Reliable Predictive Tool Research Articles

Association and predictive ability between significant perioperative cardiovascular adverse events and stress glucose rise in patients undergoing non-cardiac surgery

BackgroundThe predictive importance of the stress hyperglycemia ratio (SHR), which is composed of admission blood glucose (ABG) and glycated hemoglobin (HbA1c), has not been fully established in noncardiac surgery. This study aims to evaluate the association and predictive capability the SHR for major perioperative adverse cardiovascular events (MACEs) in noncardiac surgery patients.MethodsIndividuals who underwent noncardiac surgical procedures between 2011 and 2020, including both diabetic and non-diabetic patients, were identified in the perioperative medicine database (INSPIRE 1.1) and classified into tertiles based on their SHR. The connection between the SHR and the risk of MACEs was studied using Cox proportional hazards regression analysis, then restricted cubic spline (RCS) was employed to assess the association’s form. Additionally, the SHR’s incremental predictive utility for MACEs was assessed by the C-statistic, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), thereby quantifying the enhancement in predictive accuracy brought by incorporating the SHR into existing risk models. Feature importance and predictive models were generated utilizing the Boruta algorithm and machine learning approaches.ResultsA total of 5609 patients were enrolled. With an upwards shift in SHR vertices, the rate of perioperative MACEs and cardiac death event steadily rose. The RCS analysis for perioperative MACEs and cardiac death event both indicated J-shaped associations. Inflection points occurred at SHR = 0.81 for MACEs and SHR = 0.97 for cardiac death. The model’s fit improved significantly, with a continuous NRI of 0.067 (95% CI: 0.025–0.137, P < 0.001) and an IDI of 0.305 (95% CI: 0.155–0.430, P < 0.001). When SHR was added as a categorical variable (> 0.81), the C-statistic increased to 0.785 (95% CI: 0.756–0.814) with a ΔC-statistic of 0.035 (P = 0.009), a continuous NRI of 0.007 (95% CI: 0.000-0.021, P = 0.016), and an IDI of 0.076 (95% CI -0.024-0.142, P = 0.092). In the Boruta algorithm, variables identified as important features in the green area were incorporated into the machine learning models development.ConclusionsThe SHR was related with an increased risk of perioperative MACEs in patients following noncardiac surgery, highlighting its potential as a useful and reliable predictive tool for assessing the risk of perioperative MACEs.Graphical

Residual Stress Analysis at the Conductor–Insulator Interface During the Curing Process of Hair-Pin Motors

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor’s lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design.

Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This study presents a detailed sustainable model for predicting PET using the Thornthwaite equation, which requires only mean monthly temperature (Tmean) and latitude, with calculations performed using R-Studio. A geographic information system (GIS) was employed to interpolate meteorological data, ensuring coverage of all sub-basins within the Murat River basin, the study area. Additionally, Python libraries were utilized to implement artificial intelligence-driven models, incorporating both machine learning and deep learning techniques. The study harnesses the power of artificial intelligence (AI), applying deep learning through a convolutional neural network (CNN) and machine learning techniques, including support vector machine (SVM) and random forest (RF). The results demonstrate promising performance across the models. For CNN, the coefficient of determination (R2) varied from 96.2 to 98.7%, the mean squared error (MSE) ranged from 0.287 to 0.408, and the root mean squared error (RMSE) was between 0.541 and 0.649. For SVM, the R2 varied from 94.5 to 95.6%, MSE ranged between 0.981 and 1.013, and RMSE ranged from 0.990 to 1.014. RF showed the best performance, achieving an R2 of 100%, MSE values of 0.326 and 0.640, and corresponding RMSE values of 0.571 and 0.800. The climate and topography data used for all algorithms were consistent, and the results indicate that the RF model outperforms the others. Consequently, The RF model’s superior accuracy highlights its potential as a reliable tool for sustainable PET prediction, supporting informed decision-making in water resource planning. By leveraging GIS, AI, and machine learning, this study enhances PET modeling methodologies, addressing critical water management challenges and promoting sustainable hydrological practices in the face of climate change and resource limitations.

Optimization of Thermal and Pressure Drop Performance in Circular Pin Fin Heat Sinks Using the TOPSIS Method

This study aims to optimize the thermal performance of pin fin heat sinks by minimizing the maximum temperature of the heat source. Using ANSYS ICEPAK, simulations were conducted for various design parameters, including the number of fins, inlet flow rate, and fin thickness, across circular fins in both inline and staggered arrangements. The circular staggered configuration with 36 fins (3 mm thick) and a flow rate of 6 CFM (Cubic Feet per Minute) achieved the lowest temperature of 34.96 °C, outperforming the inline arrangement. The Taguchi method helped strike a balance between heat transfer and pressure drop, revealing that flow rate has a greater influence when varied compared to the number of fins and fin thickness. An optimal configuration was identified with 36 fins and a flow rate of 4 CFM, which was less sensitive to operational variations. Analysis of Variance (ANOVA) revealed that inlet flow rate significantly impacts heat sink performance, while polynomial regression models demonstrated strong generalization capabilities, with Root mean square error (RMSE) of 8.92%. These findings provide reliable predictive tools and practical insights for optimizing heat sink designs in electronics cooling applications. By utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, the coefficient of relative closeness (Cn*) is plotted as a main effect. Referring to the multi-objective optimization-based TOPSIS method, it is found that the attributes are partly from the inlet flow rate (Q) are 63.4% of the number of fins (Nf) (25.05%).

Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities

Cardiovascular disease (CVD) is a significant global health concern and the leading cause of death in many countries. Early detection and diagnosis of CVD can significantly reduce the risk of complications and mortality. Machine learning methods, particularly classification algorithms, have demonstrated their potential to accurately predict the risk of cardiovascular disease (CVD) by analyzing patient data. This study evaluates seven binary classification algorithms, including Random Forests, Logistic Regression, Naive Bayes, K-Nearest Neighbors (kNN), Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, to understand their effectiveness in predicting CVD. Advanced preprocessing techniques, such as SMOTE–ENN for addressing class imbalance and hyperparameter optimization through Grid Search Cross-Validation, were applied to enhance the reliability and performance of these models. Standard evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC), were used to assess predictive capabilities. The results show that kNN achieved the highest accuracy (99%) and AUC (0.99), surpassing traditional models like Logistic Regression and Gradient Boosting. The study examines the challenges encountered when working with datasets related to cardiovascular diseases, such as class imbalance and feature selection. It demonstrates how addressing these issues enhances the reliability and applicability of predictive models. These findings emphasize the potential of kNN as a reliable tool for early CVD prediction, offering significant improvements over previous studies. This research highlights the value of advanced machine learning techniques in healthcare, addressing key challenges and laying a foundation for future studies aimed at improving predictive models for CVD prevention.

Lower Limb Joint Angle Prediction Based on Multistream Signaling and Quantile Regression, Temporal Convolution Network–Bidirectional Long Short-Term Memory Network Neural Network

In recent years, the increasing number of patients with spinal cord injuries, strokes, and lower limb disabilities has led to the gradual development of rehabilitation-assisted exoskeleton robots. A critical aspect of these robots is their ability to accurately sense human movement intentions to achieve smooth and natural control. This paper describes research carried out on predicting the motion angles of human lower limb joints. Based on the design of a signal acquisition system for physiological muscle signals and inertial measurement unit (IMU) data, a hybrid neural network prediction model (QRTCN-BiLSTM) and a single neural network prediction model (QRBiLSTM) were constructed using quantile regression, temporal convolution network (TCN) and bidirectional long short-term memory network (BiLSTM), respectively. At the same time, 7-channel surface electromyographic signals (sEMG) and 12-channel IMU data from hip and knee joints were collected and input into the QRBiLSTM and QRTCN-BiLSTM models to unfold the training and analyze the comparison. The results show that the QRTCN-BiLSTM model can more accurately infer human movement intention and provide a more reliable and accurate prediction tool for human–robot interaction research in rehabilitation robotics.

Machine learning model‐based preterm birth prediction and clinical nomogram: A big retrospective cohort study

AbstractObjectiveThis study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.MethodsThis retrospective cohort study utilized 2022 and 2018 National Vital Statistics System (NVSS) birth data, with the 2022 cohort arbitrarily split into training (70%) and internal verification (30%) subsets, and the 2018 cohort for external validation. Four machine learning algorithms—logistic regression, adaptive lasso regression, bootstrap forest, and boosted trees—identified features associated with preterm birth. The study then integrated the consensus features identified across the four models to construct a logistic regression‐based preterm birth prediction nomogram. To evaluate the model's efficacy, calibration, receiver operating characteristic (ROC), and decision curve analysis were applied to both the internal and external validation sets.ResultsThe study included 2 567 040 mother–infant pairs from the 2022 cohort and 2 688 568 mother–infant pairs from the 2018 cohort. All four machine learning models demonstrated high accuracy (area under the curve [AUC] &gt;0.7) in predicting preterm birth, and the internal validation results indicated good model generalizability. Feature selection identified nine common risk factors associated with preterm birth. The prediction nomogram based on these nine common features achieved AUCs of 0.701, 0.702, and 0.704 in the training, internal validation, and external validation sets, respectively. The calibration curves showed good agreement, and the decision curve analysis confirmed the model's net clinical benefits.ConclusionThis study developed a reliable preterm birth prediction tool using large‐scale birth cohort data, filling the gap of lacking external validation for existing preterm birth prediction models.

Advanced Monthly Rainfall Forecasting in Southern Lazio: Integrating Climatic Indices, Classical or Deep Neural Networks, and a Feature Selection Algorithm

Accurately predicting monthly precipitation is crucial for managing water resources, particularly in regions with complex climates like Southern Lazio, Italy. This study addresses the challenges of selecting relevant climate parameters and optimizing model complexity for precise forecasting. Four models, based on Bi-directional Long Short-Term Memory (BI-LSTM) and Radial Basis Function (RBF) neural networks, were compared, focusing on their ability to predict monthly precipitation using diverse inputs such as mean temperature, relative humidity, sea level pressure, radiation, precipitation change, and 12 climate indices. One significant challenge was determining the most impactful subset of input variables from a large dataset to enhance model performance without overfitting. To address this, the Particle Swarm Optimization (PSO) feature selection algorithm was employed, significantly improving model accuracy by identifying the optimal subset of input variables. The comparative analysis revealed that PSO-based models consistently outperformed those using all input variables, with the PSO-RBF model achieving a Kling-Gupta Efficiency (KGE) value of up to 0.80 during testing. Notably, both PSO-BI-LSTM and PSO-RBF models demonstrated strong performance in predicting precipitation peaks, achieving KGE values of 0.74. The study highlights the efficacy of the RBF model combined with PSO, which optimizes fewer parameters compared to the more complex BI-LSTM network while reducing input variables. This approach is not only less computationally intensive but also easier to implement, particularly in regions with limited instrumentation. These findings suggest that the PSO-RBF model is a reliable and efficient tool for monthly precipitation prediction in challenging environments.

Identification of independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4: the construction of a nomogram.

Hypoalbuminemia is a common complication in patients with chronic kidney disease (CKD) and is associated with various adverse clinical events. Currently, there are few studies focused on identifying risk factors and constructing models for hypoalbuminemia in patients with CKD stages 3 and 4. This study aims to identify independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4 and construct a nomogram for predicting the risk of hypoalbuminemia in these patients. A total of 237 patients with CKD stages 3 and 4 treated at Anning First People's Hospital from January to December 2023 were included. Univariate and bidirectional stepwise multivariate logistic regression analyses were used to identify independent risk factors for hypoalbuminemia in these patients. Based on the results of logistic regression analyses, a nomogram was constructed. The model performance was assessed using area under the receiver operator characteristic curve, calibration curves, and decision curve analysis. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors for hypoalbuminemia in these patients. The contributions of each independent risk factor to hypoalbuminemia were visualized in a nomogram. The area under the receiver operator characteristic curve of the model was 0.819, indicating good discrimination. The calibration curves showed good agreement between predicted and observed outcomes. The decision curve analysis also verified that the model had the good clinical utility. Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors of hypoalbuminemia in patients with CKD stages 3 and 4. The nomogram exhibits good discrimination, calibration, and clinical utility, offering a reliable tool for the early prediction and identification of hypoalbuminemia in these patients.

Identifying Prostatic Utricle Translucent Membrane in Hematospermia Patients Using a Novel Nomogram.

Aims/Background Hematospermia, characterized by blood in the ejaculate, is a common and distressing condition in urology. Identifying the underlying causes, including translucent membranes in the prostatic utricle, is crucial for effective management. Despite advancements in diagnostic techniques, reliable predictive tools are needed to enhance preoperative planning and patient outcomes. This study aimed to develop a novel nomogram to predict the presence of translucent membranes in the prostatic utricle of hematospermia patients. Methods In total, 284 patients were selected from The Second People's Hospital of Hefei database based on inclusion and exclusion criteria. The cohort was divided into a training set (198 patients) and a validation set (86 patients). To identify risk factors associated with the prostatic utricle translucent membrane, multivariable logistic regression analysis was employed. The identified risk factors were then used to construct a predictive nomogram model. The performance of the nomogram was evaluated using several statistical tools: receiver operating characteristic (ROC) curves to assess discriminative ability, calibration curves to evaluate prediction accuracy, and decision curve analysis (DCA) to determine clinical utility. Results The findings revealed that age, duration of disease, history of seminal vesiculitis, and seminal vesicle dimensions (width, length, and thickness) were independent risk factors for the presence of a prostatic utricle translucent membrane in patients with hematospermia. Using these variables, a nomogram was developed. The nomogram demonstrated strong predictive capability, as evidenced by its performance in ROC and calibration curve analyses. Furthermore, the DCA indicated that the nomogram offered significant clinical net benefits in predicting the presence of a translucent membrane. Conclusion Clinical use of the developed nomogram can assist clinicians in identifying patients with hematospermia who have translucent membrane in the prostatic utricle and in developing individualized treatment.

Adverse cardiovascular events in patients with cancer: a biomarker-enhanced clinical risk score

Abstract Background/Introduction Continuously improving cancer-specific survival puts a growing proportion of cancer patients at risk of major adverse cardiovascular events (MACE), but reliable tools for cardiovascular risk prediction remain unavailable (1). Purpose To assess a broad panel of cardiovascular biomarkers and clinical risk factors and to provide a novel risk score for the prediction of MACE in cancer patients. Methods Among 2’192 patients with newly diagnosed or recurrent cancer, a broad panel of cardiovascular biomarkers, including NT-proBNP, P-/E-/L-selectins, ICAM-1, VCAM-1, sLOX-1, CRP, and Lp(a) were measured by blinded study personnel. Patients were followed prospectively for the occurrence of 2-year MACE and 5-year cardiovascular death (2,3). Uni- and multivariable competing risk models were fit to assess independent associations of cardiovascular risk factors and biomarkers with MACE, and a risk score was developed. Results Beyond clinical risk factors, ICAM-1, P-/E-/L-selectins and NT-proBNP levels were independently linked to 2-year MACE risk, though shape of associations differed markedly. A clinical risk score was derived, assigning +1 point for male sex, smoking, and age ≥60 years, and +2 points for ASCVD history, yielding a bootstrapped C-statistic of 0.76 (95%CI:0.71-0.81). Implementation of cardiovascular biomarkers conferred improved performance (0.83, 95%CI:0.78-0.88). A simplified model, solely informed by clinical variables and readily available NT-proBNP (+1 point for NT-proBNP≥230pg/mL), achieved similar performance (0.80, 95%CI:0.74-0.86), with a cumulative 2-year MACE incidence of 0% in low- and 11.0% in high-risk patients. Conclusions Cancer patients are at substantial MACE risk. The herein presented first-of-its-kind risk score, integrating traditional cardiovascular risk factors and biomarkers, including NT-proBNP, effectively predicts 2-year MACE and 5-year cardiovascular death.

Disasterwatch: Android-Based Implementation of Yogyakarta Natural Disaster Monitoring Prediction

The increasing threat of natural disasters in Yogyakarta, Indonesia, calls for technological solutions to predict and monitor disaster events more accurately. The development of the DisasterWatch application aims to provide a reliable disaster prediction tool, using machine learning and data mining-based predictive modeling methods. The app utilizes various machine learning algorithms to analyze historical disaster data as well as related risk factors, such as weather, seismic activity and soil conditions. Preliminary results show that this prediction system can improve the accuracy of early warnings, but further development is needed to ensure its accessibility and reliability for the community. In addition, to ensure its accessibility and reliability for the wider community, especially in disaster-prone areas. Collaboration with BMKG, and local communities is being sought to ensure this application can be practically implemented in the field

Hybrid machine learning approach for accurate prediction of the drilling rate index

The drilling rate index (DRI) of rocks is important for optimizing drilling operations, as it informs the choice of appropriate methods and equipment, ultimately improving the efficiency of rock excavation projects. This study presents a hybrid machine learning approach to predict the DRI of rocks accurately. By integrating grey wolf optimization with support vector machine (GWO-SVM), random forest (GWO-RF), and extreme gradient boosting (GWO-XGBoost) models, the aim was to enhance predictive accuracy. Among these, the GWO-XGBoost model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.999, mean absolute error (MAE) of 0.00043, root mean square error (RMSE) of 1.98017, and severity index (SI) of 0.0350 during training. Testing results confirmed its accuracy with R² of 0.999, MAE of 0.00038, RMSE of 1.80790, and SI of 0.0312. Furthermore, the GWO-XGBoost model outperformed the other models in terms of precision, recall, f1-score, and multi-class confusion matrix results for each DRI class. The GWO-RF model also demonstrated high accuracy, ranking second, while the GWO-SVM model showed comparatively lower performance. This research aims to advance rock excavation practices by providing a highly accurate and reliable tool for DRI prediction. The results highlight the significant potential of the GWO-XGBoost model in improving DRI predictions, offering valuable intuitions and practical applications in the field.

An interpretable machine learning-based model for shear resistance prediction of CFRP-strengthened RC beams using experimental and synthetic dataset

Existing analytical models for predicting the shear resistance of RC beams strengthened with externally bonded CFRP reinforcements exhibit deficient performance due to their inability to accurately capture the complex resisting mechanisms. Combined with significant statistical uncertainties in shear failure, driven by its brittle nature, this further undermines the reliability of these models. To address these limitations, this study leverages Machine Learning (ML) to develop more robust and reliable predictive tool. A rigorous feature-selection process identified eight predictors as the most influential. Subsequently, nine ML-algorithms were trained on a refined experimental dataset comprising 239 beams, with XGBoost emerging as the top performer. This model also outperformed established models likefib Bulletin-90 and ACI 2023 models. However, the limited scope of the experimental dataset constrained the model’s predictive performance especially when separately evaluated on beams strengthened with U-wraps, full wraps or side-bonded FRP configurations. Therefore, to achieve a more reliable model a synthetic dataset was generated using Tabular Variational Auto-Encoder. The XGBoost model trained with the synthetic dataset significantly improved the performance of the former model and exhibited better predictions for all strengthening configurations. Finally, to ensure the physical consistency of predictions, values obtained from the SHapley Additive exPlanations method were analysed.

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process.

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model's adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model's potential as a reliable tool for real-time RUL prediction in industrial applications.

Draw on advantages and avoid disadvantages: CT-derived individualized radiomic signature for predicting chemo-radiotherapy sensitivity in unresectable advanced non-small cell lung cancer

BackgroundPresently, the options of concurrent chemo-radiotherapy (CCR) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) are controversial and there is no reliable prediction tool to stratify poor- and good-responders. Although radiomic analysis has provided new opportunities for personalized medicine in oncological practice, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This study aimed to develop a qualitative radiomic signature based on the within-sample rank of radiomics features, and to use this novel method to predict CCR sensitivity in LA-NSCLC, avoiding the variability of quantitative signatures to multicenter effect.MethodsWe retrospectively analyzed 125 patients with stage III NSCLC who received treatment from our hospital. Radiomic features were extracted from pretreatment plain CT scans and constructed as feature pairs based on their within-sample rank. Fisher and univariate Cox analyses were performed to select feature pairs significantly associated with patients’ overall survival (OS). NSCLC-Radiomic (R422) cohort including 104 NSCLC patients was used as an independent testing cohort. NSCLC-Radiogenomic (RG211) cohort with matched RNA sequencing profiles, was used for functional enrichment analysis to reveal the underlying biological mechanism reflected by the signature.ResultsA qualitative signature, consisting of 15 radiomic feature pairs (termed as 15-RiFPS), was developed based on the Genetic Algorithm, which could optimally distinguish responder from non-responder with significantly improved OS if they received CCR treatment (log-rank P = 0.0009, HR = 13.79, 95% CIs 1.83–104.1). The performance of 15-RiFPS was validated in an independent public cohort (log-rank P = 0.0037, HR = 2.40, 95% CIs 1.30–4.40). Furthermore, the transcriptomic analyses provided biological pathways (‘glutathione metabolic process’, ‘cellular oxidant detoxification’) underlying the signature.ConclusionsWe developed a CT-derived 15-RiFPS, which could potentially help predict individualized therapeutic benefit of CCR in patients with LA-NSCLC. Additionally, we investigated the underlying intra-tumoral biological characteristics behind 15-RiFPS which would accelerate its clinical application. This approach could be applied to a wider range of treatments and cancer types.

An energy analysis methodology for residential heat pump retrofits

The government has targets to upscale domestic heat pump retrofit as part of the strategy to decarbonise heat. There are significant barriers to uptake including installation cost together with a lack of expertise in predicting and reporting energy and carbon performance. As a result, end user confidence in heat pumps can be low. This paper presents analysis of two monitored domestic properties using high resolution data. The aim is to develop reliable prediction and in-use diagnostic tools to improve transparency and reliability of heat pump performance. This paper focuses on a method drawn from theory as set out in CIBSE TM41, and shows how real building heat loss coefficients can be determined from regression analysis over different time frame resolutions. Practical application This research aims to provide residential heat pump retrofit providers with cost effective methods for assessing the suitability of dwellings to accept heat pump retrofit solutions, and to extend these techniques for the effective monitoring and performance evaluation of heat pumps post installation.

Utilization of High-Temperature miniaturized testing to Assist in life management for gas turbine Blades: An Overview

Safely operating gas turbine blades in aero-engines or industrial gas turbines under severe thermo-mechanical conditions always poses a continuous challenge. There is an urgent need for more reliable life prediction tools, necessitating earlier and more rigorous assessments of in-service damage. This paper introduces a systematic examination of high-temperature miniature specimen testing method within a practical life management framework. To improve current practices in assessing the condition of service-aged gas turbine blades, a proactive utilization of miniature specimen testing is recommended for early in-service assessment within the component lifecycle and integrated into a comprehensive life assessment/management framework. The paper outlines a structured approach to life management, combining miniature specimen sampling and creep and fatigue testing methods. This forms the basis for a novel, holistic life assessment methodology, which is proposed in this paper, incorporating the innovative use of high-temperature miniature specimen testing.

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human lives, property, and the natural environment, particularly in regions with steep slopes, heavy rainfall, and unfavorable human interference, including deforestation and urbanization. Nigeria’s diverse geographical structure, ranging from urban areas such as Lagos and Abuja to rural regions, underscores the importance of accurate predictions for risk management and avoidance techniques. This research utilized remote sensing and GIS assessment to analyze landslide susceptibility in several regions of Nigeria. Data related to terrain, vegetation, soil moisture, and ground deformation were gathered using high-resolution satellite imagery from sources such as SPOT, ASTER, differential synthetic aperture radar interferometry (D-InSAR), and Landsat TM. GIS data layers included DEM, LULC, soil and geological maps, as well as hydrological maps and data. Methods applied in this study include logistic regression, STAT-R, frequency ratio, and the Random Forest tree-based model. The research produced detailed landslide susceptibility maps for various regions in Nigeria and identified significant factors such as slope, elevation, land use, precipitation, and access to transportation facilities. The Random Forest model demonstrated the most robust predictive capability. The integration of remote sensing with GIS was particularly significant, enhancing the precision of predictions and improving the efficacy of planning and management strategies. By incorporating remote sensing, GIS, and various machine learning algorithms, the researchers have developed a reliable tool for landslide risk prediction and management in Nigeria. Future research should focus on improving data quality and enhancing the generalizability of results to other regions.

Predicting osteoradionecrosis risk in patients with locoregionally advanced nasopharyngeal carcinoma undergoing concurrent chemoradiotherapy: The value of the CARWL index.

Osteoradionecrosis (ORN) is a severe complication that can arise in patients with nasopharyngeal carcinoma due to the aggressive nature of chemoradiotherapy treatment. The purpose of our study was to assess the utility of the recently introduced CARWL index, which integrates the C-reactive protein-to-albumin ratio (CAR) and significant weight loss (SWL), in predicting the risk of ORN in patients with locoregionally advanced nasopharyngeal cancer (LA-NPC) undergoing concurrent chemoradiotherapy (CCRT). We conducted a retrospective cohort analysis on 304 patients with LA-NPC treated with CCRT. Patients were categorized into CARWL index groups based on CAR (cut-off: 3.0) and SWL (weight loss > 5% over the past six months): CARWL-0 (CAR < 3.0, SWL ≤ 5%), CARWL-1 (CAR < 3.0 with SWL > 5% or CAR ≥ 3.0 with SWL ≤ 5%), and CARWL-2 (CAR ≥ 3.0 and SWL > 5%). The primary endpoint was the incidence of ORN in each CARWL index group. At a median follow-up of 67.2 months, 28 patients (9.2%) developed ORN. The incidence of ORN was 2.1%, 9.4%, and 16.3% in the CARWL-0, CARWL-1, and CARWL-2 groups, respectively (P < 0.001). Multivariate analysis identified smoking status (HR: 2.58, P = 0.034), N-stage (HR: 1.96, P = 0.008), T-stage (HR: 1.84, P = 0.017), pre-CCRT tooth extraction status (HR: 5.81, P < 0.001), post-CCRT tooth extraction status (HR: 6.82, P < 0.001), mandibular V55.8 Gy (HR: 6.12, P < 0.001), and CARWL score (HR: 5.67, P = 0.002) as significant predictors of ORN. The CARWL index is a reliable predictive tool for evaluating the risk of ORN in LA-NPC patients undergoing CCRT. If further validated, its use in clinical settings could aid in the early identification of high-risk patients and enable the implementation of personalized preventive strategies.