Early Failure Detection Research Articles

Serum metabolomics improve risk stratification for incident heart failure

Abstract Background The prediction and early detection of heart failure (HF) is vital to reduce its substantial impact on quality of life, survival and healthcare expenditure. Current incident HF risk stratification strategies achieve only modest performance. Moreover, state-of-the-art risk scores focus on commonly acknowledged clinical risk factors, thus necessitating the integration of heterogenous data sources and prioritizing individuals with obvious risk constellations. Purpose Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. Methods Leveraging data of n = 68,311 individuals and > 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort, we (I) evaluated the association of individual metabolites with incident HF via fitting of per-metabolite COX proportional hazards models and (II) trained and validated elastic net (EN) models to predict incident HF using the serum metabolome. Discriminative performance was benchmarked against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF). External validation was conducted in the independent FINRISK study. Results Several metabolites were independently associated with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈ 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (Harrel’s C: 0.768 vs. 0.755, ΔC = 0.013 (95% confidence interval (CI) 0.004 – 0.022), continuous net reclassification improvement (NRI) = 0.287 (95% CI 0.200 – 0.367), relative integrated discrimination improvement (IDI): 17.47 % (95% CI 9.463 - 27.825)). Simplified models only including age, sex and metabolomics performed almost as well as the PCP-HF model (Harrel’s C: 0.745 vs. 0.755, ΔC = 0.010 (95% CI -0.004 - 0.027), continuous NRI: 0.097 (95% CI -0.025 - 0.217), relative IDI: 13.445 % (95% CI -10.608 - 41.454)). Risk and survival stratification was improved by integrating metabolomics. External validation within FINRISK revealed similar patterns using the original model coefficients. Conclusions Serum metabolomics improve the prediction of incident HF risk. Scores obtained from the combination of age, sex and metabolomics exhibit similar predictive power as clinical risk models. Offering serum metabolomics to a middle-aged cohort might significantly improve HF risk stratification, thus facilitating preventive efforts. Via a single blood draw, serum metabolomics displays a highly cost- and time-effective, standardizable, and scalable alternative to clinical risk scores involving physical measurements and history taking in addition to clinical chemistry.Relative performance (test split)ROC & DCA plots (test split)

Contribution to Conditional Maintenance by Vibration Analysis of Rotating Machine Mechanical Failures and Proposed Solutions

Objective: The aim of this study is to analyze the Cement Separator machine and identify potential condition indicators through vibration measurements and analysis. The goal is to diagnose and repair the machine before any material damage occurs. Background: Maintenance Technology: Modern maintenance technologies are crucial for monitoring equipment performance and planning timely maintenance interventions. Vibration Monitoring: As a key component in maintenance programs, especially for mechanical systems, vibration monitoring provides early warnings of faults based on the actual state of the machine. Cement Separator Machine: Issue: The cement separator exhibits significant vibrations despite its low operating speed. Data Sources: The construction and operational details of the machine are available from its operating manual and vibration measurements. Methodology: Practical Vibration Measurements: Extensive measurements are performed in a research laboratory and on-site to monitor the machine's condition. Vibration measuring devices are used to monitor rotating machines at specified intervals according to their condition and international standards. Condition Indicators: Various condition indicators are monitored to detect possible deterioration and the onset of mechanical or electrical failures during the machine's operation. Experimental Studies: Practical experiments validate the theoretical and numerical findings and refine the vibration analysis approach. Strategy: Adequate scheduling of interventions and repairs based on the machine's condition. Continuous monitoring through vibration analysis devices. Expected Outcomes: Early detection of potential mechanical or electrical failures. Sustainable solutions to reduce vibrations and enhance the machine's performance. Improved maintenance scheduling to prevent material damage and extend the machine's lifespan. By integrating vibration monitoring and analysis with practical studies, this research aims to provide a comprehensive diagnosis and repair strategy for the cement separator machine.

Early diagnosis and revision repair of lesser tuberosity osteotomy failure after anatomic total shoulder arthroplasty

BackgroundLesser tuberosity osteotomy (LTO) in total shoulder arthroplasty (TSA) has a relatively high success rate owing to bone-to-bone healing and modern repair techniques. Failure of the LTO repair has been associated with poorer outcomes, decreased patient satisfaction, and the potential need for revision arthroplasty. Despite this, few studies closely examined outcomes for revision LTO repair in the setting of prior TSA. MethodsA retrospective review of all TSAs performed by a single surgeon from 2016 until 2023 was performed to identify all cases with LTO failure who underwent acute revision LTO repair. Baseline characteristics, postoperative complications including infection, need for revision surgery, and time to revision surgery were collected and reported on all TSA patients. All patients were followed for a minimum of 1 year after their TSA and revision LTO fixation if required. Subgroup analysis was performed on the LTO revision cohort to evaluate for potential risk factors. ResultsA total of 166 patients underwent TSA with LTO. Of these, eight patients were diagnosed with an acute LTO failure at a mean of 5.7 weeks after surgery and underwent revision LTO fixation at a mean of 6.3 weeks after surgery. At a mean follow-up of 147.9 weeks (standard deviation: 90.3; range: 52.1-284.9), seven of eight patients (87.5%) demonstrated healing of their LTO and had no complaints, complications, or reoperations at the latest follow-up. One patient developed a periprosthetic infection requiring two-stage revision. No patients in the overall cohort underwent revision shoulder arthroplasty due to subscapularis failure. ConclusionEarly identification of subscapularis failure is important to optimize outcomes of revision fixation. Use of LTO for subscapularis repair is beneficial by allowing early radiographic detection of subscapularis failure. Revision LTO repair can be successful when performed acutely and appears to minimize the need for revision shoulder arthroplasty due to subscapularis failure.

Detectable Adhesives: Nondestructive Detection of Adhesion

AbstractMetal‐to‐metal adhesives are vital across diverse sectors including automotive, aerospace, and industrial assembly. However, traditional adhesion detection methods are mainly based on the fracture mechanism, posing challenges for nondestructive detection. Here, a nondestructive method that leverages electrical signals to monitor the adhesion state of metal interfaces in real‐time is presented. The approach utilizes detectable adhesives (DA) synthesized from poly(ionic liquid) (PIL), which not only exhibit robust adhesion properties (4.9 MPa) but also function as ionic conductors. By correlating changes in capacitance and resistance with the adhesive state, the method allows for in situ monitoring of the curing process, prediction of adhesion strength, and early detection of potential failures. This dual‐sensing capability, combining electrical and mechanical aspects, enhances understanding of adhesive behavior in diverse conditions, presenting a fresh approach for digitally transforming adhesive technologies.

Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques.

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.

T2-LSTM-Based AI System for Early Detection of Motor Failure in Chemical Plants

In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. The rapid development of artificial intelligence provides powerful tools for early fault detection and maintenance. In this study, the Hotelling T2 index is used to calculate the root mean square values of the normal motor’s x, y, and z axes. A long short-term memory (LSTM) model creates a trend model for the Hotelling T2 index, determining an early warning threshold. Current anomaly detection follows the ISO 10816-1 standard, while future anomaly prediction uses the T2-LSTM trend model. Validated at a chemical plant in Southern Taiwan, the method shows 98% agreement between the predicted and actual anomalies over three months, demonstrating its effectiveness. The T2-LSTM model significantly improves the accuracy of motor fault detection, potentially reducing economic losses and improving safety in the chemical industry. Future research will focus on reducing false alarms and integrating more sensor data.

Enhanced algorithm for predictive maintenance to detect turbocharger overspeed in diesel engine rail vehicles

The reliability and safety of locomotives is crucial for efficient train operation. Repeated turbocharger failures in Israel Railways locomotive fleet have raised serious safety concerns. An investigation into the failures revealed that the uncontrolled acceleration and overspeed transients of the turbocharger shaft occurred before the failure. Early detection of potential turbocharger failures by predicting overspeed conditions is critical to the safety and reliability of locomotives. In this study, an enhanced novel algorithm for estimating the Instantaneous Angular Speed (IAS) of the turbocharger and diesel engines is presented to overcome the challenges of transient operating conditions of diesel engines. Using adaptive dephasing, the algorithm effectively isolates critical asynchronous vibration components that are crucial for the early detection of turbocharger failures. This algorithm is suitable for non-stationary speeds and is applicable to any range of rotational speed and rate of change. The algorithm requires the input of the basic parameters of the system, while all other parameters that control the process are determined automatically. The algorithm was developed specifically for the special operating conditions of diesel engines and improves predictive maintenance and operational reliability. The method is robust as it correlates between several characteristic frequencies of the rotating parts of the system. The algorithm was verified and validated with simulated and experimental data.

Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment

Purpose This study aims to evaluate the efficacy of various machine learning models in predicting heart failure incidence using medical data, focusing on the innovative aspect of a novel dataset. Unlike previous studies that predominantly examined features such as smoking status or age, this research explores novel features. The primary challenge addressed is the utilization of these new features, coupled with machine learning techniques, to accurately diagnose heart failure. Methods Five machine learning models, including logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors, were applied to analyze medical data from a dataset comprising over 900 individuals. The dataset encompasses diverse parameters such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, and electrocardiogram results, introducing a novel approach to feature selection. Results The evaluation of machine learning models unveiled varying performances in predicting heart failure. Logistic regression and support vector classifier exhibited the highest accuracy of 88%, followed by the decision tree classifier (below 85%), random forest classifier (84%), and K-nearest neighbors (82%). Additionally, the analysis revealed a balanced dataset distribution and highlighted sex-based disparities in heart failure incidence, along with significant correlations with factors such as age, chest pain severity, blood glucose levels, and physical activity. Conclusions The findings underscore the potential of integrating multiple machine learning models for early detection of heart failure, leveraging the inclusion of novel features in the dataset. However, careful model selection is crucial to account for discrepancies in accuracy among different models, emphasizing the importance of tailoring approaches based on specific project requirements.

COMPARISON OF CORONA DISCHARGE IDENTIFICATION IN 20 kV CUBICLES BASED ON VOLTAGE AND NOISE USING ED, HMM, AND FCM

Phenomena such as corona discharge (CD) still occurs in many electrical systems in Indonesia. As a first step for early detection of insulation failure. Identification of CD acoustic in this study namely clustering based on voltage and based on noise. So that the CD acoustic classification is set into 3 clusters. In addition, this study also classifies CD acoustic based on noise with three clusters, namely pure CD, CD with noise, and pure noise. Clustering was performed using the linear predictive coding (LPC) method as feature extraction, then a comparison of pattern matching results of feature extraction was performed using Euclidean distance (ED), hidden Markov model (HMM) and fuzzy cluster mean (FCM). The temperature in the cubical is between 27.5 ℃ - 35.3 ℃ and humidity ranges from 70% - 95%. The results of clustering accuracy on the average base voltage using the ED, HMM and FCM methods were obtained respectively 100%, 100% 93.93% for training data and 80.74%, 84.44%, 80.55% for testing data. While the results of the average base noise clustering accuracy using the ED, HMM and FCM methods were obtained respectively 100%, 100%, 94.69% for training data and 100%, 100%, 100% for testing data.

A Reduced-Order Model of the Coupled Venting-Thermal-Electrochemical Behavior to Predict First Venting Under External Short Circuit Conditions

Modeling abusive battery operation before thermal runaway (TR) can reduce the testing requirements and facilitate the development of effective and reliable early failure detection and fast discharge strategies [1] which require multiple types of sensor signals, including current, voltage, temperature, and pressure. We present a multi-physics model that can predict the rapidly evolving discharge behavior of cells undergoing an external short circuit, including hazards like first venting. First venting models account for gas generation due to SEI decomposition and electrolyte vaporization at high temperatures to calculate the build-up of internal cell pressure under constant-displacement conditions [2]. Modeling gas generation requires accurate temperature prediction, which subsequently requires accurate Joule heating calculated from current and voltage predictions. From the cascading chain of model input dependencies, the first challenge is managing error propagation through the submodels with appropriate parameterization. Previous modeling work parameterized a venting model for a cell undergoing an external short circuit (ESC) but did not include a thermal or electrical model [2]. The second challenge is modeling the diffusion-limited discharge behavior with currents approaching 50-80 C with a fast and accurate model that is feasible for detection and control applications. Diffusion-limited behavior occurs when the local Li concentration saturates, suddenly causing large concentration overpotentials that are observable in the terminal voltage and current (see Fig. 1) as well as numerical instability when solving the Butler-Volmer (BV) equation. Previous ESC modeling works either used the Doyle-Fuller-Newman (DFN) model with a limiting current density in the BV equation [3] or an equivalent circuit model (Eq-CM) [1]. However, solving the DFN is computationally intensive for a control application, while Eq-CMs cannot predict the diffusion-limited electrical behavior beyond the data they were parameterized on, requiring apriori experiments to be accurate. To balance the trade-offs, we instead look towards reduced-order modeling.In this work, we present a control-oriented, reduced-order, multi-physics model that captures the electrochemical, thermal, and venting behavior of four 4.6 Ah NMC pouch cells undergoing an external short circuit with different initial state-of-charge (SOC). The multi-physics model couples a first venting model with a lumped thermal model driven by Joule heating calculated through the overpotentials from the Single-Particle Model with electrolyte (SPMe). The combined model was parameterized through four experiments by fitting five key parameters in the submodels related to the SEI decomposition rate, cell thermal behavior, and solid electrode diffusion to capture the first venting timing, peak temperature, and diffusion-limited current-voltage drops. Additionally, two resistances were tuned to match the initial current and voltage with all other parameters were taken from the literature. Applying the same parameter set consisting of the average of the fitted values on all four cells, the combined multi-physics model correctly predicted that the fully-charged cells would vent and conservatively predicted the cell venting timing up to about 40 s before it occurred in the experiment. For high initial SOC cells, the SPMe also accurately predicted the current and voltage drops associated with diffusion limitations and SOC up until the cell vented. This is the first work that systematically parameterizes and couples a reduced-order, physics-based model to capture the electrical, thermal, and venting behavior under battery abuse conditions. Future work will explore applications for the model in early detection and response to critical safety events.REFERENCES[1] Tran, Vivian, Jason Siegel, and Anna Stefanopoulou. "Emergency Li-ion Battery Discharge using Nonlinear Model Predictive Control with Temperature and Venting Pressure Constraints." 2023 American Control Conference (ACC). IEEE, 2023.[2] Cai, Ting, et al. "Modeling li-ion battery first venting events before thermal runaway." IFAC-PapersOnLine 54.20 (2021): 528-533.[3] Rheinfeld, Alexander, et al. "Quasi-isothermal external short circuit tests applied to lithium-ion cells: Part ii. modeling and simulation." Journal of The Electrochemical Society 166.2 (2019): A151. Fig. 1: 15-min simulation of an external short circuit for cells at different initial SOC. Model results and experimental measurements are shown for four cells that were externally shorted. The measured current, voltage, temperature, and expansion force were measured at 10 Hz and plotted versus log time to highlight the dynamic current-voltage behavior in the first few minutes. The diffusion-limited current and voltage behavior are captured by the model. The two cells at 100% SOC, experienced venting and higher peak temperatures, which are well captured by the model. The cell venting timing was predicted to be 43 s and 7 s early for Cell 100A and 100B, respectively. The cells that were shorted at lower SOC did not vent. Figure 1

Impact of Artificial Intelligence on Mechanical Engineering: A Comprehensive Overview

Artificial Intelligence (AI) is coming to mainstream and as emerged as a transformative force in various fields not just limited including mechanical engineering. This paper provides an overview of the profound impact of AI on the practice and evolution of mechanical engineering. The usage of AI technologies in the field of mechanical engineering has potential to revolutionize traditional design, manufacturing, and maintenance processes. With AI-powered design tools engineers now can generate optimized designs faster with greater efficiency, leading to enhanced product performance and reduced development cycles. Further, Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.

Sexual orientation, gender identity and virologic failure among people with HIV: a cohort study in all of US research program

BackgroundSexual and gender minorities (SGMs) are at higher risk of HIV incidence compared to their heterosexual cisgender counterparts. Despite the high HIV disease burden among SGMs, there was limited data on whether they are at higher risk of virologic failure, which may lead to potential disease progression and increased transmission risk. The All of Us (AoU) Research Program, a national community-engaged program aiming to improve health and facilitate health equity in the United States by partnering with one million participants, provides a promising resource for identifying a diverse and large volunteer TGD cohort. Leveraging various data sources available through AoU, the current study aims to explore the association between sexual orientation and gender identity (SOGI) and longitudinal virologic failure among adult people with HIV (PWH) in the US.MethodsThis retrospective cohort study used integrated electronic health records (EHR) and self-reported survey data from the All of Us (AoU) controlled tier data, version 7, which includes participants enrolled in the AoU research program from May 31, 2017, to July 1, 2022. Based on participants’ sexual orientation, gender identity, and sex assigned at birth, their SOGI were categorized into six groups, including cisgender heterosexual women, cisgender heterosexual men, cisgender sexual minority women, cisgender sexual minority men, gender minority people assigned female at birth of any sexual orientation, and gender minority people assigned male at birth of any sexual orientation. Yearly virologic failure was defined yearly after one’s first viral load testing, and individuals with at least one viral load test > 50 copies/mL during a year were defined as having virologic failure at that year. Generalized linear mixed-effects models were used to explore the association between SOGI and longitudinal virologic failure while adjusting for potential confounders, including age, race, ethnicity, education attainment, income, and insurance type.ResultsA total of 1,546 eligible PWH were extracted from the AoU database, among whom 1,196 (77.36%) had at least one viral failure and 773 (50.00%) belonged to SGMs. Compared to cisgender heterosexual women, cisgender sexual minority women (adjusted Odds Ratio [aOR] = 1.85, 95% CI: 1.05–3.27) were at higher risk of HIV virologic failure. Additionally, PWH who were Black vs. White (aOR = 2.15, 95% CI: 1.52–3.04) and whose insurance type was Medicaid vs. Private insurance (aOR = 2.07, 95% CI: 1.33–3.21) were more likely to experience virologic failure.ConclusionsMaintaining frequent viral load monitoring among sexual minority women with HIV is warranted because it allows early detection of virologic failure, which could provide opportunities for interventions to strengthen treatment adherence and prevent HIV transmission. To understand the specific needs of subgroups of SGMs, future research needs to examine the mechanisms for SOGI-based disparities in virologic failure and the combined effects of multi-level psychosocial and health behavior characteristics.

Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study.

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77-0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4-6 months: 0.82; 7-9 months: 0.75; 10-12 months: 0.71), accuracy (0.68-0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.

Chemiluminescence immunoassay amplified by functional acridinium ester for the detection of NT-proBNP in clinical samples

High sensitivity and accuracy of heart failure biomarker, N-terminal prohormone brain natriuretic peptide (NT-proBNP), is critical for the early detection of heart failure. However, traditional chemiluminescent (CL) probe still face the challenges of poor solubility, in particular, significant disappointing bioavailability, which profoundly limit its clinical applications. In this paper, we demonstrate functional CL probe acridinium ester (AE-2) for the detection of NT-proBNP, through introducing a hydrophilic unit for ensuring high water dispersity and reactive group for promoting better bioavaiability with antibody. Compared to the traditional CL probe, the functional CL probe AE-2 exhibits extraordinary features, such as better biocompatibility, higher label rate with antibody, as well as significant chemiluminescence signal. In addition, this CL probe exhibits excellent linearity with the test data of Longhua Hospital, suggesting that AE-2 could be used as a clinical examination assay to detect NT-proBNP in real clinical samples. Thus, this functional CL probe AE-2 can be a powerful tool for elucidating the roles of NT-proBNP in clinical samples.

Evaluation of the Transfemoral Bone-Implant Interface Properties Using Vibration Analysis.

Evaluating the bone-implant interface (BII) properties of osseointegrated transfemoral (TFA) implants is important for early failure detection and prescribing loads during rehabilitation. The objective of this work is to derive and validate a 1D finite element (FE) model of the Osseointegrated Prosthetic Limb (OPL) TFA system that can: (1) model its dynamic behaviour and (2) extract the BII properties. The model was validated by: (1) comparing the 1D FE formulation to the analytical and 3D FE solutions for a simplifiedcylinder, (2) comparing the vibration modes of the actual TFA geometry using 1D and 3D FE models, and (3) evaluating the BII properties for three extreme conditions (LOW, INTERMEDIATE, and HIGH) generated using 3D FE and experimental (where the implant was embedded, using different adhesives, in synthetic femurs) signals for additional validation. The modes predicted by the 1D FE model converged to the analytical and the 3D FE solutions for the cylinder. The 1D model also matched the 3D FE solution with a maximum frequency difference of 2.02% for the TFA geometry. Finally, the 1D model extracted the BII stiffness and the system's damping properties for the three conditions generated using the 3DFE simulations and the experimental INTERMEDIATE and HIGH signals. The agreement between the 1D FE and the 3D FE solutions for the TFA geometry indicates that the 1D model captures the system's dynamic behaviour. Distinguishing between the different BII conditions demonstrates the 1D model's potential usefor the non-invasive clinical evaluation of the TFA BII properties.

The importance of ward-based education in the postoperative assessment of free flaps

Background: Despite the theoretical and practical advantages of adjunct-based free flap failure detection, the introduction of ultrasonographic techniques has not changed free flap failures rates and their use remains secondary to clinical assessment. Our study aims to develop an educational framework to improve the confidence of nursing staff caring for free flaps, with an emphasis on early detection of flap failure, thereby improving postoperative outcomes. Methods: Nursing staff in intensive care units and surgical wards caring for free flaps completed standardised questionnaires either before or after an educational seminar detailing clinical and Doppler assessment of free flaps. Differences between cohorts were summarised descriptively with frequencies and percentages. Overall confidence scores for pre-education and post-education cohorts were compared. Results: Overall, 173 responses were collected. Most staff were from intensive care units (n = 114) and almost one-third completed surveys following the educational intervention (n = 51). Microsurgical ward staff had more experience in the postoperative care of free flaps than intensive care unit staff (29% vs 18% had experience caring for &gt; 50 free flap cases). Confidence in assessing a free flap by clinical, implantable Doppler or external Doppler ultrasound was universally increased among ward staff compared to intensive care unit staff (p ≤ 0.007). Altogether, 51 per cent of intensive care unit staff had never identified a failing flap compared with 35 per cent of ward staff. Education increased confidence in managing and escalating failing flaps unanimously. Most failing flaps were identified by clinical assessment alone or combined with adjunct-based methods (94%). Conclusion: This study demonstrates that the provision of targeted education for nursing staff, both in intensive care units and on surgical wards, is useful for increasing confidence in the clinical assessment and early detection of free flap compromise. Increased confidence and improved clinical assessment may allow for early intervention and improved salvage rates for a threatened free flap, thereby improving patient outcomes.

Ag-Au bimetallic nanoparticle-based electrochemical sensing platform for quantification of B-type natriuretic peptide

A highly responsive and specific electrochemical biosensor utilizing Ag-Au bimetallic nanoparticles was engineered for the measurement of B-type natriuretic peptide (BNP), serving as an indicator for heart failure. The sensor was fabricated by modifying a glassy carbon electrode with Ag-Au bimetallic nanoparticles (average diameter: 20 ± 3 nm) and immobilizing anti-BNP antibodies. The sensor exhibited a wide linear range (0.0001–100 ng/mL), low limit of detection (0.03 pg/mL), and excellent specificity towards BNP, with minimal interference from other proteins (< 5 %). The practicality of the sensor was demonstrated by analyzing spiked serum samples, with recoveries ranging from 96.4 % to 103.5 % and relative standard deviations less than 4.5 %. The superior analytical property of the sensor is due to the synergistic effect of Ag and Au in enhancing electron transfer and antigen-antibody binding. The suggested biosensor holds significant promise for the early detection and monitoring of heart failure in medical environments.

An innovative transformer neural network for fault detection and classification for photovoltaic modules

Solar energy from photovoltaic systems (PV) ranks as the third greatest renewable electricity generation resource, expanding quickly through the years as it is free from environmental pollution and has cheap installation costs. Effective performance at high working rates is contingent on the early failure detection of PV modules. This study introduces an innovative deep learning model utilizing a Vision Transformer (ViT) artificial neural network (ANN) for the automatic detection of faults in infrared thermography (IR) images of PV modules. Our approach aims to enhance the accuracy of PV fault detection and classification compared to existing deep learning methods. The proposed framework encompasses three primary stages: (1) image preprocessing, which includes the application of the unsharp mask to sharpen the image’s edges or high-frequency components; (2) data augmentation techniques designed to overcome the problem of unbalanced classes that affect the training process, resulting in learning specific majority classes better than others; and (3) implementing a Vision Transformer deep learning model for its precision in digital image analysis. We evaluated the framework using the public Infrared Solar Modules dataset. The performance was quantitatively assessed using several metrics: accuracy, recall, precision, and F1 score. The dataset is classified into eleven different PV anomalies and another class of no-anomaly PV modules. The results show that our proposed approach has 98.23% accuracy for classifying the dataset into two classes, one for the PV anomaly and the other for the no-anomaly. It also has 96.19% accuracy for classifying eleven PV failures and 95.55% for twelve classes, including the no-anomaly class with the eleven types of anomalies. The experimental results underscore the potential of our model for earlier and more precise detection of PV faults. Furthermore, comparative analysis revealed the superior performance of the proposed approach over other deep learning methods.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

Management of spontaneous pyogenic spondylodiscitis: a descriptive cohort study

BackgroundSpontaneous spondylodiscitis (SD) is an inflammation of the vertebral endplate and the intervertebral disc with no prior surgery or intervention. The treatment of spontaneous pyogenic SD mainly consists of systemic antibiotics and rest. Some cases require surgery due to failure of medical treatment or due to neurological compromise. Due to the disease heterogeneity, there are no standardized, widely adopted treatment protocols. We conducted this study to assess the clinical outcome of the different treatment modalities.ResultsThis is a retrospective analysis of prospectively collected data of patients with spontaneous non-tuberculous spondylodiscitis. Thirty-eight patients were identified, including 14 males and 24 females. The mean age was 49 years. Twenty-three patients underwent surgery from the start while 15 patients received conservative medical treatment. Among the latter, 8 patients showed disease progression and required surgical intervention. The ODI/NDI at 1 year (mean = 8) was significantly better than before treatment (mean = 18). Among 11 patients with motor deficit at presentation, 8 improved and 3 remained stable. There were 14 complications, including 11 minor and 3 major, requiring one revision surgery.ConclusionsSurgical decompression and debridement with or without instrumented fusion for complicated SD cases is a safe and effective treatment modality. Close follow-up is needed in case of conservative treatment for early detection of treatment failure and disease progression. Baseline characteristic variabilities in patients with spontaneous pyogenic SD could predict conservative treatment failure, but this needs to be validated in larger series.