Operational Data Research Articles

Analyzing greenhouse gas emissions and influencing factors of 247 actual wastewater treatment plants in China using emission factor and operational data integrated methods (ODIM).

In response to China's policies on pollution control and carbon emission (CE) reductions, more stringent regulations have been implemented to evaluate CE in wastewater treatment facilities. In this study, we have analyzed CE from China's wastewater treatment plants (WWTPs) and influencing factor. Emission factor (EF) and operational data integrated methods (ODIM) were utilized to measure emissions, using data collected from 247 WWTPs over a 1-year period across seven regions in China. The average CE intensity was 0.45 kgCO2-eq/m3, affected by region, season, influent water quality, treatment processes, effluent discharge standards, and facilities. The scale effect was obvious only in the range of 2 × 105 m3/day. Underground WWTPs exhibited significantly higher CE compared to aboveground WWTPs. In summary, the assessment of CE in 247 actual WWTPs not only identifies emission reduction potential but also provides a scientific basis for formulating targeted emission reduction measures.

Retrofitting of a solar cooling and heating plant by employing PCM storage and adjusting control strategy

A solar cooling and heating plant with a 1000 l hot water storage tank has been operated since 2018. Two main problems were observed: (1) a fluctuating operation of the absorption chiller due to a high number of stat-up and shut-down procedures; (2) high electricity consumption caused by a low solar fraction SFn of 50.9% during the cooling period. In 2021, the solar plant was retrofitted by employing a shell and tube heat exchanger based on a PCM melting at 64 °C and by adjusting the control strategy. The system performance was evaluated and compared before and after retrofitting based on the operational data in 2021 and 2022. The total number of switch-on times of the chiller shrank by 33.5% and the operation time was increased by 65% in 2022 due to the stable hot water inlet temperature. Contributing to the high heat storage capacity of the PCM storage, more solar thermal energy was utilized and the average SFn reached 75.6% during the cooling period in 2022, leading to a 24.4% reduction in the total electricity consumption compared to 2021.

Modeling of Unburned Carbon in Pulverized Coal-Fired Supercritical Steam Generators

ABSTRACT In coal-fired boilers, one of the most important metrics for optimizing combustion is the proportion of unburned carbon (UC) in the ash. In most Indian power plants, it is still assessed offline through manual sample collection and laboratory analysis. The laborious processes of sample collection, preparation, analysis, and reporting require at least five to eight hours of work. The delayed results may not be useful to optimize combustion in the boiler furnace, as the boiler operating conditions may be completely different from the conditions when the samples were collected. Instead, the use of prediction algorithms may bring timely corrective action if the UC goes high. The present experimental study is taken up to obtain a correlation between UC and corresponding relevant boiler operational parameters and on the residence time of coal particles in the boiler furnace. Multiple regression analysis is performed using ANOVA tools. One hundred and ninety-four sets of coal, ash samples and boiler operating parameters are collected from 07 supercritical units. Coal proximate, coal fineness and ash UC analysis are carried out. The analyzed results and collected boiler operational data are statistically analyzed using Microsoft Excel’s ANOVA tool to understand the dependent behavior of UC on the boiler operational parameters and fired coal characteristics. The empirical equations are proposed for the prediction of UC in bottom and fly ash for supercritical boilers. Additionally, fresh 20 sets of coal, ash samples, and related operating data are gathered for the validation of the proposed empirical equations. For additionally collected samples, the predicted results are compared with the offline measured laboratory results. The maximum deviation between the predicted and laboratory results are within the range of ± 0.40% and ± 0.24% for bottom and fly ash samples, respectively.

Microalgae production in an industrial-scale photobioreactors plant: A comprehensive Life Cycle assessment

Microalgae cultivation provides multiple opportunities to produce valuable bioproducts, but greater clarity must be achieved regarding the real sustainability of current technologies. Numerous life cycle assessment (LCA) studies have been conducted so far. However, most of them were based on literature data and/or extrapolations of lab-scale results, while only a few studies used primary data from pilot or full-scale microalgal plants. Moreover, the obtained results showed great variability, leaving the debate on microalgae sustainability fully open.This work presents a thorough LCA based on primary data from an industrial-scale microalgal facility located in Caltagirone, Italy. The plant is based on vertically-stacked horizontal photobioreactors (total volume of 40.4 m3) installed in a greenhouse and has a capacity of 1200 kgDW/y (Chlorella vulgaris). A cradle-to-gate assessment was performed with the functional unit of 1 kgDW biomass, including operational and infrastructural data. The results emphasized the key role in the generation of potential impacts played by cultivation among process stages and by chemicals (nutrients and cleaning agents) and electricity (mainly for agitation and thermoregulation) among flow types. In comparison with studies from the literature, the analysed microalgal plant has an intermediate environmental performance (e.g., global warming potential of 153 kg CO2,eq/kgDW). This result is encouraging, as it comes from a reliable assessment built on full-scale primary data. On the other hand, it highlights the need to explore alternative strategies (e.g., industrial symbiosis and circular bioeconomy) to reduce the environmental footprint of the process and enhance its economic attractiveness.

Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia

The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall–runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included the following: CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts; NSE, PCC, MAE, KGE, PBias, and RMSE; and three categorical metrics, CSI, FAR, and POD, for deterministic forecasts. The skill scores, CRPS, relative CRPS, CRPSS, and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE, and RMSE as well as for the categorical metrics. Forecast performance also progressively decreased with higher streamflow volumes. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country—they were generally better in the high-runoff-generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia, and the mid-west of Western Australia. We did not find any association between the model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resource management.

Evaluation of a Military Global Health Engagement Mission for Critical Wartime Surgical Specialty Readiness.

Surgical volume at Military Treatment Facilities (MTFs) has been gradually decreasing for roughly the past 2 decades. The Knowledge, Skills, and Abilities (KSA) Clinical Readiness Program linked surgical volume and readiness using a tool known as the KSA metric. However, the extent to which military medical missions contribute to the readiness of critical wartime specialties has not been evaluated using this metric. In this study, a retrospective analysis was conducted using the surgical case logs from the US Naval Ship (USNS) Comfort missions in 2018 and 2019. The comprehensive case log data were categorized by year, surgeon, procedure, and location. The analysis focused on providing detailed descriptive statistics, including percentages pertaining to the types of procedures performed during these missions. The 2018 mission was 11 weeks in duration, and supported activities in Ecuador, Peru, Colombia, and Honduras. The USNS Comfort mission in 2019 lasted 6 months (June-November 2019), and visited 12 countries in Central America, South America, and the Caribbean. The 2019 mission case log, spanning 6 months, was evaluated using the KSA score in order to assess readiness and compare against 6 months of MTF KSA values within the same calendar year. In 2019, the orthopedic surgeon aboard the USNS Comfort had a total KSA score of 44,006, but the 6-month USNS Comfort mission only contributed 5,364 points (12% of the annual score). The general surgery practice aboard the USNS Comfort produced lower KSA scores compared to each surgeon's respective MTF practice (Table III). Analyzing the cases logged by general surgeons also highlights minimal surgical diversity during these missions, with more than 90% of cases being hernia repairs or laparoscopic cholecystectomies (Table I). In addition, 35% of total procedures performed in 2018 and 2019 were performed laparoscopically. The analysis of operative data from the 2019 USNS Comfort mission, in comparison with the surgeons' work at their respective MTFs, reveals limited benefit in the ability of hospital-ship missions to bolster surgical readiness as measured by the KSA score. However, this is not a reflection on the value of Global Health Engagement (GHE) itself but a review of the way in which it is leveraged to support surgical readiness. Military surgeons participate in GHE as part of a larger strategy to strengthen relationships with partner nations, improve military medical force interoperability, and bolster partner nation medical capacity and capabilities. The KSA score offers an excellent tool to compare readiness metrics across significantly different GHE missions, and facilitates the opportunity for future prospective studies to improve case volume, diversity, and ultimately readiness.

Use of Fish Skin Grafts for Wound Management: Report of 5 Pediatric Cases

Abstract Background Managing wounds and scars in children presents a significant challenge for surgical and rehabilitation teams. Studies have already explored the potential of fish skin grafts with Omega-3 in hard-to-heal lower extremity chronic ulcers and deep-burn management. Aims The purpose of this study is to assess the safety and outcome of intact fish skin grafts with Omega-3 when used as skin replacement therapy in the pediatric population. Methods This article was conducted in adherence to our hospital's ethical guidelines. Patients enrolled in this study were identified retrospectively. Their medical records were reviewed, and data was collected concerning patient demographics, initial mode of injury, indication for fish skin placement, operative data, postoperative care, complications and outcomes. The acellular dermal matrix was obtained from North Atlantic codfish skin (Gadus morhua) farmed in Isafjordur, Iceland. Results All patients underwent meticulous wound debridement and patch application under general anesthesia and were discharged the same day. Wound surface ranged between 10 and 110 cm2 (mean: 53 cm2). Standard analgesic medications were taken for a maximum period of 48 hours in all patients. Upon follow-up at our outpatient clinic, shrinkage in the wound surface was observed in all patients after a few days, followed by early wound granulation. Two patients underwent subsequent split-thickness grafting to achieve healing. The mean time required to achieve complete epithelialization was 48.6 days (range: 29 – 62 days). No hypersensitivity or allergic reaction was reported. The definitive outcome was satisfactory in all wounds, meaning complete wound coverage. Conclusion The findings from this case series reveal that fish skin grafts with Omega-3 can be an effective and a safe scaffold in the process of repairing damaged tissues in the pediatric population. We have been able to accomplish a quality skin replacement therapy, with no donor site morbidity.

Investigation of risk factors in the development of recurrent urethral stricture after internal urethrotomy.

Urethral stricture is a condition that often develops with trauma and results in narrowing of the urethral lumen. Although endoscopic methods are mostly used in its treatment, it has high recurrence rates. Therefore, open urethroplasty is recommended after unsuccessful endoscopic treatments. To investigate the risk factors associated with urethral stricture recurrence. The data of male patients who underwent internal urethrotomy for urethral stricture between January 2017 and January 2023 were retrospectively analyzed. Demographic data, comorbidities, preoperative haemogram, and biochemical values obtained from peripheral blood and operative data were recorded. Patients were divided into two groups in terms of recurrence development; recurrence and non-recurrence. Initially recorded data were compared between the two groups. A total of 303 patients were included in the study. The mean age of the patients was 66.6 ± 13.6 years. The mean duration of recurrence development was 9.63 ± 9.84 (min-max: 1-39) months in the recurrence group. Recurrence did not occur in non-recurrence group throughout the follow-up period with an average time of 44.15 ± 24.07 (min-max: 12-84) months. In the comparison of both groups, the presence of diabetes mellitus (DM), hypertension (HT), and multiple comorbidities were significantly higher in the recurrence (+) group (P = 0.038, P = 0.012, P = 0.013). Blood group, postoperative use of non-steroidal anti-inflammatory drugs, preoperative cystostomy, cause of stricture, iatrogenic cause of stricture, location and length of stricture, indwelling urinary cathater size and day of catheter removal did not differ between the two groups. No statistically significant difference was observed between the two groups in terms of age, uroflowmetric maximum flow rate value, hemogram parameters, aspartate aminotransferase (AST), alanine aminotransferase (ALT), fasting blood sugar, creatinine, glomerular filtration rate, neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, lymphocyte-monocyte ratio, monocyte-lymphocyte ratio and AST/ALT ratios. In patients with urethral stricture recurrence, only the frequency of DM and HT was high, while inflammation marker levels and stricture-related parameters were similar between the groups.

Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI

Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.

Energy Efficiency Analysis of Waste-to-Energy Plants in Poland

The issue of enhancing energy recovery efficiency is a key concern within the European Union’s climate protection efforts. In particular, it applies to all processes and plants for the harvesting, gathering, and conversion of energy. The abandonment of fossil fuels in favour of alternative energy sources, and the increasing of energy efficiency and its recovery, is now a widely accepted direction of energy development. This study focuses on facilities that recover and process energy from municipal waste left after recycling processes, known as waste-to-energy (WtE) plants. These plants’ energy recovery efficiency is governed by the R1 Formula in EU countries. This report is based on an analysis of four years of operational data from selected Polish municipal waste incinerators, supplemented by a discussion of various studies on energy recovery efficiency. The primary objective of this report is to evaluate the effectiveness of these plants in contributing to sustainable waste management and energy recovery. The main effect of the developed report is the set of results of the energy recovery efficiency factor values, determined based on the R1 formula valid in the EU legislation, tabulated and graphically illustrated, and calculated for five selected Polish waste-to-energy plants. The presented results, with their graphical interpretation, discussion, and conclusions, provide insights into several factors influencing the value of the R1 efficiency factor. They can be a valuable contribution to operators of waste-to-energy plants, especially those operating in countries outside the EU.

A control-oriented LPV model of large-scale offshore wind turbines for deloading operation

ABSTRACT This article develops a new control-oriented linear parameter varying (LPV) model, which is a linear state-space model with varying parameters aimed at efficiently representing large-scale offshore wind turbines (LOWTs). Firstly, the LPV model is constructed by the mechanism analysis of a 15-MW direct-drive permanent magnet wind turbine. Subseuently, based on the operational data from FAST, the parameters with unknown values in the LPV model are determined by gain scheduling and parameter estimation. Furthermore, a deloading operation for the active output power regulation of LOWTs is proposed based on coordinating the rotor over-speed control (ROC) and the pitch angle control (PAC).

On the Model Update Strategies for Supervised Learning in AIOps Solutions

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in the field are constantly evolving due to factors such as the changing operational environment and user base, the models in AIOps solutions need to be constantly maintained after deployment. While prior works focus on innovative modeling techniques to improve the performance of AIOps models before releasing them into the field, when and how to update AIOps models remain an under-investigated topic. In this work, we performed a case study on three large-scale public operation data: two trace datasets from the cloud computing platforms of Google and Alibaba and one disk stats dataset from the BackBlaze cloud storage data center. We empirically assessed five different types of model update strategies for supervised learning regarding their performance, updating cost, and stability. We observed that active model update strategies (e.g., periodical retraining, concept drift guided retraining, time-based model ensembles, and online learning) achieve better and more stable performance than a stationary model. Particularly, applying sophisticated model update strategies (e.g., concept drift detection, time-based ensembles, and online learning) could provide better performance, efficiency, and stability than simply retraining AIOps models periodically. In addition, we observed that, although some update strategies (e.g., time-based ensemble and online learning) can save model training time, they significantly sacrifice model testing time, which could hinder their applications in AIOps solutions where the operation data arrive at high pace and volume and where immediate inferences are required. Our findings highlight that practitioners should consider the evolution of operation data and actively maintain AIOps models over time. Our observations can also guide researchers and practitioners in investigating more efficient and effective model update strategies that fit in the context of AIOps.

Value of Intracardiac Echocardiography in the Interventional Closure of Patent Foramen Ovale with Adjacent Atrial Septal Defect

Objective. This study aimed to investigate the application value of intracardiac echocardiography (ICE) in transcatheter closure of a patent foramen ovale (PFO) combined with an adjacent atrial septal defect (ASD). Methods. This retrospective study included five patients with PFO combined with adjacent ASD who underwent transcatheter closure and were admitted to the Zhongshan Hospital of Fudan University from June to September 2023. General conditions, ultrasound and ICE findings, and operative data were recorded and followed up for 2–6 months. Results. Of the five patients, two and three had embolic stroke of undetermined source and migraine, respectively, aged 45.6 ± 12.0 years. All patients underwent successful transcatheter closure via the PFO tunnel under the guidance of ICE, without complications or new stroke, and showed significantly reduced migraine at the follow-up. Conclusion. In patients with PFO combined with adjacent ASD, closure of a PFO tunnel could be successfully achieved under ICE guidance; its clinical efficacy was accurate and worthy of promotion.

Regression calibration of self-reported mobile phone use to optimize quantitative risk estimation in the COSMOS study.

The Cohort Study of Mobile Phone Use and Health (COSMOS) has repeatedly collected self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information is prone to measurement error, but operator data were available prospectively for only part of the study population and did not cover past mobile phone use. To optimize the available data and reduce bias, we evaluated different statistical approaches for constructing mobile phone exposure histories within COSMOS. We evaluated and compared the performance of four regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case (CC) analysis and multiple imputation (MI) in a simulation study with a binary health outcome. We used self-reported and operator-recorded mobile phone call data collected at baseline (2007-2012) from participants in Denmark, Finland, the Netherlands, Sweden, and the UK. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with CC analysis or MI. We showed that RC methods resulted in more accurate estimation of the relation between mobile phone use and health outcomes, by combining self-reported data with objective operator-recorded data available for a subset of participants.

Accuracy and completeness of registry-reported unicompartmental knee arthroplasty revision.

The key outcome of joint registries is revision events, which inform clinical practice and identify poor-performing implants. Registries record revision events and reasons, but accuracy may be limited by a lack of standardized definitions of revision. Our study aims to assess the accuracy and completeness of unicompartmental knee arthroplasty (UKA) revision and indications reported to the New Zealand Joint Registry (NZJR) with independent clinical review. Case record review of 2272 patients undergoing primary UKA at four large tertiary hospitals between 2000 and 2017 was performed, identifying 158 patients who underwent revision. Detailed review of clinical findings, radiographs and operative data was performed to identify revision cases and the reasons for revision using a standardized protocol. These were compared to NZJR data using chi-squared and Fisher exact tests. The NZJR recorded 150 (95%) of all UKA revisions. Osteoarthritis progression was the most common reason on the systematic clinical review (35%), however, this was underreported to the registry (8%, P < 0.001). A larger proportion of revisions reported to the registry were for 'pain' (30% of cases vs. 5% on clinical review, P < 0.001). A reason for revision was not reported to the registry for 10% of cases. The NZJR had good capture of UKA revisions, but had significant differences in registry-reported revision reasons compared to our independent systematic clinical review. These included over-reporting of 'pain', under-reporting of osteoarthritis progression, and failing to identify a revision reason. Efforts to improve registry capture of revision reasons for UKA could be addressed through more standardized definitions of revision and tailored revision options for UKA on registry forms.

Reconsidering screening thresholds in health assessments for obstructive sleep apnea using operational and safety incident data

The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016–2018) and subsequent rail incident data (2016–2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met ‘at risk’ criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02–2.56) in the years following their health assessment compared to drivers who did not meet ‘at risk’ criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 ‘at risk’ train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00–2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.

A digital twin model of the axial temperature field of a DC cable for millisecond calculations

The most important features of digital twin technology for transmission and transformation equipment are symbiotic evolution and state prediction. In constructing the digital twin model for DC cables, the transient and steady-state temperature fields need to be considered comprehensively in the modeling process to consider the multi-physical field coupling, mathematical model, operational data, and environmental conditions in specific cases, which makes it difficult to satisfy the real-time requirements of the digital twin. To solve this problem, in this paper, a digital twin technology architecture for DC cables is proposed, and a neural network learning model is constructed to minimize the latency due to the complexity of the model simulation. Moreover, an efficient digital twin model that is based on the model-driven approach is developed for the fast computation of the axial temperature field of DC cables. The use of the Sequence-to-Sequence residual neural network algorithm for the reduced-order multi-physics field coupling modeling reduces the computational time and ensures as much accuracy as possible up to the finite element model simulation. The simulation results show that the use of the digital twin model reduces the simulation time from 275 s to 0.56 s, and the relative error between the accuracy of the digital twin model and the finite element model for the axial temperature field of the DC cable is 0.46 %. Finally, verification of the reasonableness and feasibility of the proposed method is carried out via an experimental platform according to the standard of CIGRE TB496. In particular, the maximum value of the DC cable temperature is verified to have a small error compared with the result of the Sequence-to-Sequence calculation under specific working conditions.

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.

ACCURACY OF REGISTRY-REPORTED REASONS FOR UNICOMPARTMENTAL KNEE ARTHROPLASTY REVISION

IntroductionA key outcome measured by national joint registries are revision events. This informs best practice and identifies poor-performing surgical devices. Although registry data often record reasons for revision arthroplasty, interpretation is limited by lack of standardised definitions of revision reasons and objective assessment of radiologic and laboratory parameters. Our study aim was to compare reasons for unicompartmental knee arthroplasty (UKA) revision reported to the New Zealand Joint Registry (NZJR) with reasons identified by independent clinical review.MethodsA total of 2,272 patients undergoing primary medial and lateral UKA at four large tertiary hospitals between 2000 and 2017 were included. A total of 158 patients underwent subsequent revision with mean follow-up of 8 years. A systematic review of clinical findings, radiographs and operative data was performed to identify revision cases and to determine the reasons for revision using a standardised protocol. These were compared to reasons reported to the NZJR using Chi-squared and Fisher exact tests.ResultsOsteoarthritis progression was the most common reason for revision on systematic clinical review (30%), however this was underreported to the registry (4%, p&lt;0.001). A larger proportion of revisions reported to the registry were for ‘unexplained pain’ (30% of cases vs. 4% on clinical review, p&lt;0.001). A reason for revision was not reported to the registry for 24 (15%) of cases.Discussion and ConclusionWe found significant inaccuracies in registry-reported reasons for revision following UKA. These included over-reporting of ‘unexplained pain’, under-reporting of osteoarthritis progression, and failure to identify a reason for revision. Efforts to improve registry capture of revision reasons for UKA should focus on increasing accuracy in these three areas. This could be addressed through standardised recording methods and tailored revision reason options for UKA for surgeons to select when recording the reasons.

An Advanced Quality Assessment and Monitoring of ESA Sentinel-1 SAR Products via the CyCLOPS Infrastructure in the Southeastern Mediterranean Region

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.