Real-time Assessment Research Articles

ECOCARDIOGRAFIA TRANSTORÁCICA NO MANEJO ANESTÉSICO E INTRAOPERATÓRIO: UMA REVISÃO DE LITERATURA

The anesthetic and intraoperative management of patients undergoing surgical procedures, particularly those of high complexity, requires rigorous monitoring and the ability to promptly respond to hemodynamic changes. In this context, transthoracic echocardiography (TTE) has emerged as an important imaging tool, allowing real-time assessment of cardiac function. This study aims to review the literature published in the last five years (2020-2025) on the role of TTE in anesthetic and intraoperative management. A narrative literature review was conducted using the PubMed, Web of Science, and Embase databases. The findings of this study affirm that TTE is an essential and promising tool in anesthetic practice, with the potential to optimize hemodynamic management and improve patient safety during surgical procedures. Its integration into the perioperative routine represents a significant advancement in modern anesthesiology, contributing to the reduction of complications and enhancement of clinical outcomes. However, its use should be combined with a critical assessment of the patient’s clinical condition and the technical limitations of the equipment, ensuring the selection of the best strategy for each case.

Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement

With the increasing penetration of distributed generation (DG), the supply–demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation.

Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning

Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports and limited observations, which can introduce bias and yield incomplete evaluations. This study addresses these limitations by generating and utilizing a comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG data were collected from eight muscles as participants performed repetitive lifting tasks. For each task, the lifting index was calculated using the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE). Participants completed cycles of both low-risk and high-risk repetitive lifting tasks within a four-minute period, allowing for the assessment of muscle performance under realistic working conditions. This extensive dataset, comprising over 7 million data points sampled at approximately 1259 Hz, was leveraged to develop deep learning models to classify lifting risk. To provide actionable insights for practical occupational ergonomics and risk assessments, statistical features were extracted from the raw EMG data. Three deep learning models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were employed to analyze the data and predict the occupational lifting risk level. The CNN model achieved the highest performance, with a precision of 98.92% and a recall of 98.57%, proving its effectiveness for real-time risk assessments. These findings underscore the importance of aligning model architectures with data characteristics to optimize risk management. By integrating wearable EMG sensors with deep learning models, this study enables precise, real-time, and dynamic risk assessments, significantly enhancing workplace safety protocols. This approach has the potential to improve safety planning and reduce the incidence and severity of work-related musculoskeletal disorders, ultimately promoting better health and safety outcomes across various occupational settings.

Image-Guided Robotic Surgery for Sentinel Lymph Node Status Assessment in Uterine Cancers Using Ultrasound Drop-in Probe: Surgical Technique in 10 Steps.

Recent guidelines recommend the sentinel lymph node (SLN) technique in uterine cancers, as it is associated with lower perioperative complications.1 Image-guided surgery can address some limitations of SLN procedures, such as low frozen-section accuracy and risk of empty packets,2-4 by providing real-time lymph node assessment.5,6 This video describes the surgical procedure of intraoperative robotic ultrasound examination for SLN assessment. The Arietta L43K (2-12MHz, Hitachi, Japan) drop-in robotic ultrasound probe was used to assist with SLN dissection. The procedure was performed on the da Vinci Xi platform. The probe, introduced through an accessory trocar, was manipulated by robotic instruments, providing real-time ultrasound imaging in split-view mode on the surgeon's console. Ultrasound images, captured by the surgeon under the guidance of an experienced ultrasound examiner, were analyzed both in vivo and ex vivo.7 RESULTS: The procedure involves ten steps, detailed as follows. (1) The procedure begins with the cervical injection of indocyanine green. (2) The pelvic retroperitoneum is opened to allow access to anatomical landmarks. (3) Using near-infrared imaging mode, the lymphatic pathways are highlighted, allowing for the identification of the SLN. (4) Once identified, the drop-in ultrasound probe is introduced. (5) The console is switched to split-view mode, enabling the surgeon to observe ultrasound images alongside the endoscopic view. (6) In vivo imaging is conducted. (7) Both images and videos of the lymph node are captured. (8) The SLN is subsequently dissected. (9) SLN specimens are safely extracted. (10) Ex vivo ultrasound assessment is performed to further evaluate their characteristics. Although its accuracy compared with histology as the gold standard is yet to be demonstrated, image-guided robotic lymph node ultrasound is a feasible and promising procedure for real-time SLN assessment. A prospective study is ongoing (R-LYNUS, NCT06621823) to clinically validate this technique.

Occult Ventricular Fibrillation Visualized by Echocardiogram During Cardiac Arrest: A Retrospective Observational Study From the Real-Time Evaluation and Assessment for Sonography—Outcomes Network (REASON)

Occult Ventricular Fibrillation Visualized by Echocardiogram During Cardiac Arrest: A Retrospective Observational Study From the Real-Time Evaluation and Assessment for Sonography—Outcomes Network (REASON)

Enhanced assessment of water quality for both nitrate and nitrite using engineered E. coli with para-aminobenzoic acid biosynthesis.

Monitoring nitrate and nitrite levels in water is vital for protecting human health, aquatic ecosystems, and regulatory compliance. However, traditional detection methods often involve environmentally harmful chemicals. This study introduces a sustainable alternative by leveraging metabolically engineered E. coli to biosynthesize para-aminobenzoic acid (PABA) via the shikimate pathway, replacing conventional sulfonamides in the Griess reaction. This approach significantly reduces environmental impact while maintaining high analytical performance. This study introduces a sustainable approach for simultaneously detecting nitrate and nitrite in water using a combination of E. coli strains DH5α and BL21. Metabolically engineered E. coli BL21 produces PABA via the shikimate pathway, replacing synthetic chemicals in the modified Griess reaction. The modified Griess reaction, utilizing PABA, achieved a high sensitivity detection limit of 0.57μM with excellent selectivity for nitrite over other ions. Recognizing the importance of portability for on-site, real-time water quality assessment, we developed a paper-based detection system utilizing lyophilized cell supernatant. To enhance portability, we developed a paper-based method for detecting nitrite using lyophilized cell supernatant. This approach confirmed successful nitrite detection through a clear colorimetric response, enabling immediate and quantitative analysis of nitrate and nitrite. Validation with real-world water samples yielded a recovery rate of 90-100%, comparable to the Griess Reagent, confirming the effectiveness and reliability of the proposed sensors for environmental monitoring. By integrating the capabilities of two E. coli strains, this dual-detection system uniquely allows simultaneous quantification of nitrate and nitrite in a single sample, significantly advancing the field of water quality monitoring. This study demonstrates a sustainable, high-sensitivity solution for water quality monitoring by combining microbial metabolic engineering with a portable, paper-based detection platform. The approach meets EPA standards, minimizes environmental impact, and provides a practical tool for field-testing, underscoring the potential of engineered microbes for eco-friendly and effective environmental monitoring.

Real-Time Assessment of Alcohol Reward, Stimulation, and Negative Affect in Individuals With and Without Alcohol Use Disorder and Depressive Disorders.

The allostasis theory states that, as addiction develops, alcohol is consumed to relieve negative affect rather than to produce positive effects. This study aimed to investigate the real-time subjective effects of alcohol in individuals with alcohol use disorder (AUD) and those prone to negative affect by virtue of having comorbid depressive disorder (DEP). Participants (N=221) completed high-resolution ecological momentary assessments during 3-hour monitoring of one alcohol drinking episode and one non-alcohol drinking episode in their natural environment. Participants also completed daily mood surveys and next-day surveys. Linear mixed-effect models were used to compare drinking behavior and subjective responses (stimulation, sedation, liking, wanting, negative affect) among 120 participants with AUD (AUD+; with depression [DEP+]: N=64, without depression [DEP-]: N=56) and 101 participants without AUD (AUD-; DEP+: N=45, DEP-: N=56). During the monitoring period, participants with AUD consumed an average of 8.5 standard alcohol drinks (estimated blood alcohol concentration [eBAC]=0.115 g/dl) versus 3.7 drinks (eBAC=0.040 g/dl) for non-AUD participants. The AUD group, regardless of comorbid DEP, reported increases in stimulation and rewarding effects that persisted throughout most of the alcohol episode relative to the non-alcohol episode. To a lesser extent, alcohol relieved negative affect but this was not specific to AUD or DEP groups. Contrary to the allostasis model of addiction's emphasis on negative reinforcement drinking, findings demonstrated that people with AUD prone to negative affect displayed positive alcohol reinforcement with pronounced and prolonged sensitivity to alcohol's pleasurable effects, akin to their noncomorbid counterparts. The findings provided critical testing of addiction theories in the natural environment to enhance external validity.

Evaluation of Active Intraoperative Nerve Monitoring in Severe Developmental Dysplasia of the Hip Patients Undergoing Total Hip Arthroplasty.

Total hip arthroplasty (THA) stands as the standard treatment in neglected developmental dysplasia of the hip (DDH), and its application in severe cases may be linked to debilitating outcomes, including nerve damage. Here, we aimed to report the results of intraoperative nerve monitoring (IONM) via an active method. In this retrospective cohort study, we recruited patients with Crowe types III and IV DDH, who underwent THA. The study comprised 2 cohorts: one without nerve monitoring and the other with active IONM under epidural anesthesia. The primary study outcomes included the incidence of neural complications, the extent of achieved leg lengthening, and the necessity for femoral osteotomy. A total of 183 patients were included in this study as the cases underwent THA under epidural anesthesia and IONM, along with 156 historical cohorts of controls. In the group with IONM, no clinically postoperative nerve injury was detected, while in the control group, 6 (3.8%) patients experienced neural complications (P= .08). The mean achieved limb lengthening was significantly greater in the monitoring group as 4.2 cm (range= 2.4-5.6) than in the control group as 3.56 cm (range= 2.2-5.6) (P= .04). The rate of femoral osteotomy was significantly lower in the monitoring group (13.6%, 25/183) compared to the control group (27.5%, 43/156) (P < .005). The utilization of active IONM in patients under epidural anesthesia during THA for severe DDH proves to be an effective approach. This method allows for real-time assessment of nerve function throughout the surgical procedure, demonstrating its potential to minimize postoperative complications.

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threaten astronaut vision and mission outcomes. Current SANS monitoring, limited to pre- and post-flight exams, lacks in-flight diagnostics, highlighting an urgent need for autonomous tools capable of real-time assessment. Grok, an AI platform by xAI, offers promising potential as an advanced diagnostic tool for space-based health monitoring. Originally developed for broader applications, Grok's high-resolution imaging capabilities could be adapted to detect early SANS indicators such as optic nerve edema and shifts in globe morphology, changes linked to fluid redistribution in space. However, realizing this vision requires algorithmic and hardware adjustments to address the unique physiological shifts astronauts experience. By advancing Grok's diagnostic capability, we strongly believe astronauts could manage SANS autonomously, bringing much-needed real-time, high-accuracy diagnostics to isolated, high-stakes environments-essential as humanity embarks on increasingly ambitious missions to Mars and beyond.

Automating container damage detection with the YOLO-NAS deep learning model.

Ensuring the integrity of shipping containers is crucial for maintaining product quality, logistics efficiency, and safety in the global supply chain. Damaged containers can lead to significant economic losses, delays, and safety hazards. Traditionally, container inspections have been manual, which are labor-intensive, time-consuming, and error-prone, especially in busy port environments. This study introduces an automated solution using the YOLO-NAS model, a cutting-edge deep learning architecture known for its adaptability, computational efficiency, and high accuracy in object detection tasks. Our research is among the first to apply YOLO-NAS to container damage detection, addressing the complex conditions of seaports and optimizing for high-speed, high-accuracy performance essential for port logistics. Our method showcases YOLO-NAS's superior efficacy in detecting container damage, achieving a mean average precision (mAP) of 91.2%, a precision rate of 92.4%, and a recall of 84.1%. Comparative analyses indicate that YOLO-NAS consistently outperforms other leading models like YOLOv8 and Roboflow 3.0, which showed lower mAP, precision, and recall values under similar conditions. Additionally, while models such as Fmask-RCNN and MobileNetV2 exhibit high training accuracy, they lack the real-time assessment capabilities critical for port applications, making YOLO-NAS a more suitable choice. The successful integration of YOLO-NAS for automated container damage detection has significant implications for the logistics industry, enhancing port operations with reliable, real-time inspection solutions that can seamlessly integrate into predictive maintenance and monitoring systems. This approach reduces operational costs, improves safety, and lessens the reliance on manual inspections, contributing to the development of "smart ports" with higher efficiency and sustainability in container management.

Indocyanine Green as a Marker for Tissue Ischemia in Spinal Tumor Resections and Extended Revisions: A Technical Note

Background/Objectives: The increasing complexity of spinal oncology procedures, particularly in en-bloc tumor resections, creates challenges in tissue perfusion assessment due to extended operative times and extensive surgical dissection. Real-time visualization of tissue perfusion can be achieved with ICG using commercially available handheld imaging systems, offering potential advantages in spinal oncology cases. This study assessed the utility of ICG in analyzing soft-tissue viability during complex spine procedures extending beyond 7.5 h, with a particular focus on oncologic resections. Methods: Three cases that required over 7.5 h of operative time were chosen for ICG utilization. These cases included an en-bloc malignant peripheral nerve sheath tumor resection, an en-bloc resection of a malignant epithelioid neoplasm, and a long-segment fusion revision for pseudoarthrosis. At the conclusion of the critical portion of the procedure, a handheld intraoperative fluorescence camera was utilized to visualize the tissue penetration of intravenous ICG. Results: Prior to injecting ICG, devascularized tissue was not clearly visible. Injecting ICG allowed clear separation of vascularized (fluorescing) and devascularized (non-fluorescing) tissues. One region of non-florescent tissue was later confirmed to be devascularized with MRI and experienced postoperative infection. Conclusions: As the complexity of spinal oncology procedures increases, ICG fluorescence imaging offers a novel method for real-time assessment of tissue perfusion. This technique may be particularly valuable in extensive tumor resections, post-radiation cases, and revision surgeries where tissue viability is at risk. Further investigation in the spinal oncology population could help establish whether early identification of poorly perfused tissues impacts wound healing outcomes.

Development and external validation of a dynamic nomogram for predicting the risk of functional outcome after 90 days in patients with acute intracerebral hemorrhage

Background and purposeIntracerebral hemorrhage remains a significant cause of death and disability worldwide, highlighting the urgent need for accurate prognostic assessments to optimize patient management. This study aimed to develop a practical nomogram for risk prediction of poor prognosis after 90 days in patients with intracerebral hemorrhage.MethodsA retrospective study was conducted on 638 patients with intracerebral hemorrhage in the Second Hospital of Fujian Medical University, China, who were divided into a training set (n = 446) and a test set (n = 192) by random splitting. Then the data on demographics, clinical symptoms, imaging characteristics, and laboratory findings were collected. In this study, adverse outcomes were defined as a Modified Rankin Scale (mRS) score of 3–6 at 90 days post-ICH onset, as assessed during follow-up. Later, least absolute shrinkage and selection operator (LASSO) regression and multifactorial logistic regression were used to screen the variables and construct a nomogram. Next, the evaluation was performed using the Receiver Operating Characteristic (ROC) curve, calibration curve, and decision curve analysis. Finally, the external validation was completed using the data of 496 patients with intracerebral hemorrhage from the Jinjiang Hospital of Traditional Chinese Medicine.ResultsIn the training and test sets of intracerebral hemorrhage, the incidence of poor prognosis was 60.53 and 61.46%, respectively. Through variable screening, this study identified age, Glasgow Coma Scale (GCS), blood glucose, uric acid, hemoglobin, and hematoma location as independent predictors of poor prognosis in intracerebral hemorrhage. The developed dynamic nomogram was easy to use and demonstrated strong predictive performance (training set AUC: 0.87; test set AUC: 0.839; external validation set AUC: 0.774), excellent calibration, and clinical applicability.ConclusionThe dynamic nomogram we developed using five independent risk factors serves as a practical tool for real-time risk assessment and can help facilitate early intervention and personalized patient management, thereby improving clinical outcomes in high-risk patients.

Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study

Background/Objectives: Excess adiposity, affecting 43% of the global adult population, is a major contributor to cardiometabolic diseases. Lifestyle behaviours, specifically dietary habits, play a key role in weight management. Real-time assessment methods such as Ecological Momentary Assessment (EMA) provide context-rich data that reduce recall bias and offer insights into dietary triggers and lapses. This study examines dietary triggers among adults with excess adiposity in Singapore using EMA, focusing on factors influencing dietary adherence and lapses. Methods: A total of 250 participants with a BMI ≥ 23 kg/m2 were recruited to track dietary habits for one week, at least three times a day, using the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS) embedded within the Eating Trigger Response Inhibition Program (eTRIP© V.1) smartphone app. Logistic regression analysis was used to identify predictors of dietary adherence. Results: Of the 4708 responses, 76.4% of the responses were indicative of adherence to dietary plans. Non-adherence was primarily associated with food accessibility and negative emotions (stress, nervousness, and sadness). Factors such as meals prepared by domestic helpers and self-preparation were significantly associated with adherence. Negative emotions and premenstrual syndrome were identified as significant predictors of dietary lapses. Conclusions: EMA offers valuable insights into dietary behaviours by identifying real-time triggers for dietary lapses. Future interventions can utilise technology-driven approaches to predict and prevent lapses, potentially improving adherence and weight management outcomes.

Public and patient involvement (PPI) in the design, execution and dissemination of a trial: the BISTRO trial.

For people receiving haemodialysis, a balance has to be struck between removing sufficient but not too much fluid during a treatment session and maintaining any remaining kidney function they might have. In the BISTRO trial, this study sought to establish if getting the balance right might be improved by the additional use of bioimpedance, a device that measures body fluid composition to help decide how much fluid to remove during dialysis. Designing and executing this trial, which incorporated complex and repeated trial procedures that would be dependent on participant engagement, presented challenges that demanded effective public and patient involvement. This study aimed to develop an effective public and patient involvement participation model, ensuring that the patient voice was heard by the Trial Management Group, with a Patient Advisory Group undertaking coproduction of all participant-facing documents and communications, including dissemination of the trial results, with the main purpose of maximising participant engagement in the study. An open-label randomised controlled trial in which 439 participants from 34 centres were allocated for regular assessments of their bodily fluid content with or without the use of bioimpedance measurements. Development of an effective public and patient involvement working model that was represented within the Trial Management Group, contributing to protocol design, selection of bioimpedance device, and coproduction of all participant-facing communications including dissemination of trial findings. Public and patient involvement contribution prior to trial initiation, description of the participant-facing communications, adherence to trial materials, dropout and dissemination of trial findings. Post-trial evaluation by research teams, Patient Advisory Group and co-applicants. An effective working model was developed which relied on remuneration of the public and patient involvement patient lead and use of social media (e.g. WhatsApp) to maximise inclusivity. The Patient Advisory Group coproduced with the Trial Management Group a series of communication postcards and newsletters and a web page to support the participants and disseminate the trial results that were highly rated by research teams, but not always passed on to trial participants. Participant adherence to the main trial outcomes was excellent (113.6% urine collections obtained). Potentially avoidable dropout was 14.4%, with 3.6% being clearly attributable to inability or unwillingness to comply with the trial procedures. Reflections by the Patient Advisory Group indicated that they felt valued, involved and listened to but anticipated more direct involvement with the trial participants, recommending that barriers to this be addressed during the trial design and set-up. Evaluation of public and patient involvement was retrospective and there was a lack of real-time assessment of the impact of public and patient involvement that might have supported a causative link between public and patient involvement interventions and the successful delivery of the trial. Public and patient involvement played an important role in the design, delivery and dissemination of the BISTRO trial. Key to this success was the close relationship between the Patient Advisory Group and the Trial Management Group. Given the complexity of the intervention, dropout was reasonably low and did not compromise trial findings, but reasons were not always clear. Prospective gathering of data to capture the impact of public and patient involvement is recommended and direct support for participants facilitated. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 14/216/01.

Design of a Multi-Node Data Acquisition System for Logging-While-Drilling Acoustic Logging Instruments Based on FPGA

The logging-while-drilling (LWD) acoustic logging instrument is pivotal in unconventional oil and gas exploration, and in providing real-time assessments of subsurface formations. The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals from the formation, which directly affects the accuracy and timeliness of the logging data. Recognizing the constraints of current LWD acquisition systems, including limited data collection capabilities and inadequate precision, this study introduces an FPGA-based multi-node data acquisition system for LWD acoustic logging. This system increases sampling density and data accuracy, leading to a more comprehensive collection of formation information. The multi-node acquisition system is composed primarily of a main control circuit board and several acquisition circuit boards, all connected via an RS485 bus. The Field-Programmable Gate Array (FPGA) is utilized to develop the acquisition circuit board’s firmware, offering adjustable control over parameters, such as the AD7380’s operational mode, sampling rate, and depth, facilitating real-time and concurrent acquisition and storage of formation acoustic signals. The main control board communicates with the acquisition boards via the RS485 bus, issuing commands to enable autonomous data collection and transfer from each board, thus enhancing the system’s reliability and scalability. Experimental results confirm the system’s capacity to efficiently capture waveform signals and upload them in real-time, underscoring its dependability and timeliness. The findings suggest that the system is capable of high-speed, real-time acquisition and processing of acoustic signals, offering robust technical support for the continued application of LWD acoustic logging instruments.

Enhanced porphyrin-based hypoxia imaging by temporal oversampling of delayed fluorescence signal.

Protoporphyrin IX (PpIX) delayed fluorescence (DF) is inversely related to the oxygen present in tissues and has potential as a novel biomarker for surgical guidance and real-time tissue metabolism assessment. Despite the unique promise of this technique, its successful clinical translation is limited by the low intensity emitted. We developed a systematic study of ways to increase the PpIX DF signal through acquisition sampling changes, allowing optimized imaging at video rates. To accomplish signal increase, time-gating signal compression was achieved through changes in pulse frequency and power density, using sampling rates that are faster than the decay rate of the signal. The increased signal yield was tested and validated in vitro and then demonstrated in vivo, with comparison to settings that sample the full lifetime emission decay. Results in vitro and in vivo demonstrated that optimized timing could increase the detected intensity by a factor of 7. The images showed results that were superior than when sampling the full DF lifetime decay. The proposed timing optimization enhances PpIX-based DF real-time imaging of tissue hypoxia. By increasing sampling frequency and adjusting the acquisition gate and pulse width, the collected signal intensity improved sevenfold, demonstrated both in vitro and in vivo. The technique was shown to enable better visualization of small and anatomically challenging hypoxic structures. The improved target-to-background ratio and compatibility with pressure-enhanced sensing of tissue oxygen technique were demonstrated.

Comprehensive Echocardiographic Protocol for Pigs with Emphasis on Diastolic Function: Advantages over MRI Assessment.

Swine are increasingly utilized in cardiovascular research due to their anatomical and physiological similarities to humans, particularly for studying diastolic dysfunction. While MRI offers excellent structural imaging, echocardiography provides superior real-time assessment of diastolic parameters. To address the lack of standardized methods and reduce variability across studies, we present a comprehensive guide for performing echocardiography in Yorkshire pigs, detailing anatomical considerations, equipment requirements, and technical approaches. We describe systematic approaches for obtaining and optimizing right parasternal long and short-axis views, apical four-chamber, and subcostal imaging windows, with specific attention to anatomical variations from human cardiac orientation and standard clinical transducer positioning. These tomographic views enable comprehensive assessment of systolic and diastolic function, including ventricular volumes, wall thicknesses, chamber dimensions, ejection fraction, and Doppler measurements of blood flow and tissue velocities. This standardized methodology for echocardiographic images acquisition enhances data reliability in cardiovascular pig models, improving the interpretation of preclinical study results and strengthening translational research outcomes. The protocol also provides consistency for veterinary applications, making echocardiography a preferred modality for longitudinal studies in this valuable translational model.

A Real-Time Flame Detection and Situation Assessment Algorithm for Firefighting Robots

A Real-Time Flame Detection and Situation Assessment Algorithm for Firefighting Robots

Stress Prediction Processes of Metal Pressure-Bearing Complex Components in Thermal Power Plants Based on Machine Learning

The real-time stress assessment of metal pressure components is one of the key factors in ensuring the safe operation of thermal power plants. To address the challenge of real-time prediction of stress in the key areas of complex special-shaped metal pressure-bearing components in a certain domestic 300 MW thermal power plant, three typical complex metal pressure-bearing components, the main steam pipe tee (MSPT), the steam drum downcomer joint (DDJ) and the header ligament (HL), were taken as research objects. The stress distribution of the three complex metal pressure-bearing components under different conditions was analyzed through the finite element method, and the stress results at the dangerous points were used as samples to establish training sample data. Subsequently, different machine learning methods were employed to train the sample data. The training results indicate that neural networks (NNs) and the Auto-Sklearn Regression (ASR) models can accurately predict the stress of the key parts of complex metal pressure-bearing components in real time. The ASR method demonstrates better performance in stress prediction of the main steam pipe tee, with a prediction accuracy of ≥96%. The NN model shows better prediction for the header ligament, with a prediction accuracy of ≥94%. These research findings provide effective support for the high-temperature lifespan assessment and safe operation of thermal power plants.

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.