with respiratory events. Intermittent mouth air leak was more common in sleep stages N1+ N2 (P<0.01), while continuous leak was more common in sleep stage N3 (P<0.01). Continuous episodes of air leak were associated with a higher amplitude of mouth opening. CPAP report waveform was able to detect only 29.6% of the leak episodes detected by PSG. Only 10 patients (50%) had a high unintentional leak according to the criterion adopted by ResMed and only 2 patients (10%) presented large leak according to the Philips criterion. Intermittent and continuous mouth leak during nasal CPAP frequently co-exist and contribute to sleep fragmentation. Identification of leak waveform patterns may help detect mouth air leak which in turn is an important cause of poor CPAP adherence.

Meta-analysis on QEEG Changes to Antidepressant Treatment Among Patients with Depression

Introduction: Diagnostic and treatment accuracy of depression can lead to a better and possibly earlier response and remission in patients. The literature, though scanty, seems to suggest that quantitative electroencephalography (QEEG) can predict the outcome of antidepressant effects. Methodology: Articles published between January 1990 and July 2019, including those dealing with QEEG recordings before and after the initiation of antidepressant medication, were included. The pooled effect size and subgroup analysis of waveforms were calculated to predict response to antidepressants. Result: In all, 572 results were retrieved from the searches, of which 20 studies were included. Pooled data using a random-effects model (REM) calculated an effect size of 0.80 (95% CI [0.64–0.97]). Heterogeneity of the sample was low with Tau² = 0.02; df = 18 ( P = .30); I² = 12%. Moreover, subgroup analysis showed that theta band frequencies were better at predicting response than alpha band frequencies (the standard mean difference [SMD] for theta was 0.91 compared to 0.68 for alpha waves). Conclusions: QEEG is a valuable predictor of the antidepressant response. Among the EEG frequencies, the theta band showed the most significant change with treatment.

The in vitro cross-reactivity and blood coagulation potential of recombinant porcine factor VIII in Japanese patients with acquired hemophilia A.

Recombinant porcine factor VIII (rpFVIII) is a hemostatic agent for acquired hemophilia A (AHA). Cross-reaction of auto-antibodies against rpFVIII has been reported, although no data are available in Japanese patients. This study investigated the cross-reactivity and coagulation potential of rpFVIII in plasma samples from Japanese patients with AHA. Cross-reactivity was calculated as the ratio of anti-porcine FVIII inhibitor titer (pFVIII-INH) to human FVIII inhibitor titer. Comprehensive coagulation potential was assessed by clot waveform analysis (CWA) and thrombin generation assay (TGA) in samples spiked with rpFVIII (equivalent to 200 U/kg). Nine of 16 plasma samples (56.3%) had positive pFVIII-INH, with a median cross-reactivity of 1.2%. FVIII activity (FVIII:C) was restored to > 100% in all samples upon spiking with rpFVIII, but was weakly correlated with pFVIII-INH. CWA parameters and most TGA parameters were restored to normal upon spiking with rpFVIII; correlation of these parameters with FVIII:C was similar to that observed in controls. Overall, cross-reactivity to rpFVIII in Japanese patients was similar to that reported in Caucasian patients. Our results suggest that an initial clinical dose of 200 U/kg rpFVIII could restore coagulation potential to normal, and that FVIII:C monitoring after rpFVIII administration may be more informative than pFVIII-INH before administration.

Comparative analysis of SWIFT and Grid-SWIFT waveforms for improved ion excitation and isolation resolution

Miniaturized mass spectrometers, with their increasing portability and performance enhancements, play a crucial role in selectively analyzing target compounds within complex mixtures, particularly in situ environments. As part of our ongoing efforts to enhance the analytical capabilities of the custom-built "brick" mass spectrometer, this study compared and optimized methods for ion isolation using Stored Waveform Inverse Fourier Transform (SWIFT) and Grid-SWIFT waveforms. The efficacy of the SWIFT and Grid-SWIFT waveforms in selective ion isolation was investigated, and through optimization of both waveforms, improved ion isolation efficiency, and peak resolution were demonstrated by adjusting waveform duration and amplitude. The results conclusively showed that Grid-SWIFT outperforms traditional SWIFT waveforms in enhancing the isolation of target ions. Additionally, a novel simulation-based approach was developed to simulate the behavior of ion trajectories under the SWIFT and Grid-SWIFT waveforms within LIT to provide a theoretical basis for better performance of the Grid-SWIFT waveforms. Results show that longer waveform durations help reduce energy leakage, providing higher isolation efficiency. Grid-SWIFT waveforms exhibited significantly less energy leakage compared to SWIFT waveforms. Consequently, using Grid-SWIFT as isolation waveforms improved the mass spectra' intensity and resolution.

Design and Implementation of a 7-Person Voting Device based on Quartus

As the complexity of digital system design continues to increase, efficient and precise decision support systems have become increasingly important. This paper presents the design and implementation of a 7-voter decision system based on Altera’s Quartus software. The voter is applied in the core decision-making process to ensure the accuracy and efficiency of decisions. The study begins by outlining the design principles of the 7-voter decision system, using a design philosophy that minimizes timing delays and optimizes hardware resource utilization. A complex majority voting logic circuit is implemented using high-level hardware description languages (VHDL/Verilog), ensuring the advanced nature and applicability of the voting circuit. In terms of the logical implementation of the voting circuit, the paper conducts an in-depth analysis of the optimization of combinational and sequential logic and standardizes the input and output to enhance the signal stability and the system’s resistance to interference. On the Quartus platform, the design process of the voting system is elaborated in detail, with careful consideration given to boundary conditions and hardware compatibility. The implementation of the voter has undergone rigorous simulation and testing. Strict parameter setting for simulations and waveform analysis confirm the correctness of the circuit, and fault simulation tests further confirm its reliability. Performance indicators such as response time and percentage of resource utilization have been quantitatively analyzed and meet the design objectives set beforehand. Overall, this research not only realizes a highperformance 7-voter decision system but also provides an effective case study for applications on the high-performance Quartus design platform. Future research can build on the current work to further optimize algorithms and hardware, explore the potential for the application of the voter in a broader range of fields, and adapt to the new features of the Quartus platform to achieve more powerful decision support functions.

Advanced waveform analysis of the electrocardiogram signals using complementary signal processing techniques to investigate the response to a QTc prolonging drug vs control

Abstract Background Thorough QTc (TQT) studies using Electrocardiogram (ECG) data, are a well-established method for testing the pro-arrhythmic propensity of drugs performed during safety studies as part of drug development. ECG analysis is currently reduced to simplified biomarkers, such as rate, intervals and amplitudes. However, ECG waveforms are complex and generally sampled with high fidelity (125-1000 Hz). Our novel mathematical method, Symmetric Projection Attractor Reconstruction (SPAR) analyses the morphology and variability of such waveforms, using every data point to generate a new, faithful 2D waveform representation termed an ‘attractor’. Moxifloxacin is a reversible blocker of the rapid component of the delayed rectifier, potassium current of the cardiac IKr potassium channel and has been shown to cause a mean increase of the QTc interval of 10–14 ms after an oral single dose of 400 mg. Separately, studies have shown that meals can alter QT interval as well as altering the pharmacokinetic profile of moxifloxacin itself with gender and ethnicity impacting individual responses. In-depth knowledge of these physiological factors could support safer medicines development and clinical use. Purpose This project investigated the feasibility of SPAR analysis of ECG signals to detect drug induced changes which a higher degree of sensitivity, to support safety evaluation during medicines development. Methods High resolution triplicate 12 lead ECG time-series data from a Phase one study of healthy human volunteers cross over of placebo, moxifloxacin or moxifloxacin + food was retrospectively analysed using SPAR. Using a bespoke software tool was used to perform SPAR analyses to transform ECG time-series into corresponding attractors. Corresponding single point measurements were previously obtained as part of the original Phase one study. Results Initial evaluation of attractors revealed high inter-individual variation, consistent with previous SPAR ECG analysis. Nevertheless, we identified changes in subjects receiving Moxifloxacin (six hour time point) when compared to those receiving placebo control. These changes were not as evident in previously obtained QT intervals. Conclusion This pilot study illustrates that SPAR is highlighting changes on the ECG that are less obvious from QT data alone. Further evaluation of the whole dataset to identify generalisable SPAR features, and their comparison with existing markers is now necessary. The individualised nature of ECGs and corresponding attractors indicates the method may have highest utility in personalised medicine, evaluating each individual’s change and hence individual risk to drug induced arrhythmia.Visualisations of SPAR analysis of ECGs

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model.

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients' impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss. We have previously combined feature extraction and machine learning methodologies applied to arterial waveform analysis to develop advanced metrics that have enabled the early and accurate detection of impending shock in a canine model of hemorrhage, including metrics that estimate blood loss such as the Blood Loss Volume Metric, the Percent Estimated Blood Loss metric, and the Hemorrhage Area metric. Importantly, these metrics were able to identify impending shock well before traditional vital signs, such as blood pressure, were altered enough to identify shock. Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. Based on the performance of these advanced metrics, we conclude that the framework for developing these metrics in the previous canine model remains applicable when applied to a swine model and results in accurate performance in these advanced metrics. The success of these advanced metrics in swine, which share physiological similarities to humans, shows promise in developing advanced blood loss metrics for humans, which would result in increased positive casualty outcomes due to hemorrhage in civilian and military medicine.

Understanding perioperative risk determinants in carotid endarterectomy: the impact of compromised circle of Willis morphology on inter-hemispheric blood flow indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns.

Carotid artery stenosis (CAS) often requires surgical intervention through carotid endarterectomy (CEA) to prevent stroke. Accurate cerebrovascular risk assessments are crucial in CEA, as poor collateral circulation can lead to insufficient interhemispheric blood flow compensation, resulting in ischemic complications. Therefore, understanding perioperative risk determinants is vital. This study aims to determine the impact of compromised circle of Willis (CoW) morphology on inter-hemispheric blood flow, focusing on indices based on intraoperative internal carotid artery stump pulse pressure and backflow patterns. In 80 CAS patients who underwent CEA, preoperative CT angiography for CoW was conducted. Patients were categorized into five subgroups based on their CoW anatomy and three additional groups based on intraoperative internal carotid artery (ICA) stump backflow patterns evaluated by the surgeon. Continuous blood pressure signals, including systolic, diastolic, mean, and pulse pressure values, were recorded during the procedure. The relationship between CoW anatomical variants and the systolic and diastolic segments of the averaged pressure waveforms, particularly diastolic pressure decay, was analyzed. The correlation between CoW anatomy and stump backflow intensity was also examined. Significant variability in ICA stump backflow and pressure values was evident across CoW variants. Patients with compromised CoW morphology exhibited weaker backflow patterns and lower ICA stump pulse pressure values, consistent with impaired interhemispheric blood flow. Notably, ICA stump diastolic pressure decay was consistent across most CoW variant groups, indicating developed collateral circulation in cases with CoW anomalies. Thus, impaired CoW integrity is associated with compromised interhemispheric blood flow indices based on intraoperative ICA stump pulse pressure and backflow patterns during CEA. Integrating intraoperative pulse waveform analysis with preoperative CT angiography provides a more detailed assessment of cerebrovascular risk, guiding the selective use of shunts. This combined approach may improve surgical outcomes and patient safety by identifying patients at increased risk of perioperative neurological events due to CoW anomalies.

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Spaceborne particle identification platform and its application based on convolutional neural network

The identification of particle types in space radiation particle detection is a problem of great concern in scientific research and engineering applications. At present, the particle types are mainly identified by the difference in energy deposition of particles in the sensor, the difference in waveform in the sensor, the flight time and energy of particles, and the difference in the deflection path of particles in the electric field, such as traditional telescope detectors, particle pulse waveform analysis, flight time TOF system and electrostatic analyzer. However, the current on-orbit particle identification logic is relatively simple and the identification accuracy is limited. Convolutional neural networks have powerful target classification capabilities and are good at capturing and extracting target feature details, which can improve the accuracy of particle energy measurement and identification. Based on the environment commonly used in space environment detection payloads, this paper proposes a method for building an on-orbit convolutional neural network particle identification platform to achieve particle type identification. The platform first constructs a multi-dimensional input data set, completes the model training and weight derivation with the help of the software platform, and completes the waveform inference and data set expansion through the hardware platform. The established identification platform is used to train and test the neutron and gamma waveform data obtained in actual tests, and the accuracy of the identification of the software and hardware platforms is analyzed, completing the verification of the platform. The establishment and application of this platform provides a new idea and method for particle measurement and identification in future space environment detection, and has strong engineering practice significance.

Waveform Analysis To Identify Biomechanical Relationships and Differences Between Softball Pitchers With and Without Pain.

Softball pitchers accrue high rates of injury. Research suggests certain mechanics at discrete pitch events are related with pain. Here, we examine relationships between peak throwing shoulder kinetics and trunk/pelvis kinematics and compare trunk/pelvis kinematics between pitchers who were healthy and those currently experiencing pain. (1) Peak shoulder kinetics would be positively related to greater trunk and pelvis flexion, lateral flexion, and rotation; and (2) pitchers in pain would exhibit greater trunk and pelvis flexion, lateral flexion, and rotation during the pitch than those who were pain-free. Cross-sectional study. A total of 42 high school pitchers (height, 1.71 ± 0.06 m; weight, 75.0 ± 15.9 kg; age, 16 ± 2 years) were separated into 2 groups based on presence or absence of pain. Peak kinetic data from 3 pitches per pitcher were averaged and used as dependent variables. Kinematic data were averaged across 3 trials, and time normalized to 101 datapoints between foot contact and follow-through of the pitch. Statistical parametric mapping regressions were used to assess the relationships between peak shoulder kinetics and waveform of trunk and pelvis kinematics. Pelvic lateral tilt significantly predicted peak throwing shoulder superior (P = 0.05) and lateral (P = 0.04) force. Pelvis rotation predicted peak superior force (P = 0.02). Waveform analyses revealed no waveform differences between healthy pitchers and those currently experiencing pain. Peak shoulder kinetic variables are related with pelvic positioning during the pitch; however, trunk and pelvis kinematics do not differ according to presence of pain. Pitchers in pain do not adopt specific trunk and pelvic alterations during the pitch, potentially concealing the effects of pain from visual identification. Coaches and clinicians need to discuss health status with pitchers versus relying on visual observations to understand pain and injury risk.

Comparison of Web-Based and On-Site Lung Simulators for Education in Mechanical Ventilation.

Training in mechanical ventilation is a key goal in critical care fellowship education. Web-based simulators offer a cost-effective and readily available alternative to traditional on-site simulators. However, it is unclear how effective they are as teaching tools. In this study, we evaluated the test scores of fellows who underwent mechanical ventilation training by using a web-based simulator compared with fellows who used an on-site simulator during a mechanical ventilation course. This was a nonrandomized controlled trial conducted as part of a mechanical ventilation course that involved 70 first-year critical care fellows. The course was identical except for the simulation technology used. One group of instructors used a traditional on-site simulator, the ASL 5000 Lung Solution (n = 39). The second group was instructed in using a web-based simulator, VentSim (n = 31). Each fellow completed a pre-course test and a post-course test by using a validated, case-based ventilator waveform examination that consisted of 5 questions with a total possible score of 100. The primary outcome was a comparison of the mean scores on the posttest between the 2 groups. The study was designed as a non-inferiority trial with a predetermined margin of 10 points. There was no significant difference in the mean ± SD pretest scores between the web-based and the on-site groups (21.1 ± 12.6 and 26.9 ± 13.6 respectively; P = .11). The mean ± SD posttest scores were 45.6 ± 25.0 for the web-based simulator and 43.4 ± 16.5 for on-site simulator (mean difference 2.2; one-sided 95% CI -7.0 to ∞; P non-inferiority = .02 [non-inferiority confirmed]). Changes in mean ± SD scores (posttest - pretest) were 25.9 ± 20.9 for the web-based simulator and 16.5 ± 15.9 for the on-site simulator (mean difference 9.4, one-sided 95% CI 0.9 to ∞; P non-inferiority < .001 [non-inferiority confirmed]). In the education of first-year critical care fellows on mechanical ventilation waveform analysis, a web-based mechanical ventilation simulator was non-inferior to a traditional on-site mechanical ventilation simulator.

Highwall mining productivity enhancement with energy conservation: A case study

ABSTRACT Open-cast mining in India is transitioning from closing existing mines to extracting remaining coal seams from dormant sites. Highwall machines are key to this efficient extraction process. To meet international safety and environmental standards, audits are essential. This article evaluates the power quality of the substation’s incomer, transformer, and feeder, and proposes energy-efficient changes to boost production. Tests on the main substation transformers included voltage, current, power, power factor, harmonics, and AC waveform analysis. Thermal imaging of electrical equipment was conducted to verify operating temperatures. Results were graphed for each test. The study recommends installing an Automatic Power Factor Correction (APFC) unit and lowering the tap value from 5 to 4 to improve the transformer’s power factor. It is estimated that a 4% voltage reduction will save 1% energy, while a 5% reduction will save 1.25% energy.

Exploring waveforms with non-GR deviations for extreme mass-ratio inspirals

The fundamental process of detecting and examining the polarization modes of gravitational waves plays a pivotal role in enhancing our grasp on the precise mechanisms behind their generation. A thorough investigation is essential for delving deeper into the essence of gravitational waves and rigorously evaluating and validating the range of modified gravity theories. In this line of interest, a general description of black holes in theories beyond general relativity can serve a meaningful purpose where distinct deviation parameters can be mapped to solutions representing distinct theories. Employing a refined version of the deformed Kerr geometry, which is free from pathological behaviours such as unphysical divergences in the metric, we explore an extreme mass-ratio inspiral system, wherein a stellar-mass object perturbs a supermassive black hole. We compute the effects of deformation parameters on the rate of change of orbital energy and angular momentum, orbital evolution and phase dynamics with leading order post-Newtonian corrections. With the waveform analysis, we assess the plausibility of detecting deviations from general relativity through observations facilitated by the Laser Interferometer Space Antenna (LISA), simultaneously constraining the extent of these deviations. Therefore, this analysis provides an understanding while highlighting the essential role of observations in advancing gravitational phenomena beyond general relativity.

Reverse triggering in patients with acute brain injury undergoing controlled ventilation.

Reverse triggering (RT) is a ventilatory asynchrony characterized by the activation of respiratory muscles in response to passive mechanical insufflation. Although RT can potentially exacerbate lung injury, its characteristics in patients with acute brain injury remain under-explored. This study aims to elucidate the incidence and factors associated with RT in this patient population. A retrospective analysis was conducted using a clinical database dedicated to investigating patient-ventilator asynchrony among individuals with acute brain injury. Only patients under controlled mechanical ventilation were included. RT was identified through an analysis of airway pressure, flow, and esophageal pressure waveforms collected at 15-minute intervals. The incidence of RT was determined by calculating the ratio of the number of RT breaths to the total number of breaths. A total of 334 waveform datasets from 53 patients were analyzed. RT was observed in 8.4% of mechanical insufflations across 59 datasets (17.7%). Sixteen patients (30.2%) experienced at least one RT event. The most prevalent phenotype was mid-cycle RT (61.1%), followed by breath stacking (BD) (16.6%). Independent predictors of RT, after adjusting for confounding factors, included the combined use of opioids and sedatives, lower Sedation-Agitation Scale (SAS) scores, reduced airway delta pressure, and minimal discrepancies between the set respiratory rate and the actual respiratory rate. The pressure of occlusion at 0.1 seconds (P0.1) demonstrated substantial predictive ability for BD, with an area under the receiver operating characteristics curve of 0.72 (95% confidence interval: 0.64-0.80, P<0.001); the optimal cutoff was determined to be 1.69 cmH2O, achieving 83.3% sensitivity and 67.1% specificity. Factors such as deep sedation, lower airway delta pressure, and close alignment of ventilator and patient respiratory rates were associated with RT in patients with acute brain injury. Additionally, P0.1 served as a reliable predictor for the occurrence of BD.