Validation Phase Research Articles

CAE-ResVGG FusionNet: A Feature Extraction Framework Integrating Convolutional Autoencoders and Transfer Learning for Immature White Blood Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a highly aggressive cancer form that affects myeloid cells, leading to the excessive growth of immature white blood cells (WBCs) in both bone marrow and peripheral blood. Timely AML detection is crucial for effective treatment and patient well-being. Currently, AML diagnosis relies on the manual recognition of immature WBCs through peripheral blood smear analysis, which is time-consuming, prone to errors, and subject to inter-observers' variation. This study aimed to develop a computer-aided diagnostic framework for AML, called "CAE-ResVGG FusionNet", that precisely identifies and classifies immature WBCs into their respective subtypes. The proposed framework leverages an integrated approach, by combining a convolutional autoencoder (CAE) with finely tuned adaptations of the VGG19 and ResNet50 architectures to extract features from CAE-derived embeddings. The process begins with a binary classification model distinguishing between mature and immature WBCs followed by a multiclassifier further classifying immature cells into four subtypes: myeloblasts, monoblasts, erythroblasts, and promyelocytes. The CAE-ResVGG FusionNet workflow comprises four primary stages, including data preprocessing, feature extraction, classification, and validation. The preprocessing phase involves applying data augmentation methods using geometric transformations and synthetic image generation using the CAE to address imbalance in the WBC distribution. Feature extraction involves image embedding and transfer learning, where CAE-derived image representations are used by a custom integrated model of VGG19 and ResNet50 pretrained models. The classification phase employs a weighted ensemble approach that leverages VGG19 and ResNet50, where the optimal weighting parameters are selected using a grid search. The model performance was assessed during the validation phase using the overall accuracy, precision, and sensitivity, while the area under the receiver characteristic curve (AUC) was used to evaluate the model's discriminatory capability. The proposed framework exhibited notable results, achieving an average accuracy of 99.9%, sensitivity of 91.7%, and precision of 98.8%. The model demonstrated exceptional discriminatory ability, as evidenced by an AUC of 99.6%. Significantly, the proposed system outperformed previous methods, indicating its superior diagnostic ability.

The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO2 emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO2 emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.

NANDA-I and NOC Linkages for Six Psychosocial Nursing Diagnoses: A Validation Study

The aim of this study was to validate the diagnostic content and the NANDA-I and NOC linkages for six psychosocial nursing diagnoses. This multicentre, descriptive, cross-sectional validation study followed Fehring’s model. Expert nurses in nursing methodology and standardised nursing languages in Spain participated, with expertise criteria based on academic level and clinical, teaching, and research experience in the fields of nursing methodology and standardised nursing languages. This study adhered to the STROBE guidelines for cross-sectional studies. Eighty-seven professionals participated in the diagnostic content validation phase, and 57 in the NANDA-I and NOC linkages validation phase. The content validity indices of the six diagnoses ranged from 0.74 to 0.84, all considered valid. All 84 proposed defining characteristics were valid, with 42 scoring ≥0.8. Thirty-two linkages between diagnoses and NOC outcomes were proposed, all valid, with mean scores between 0.73 and 0.98. Each diagnosis was linked to 5–6 NOC outcomes, comprising 26 main outcomes and 6 supplementary outcomes. Overall coverage rates for each diagnosis ranged from 68.42% to 100%. All linkages between defining characteristics and NOC outcome indicators were validated. The six selected psychosocial diagnoses, their defining characteristics, and the proposed linkages between diagnoses and outcomes have been validated. The validation of linkages between NOC indicators and nearly all major defining characteristics of these six psychosocial diagnoses will make it possible to enhance diagnostic accuracy and enable continuous assessment of the effects of nursing interventions on the clinical progression of these diagnoses.

Circulating Exosomes Studied by Label-free Proteomics Analysis Reveal Transition Signatures from Diabetes Mellitus to Diabetic Kidney Disease

Background: Diabetic kidney disease (DKD) is a common microvascular complication of diabetic mellitus (DM). At present, the early diagnosis of DKD mainly depends on microalbuminuria, which is prone to be affected by confounding factors such as urinary tract infections. Methods: To identify the more stable early diagnosis markers, the whole proteome in the circulating exosomes from controls, DM patients, and DKD patients was quantified by label-free proteomics analysis and then validated with parallel reaction monitoring. Results: Three hundred ninety-one quantitative proteins were detected, and the expression trends of 7 proteins in the validation phase were consistent with that in the discovery phase. The expression level assessment results revealed that the expression of EFEMP1 and ApoA4 in the DKD group was significantly higher than those in DM and controls. Correlation analysis showed that EFEMP1 and APOA4 were positively correlated with urinary microalbumin and urinary albumin creatinine ratio and had excellent diagnostic values for distinguishing DKD from DM and controls. Conclusions: ApoA4 and EFEMP1 could serve as the early diagnosis markers of DKD. These findings provide a possibility for the development of a clinical diagnostic index that can efficiently distinguish DKD from DM in the near future.

Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF – Multilayer Perceptron (MLP), EBF – Function Tree (FT), EBF – Alternating decision tree (ADtree), EBF – Adaptive Boosting (AdaBoost), EBF – Random SubSpace (RSS), EBF – Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15–21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance.

Comprehensive analysis of normal shock wave propagation in turbulent non-ideal gas flows with analytical and neural network methods

Shock wave propagation in gases through turbulent flow has wide-reaching implications for both theoretical research and practical applications, including aerospace engineering, propulsion systems, and industrial gas processes. The study of normal shock propagation in turbulent flow over non-ideal gas investigates the changes in pressure, density, and flow velocity across the shock wave. The Mach number is derived for the system and explored across various gas molecule quantities and turbulence intensities. This study analytically investigated the normal shock wave propagation in turbulent flow of adiabatic gases with modified Rankine–Hugoniot conditions. Artificial neural network (ANN) techniques are used to estimate the solutions for shock strength and Mach number training validation phases of back-propagated neural networks with the Levenberg–Marquardt algorithm. The results reveal that pressure ratio with density ratio increase for higher values of increase in the turbulence level as well as intermolecular forces. A reverse trend is observed in velocity coefficient after shock in the presence of adiabatic gas. The regression coefficient values obtained using the network model ranged from 0.999 99 to 1, indicating an almost perfect correlation. These findings demonstrate that the ANN can predict the Mach number with high accuracy.

Development and evaluation of a duplex real-time multienzyme isothermal rapid amplification assay for the detection of hypervirulent Klebsiella pneumoniae in clinical spiked blood specimens

ObjectivesOur objective was to establish a rapid and precise method for detecting hypervirulent Klebsiella pneumoniae (hvKP) by utilizing a duplex real-time multienzyme isothermal rapid amplification (real-time MIRA) and to evaluate its performance in clinical spiked blood specimens. MethodsThe research comprised two phases: an initial pilot study to establish the methodology and a clinical validation study to assess its effectiveness. In the pilot phase, we designed specific primers and probes targeting the hvKP pg344 and incA genes and subsequently developed a duplex real-time MIRA assay to evaluate its detection limits, specificity, and efficiency. In the clinical validation phase, we analyzed thirty-three spiked blood specimens using the duplex real-time MIRA assay. ResultsThe duplex real-time MIRA assay demonstrated no cross-reactivity with other strains. Sensitivity experiments confirmed that the assay had a detection limit as low as 8 × 102 CFU per reaction for hvKP. The analysis of clinical spiked blood specimens indicated that the sensitivity and specificity of the duplex real-time MIRA assay were on par with those of duplex real-time PCR. ConclusionsThese findings confirm that the duplex real-time MIRA assay is a fast, straightforward, and dependable method for detecting hvKP.

Automated Analysis Of Diabetic Vasculopathy Using Semantic Segmentation Of Thermal Images Of Peroneal Vessel

Introduction: Diabetic vascular disease is one of most serious health problems in diabetic patients, it causes the development of severe complications including delayed wound healing and increased susceptibility to infections. Methods: To provide accurate and are non-invasive diagnosis, current work emphasizes on Diabetic Vasculopathy (DV) that is analysed with thermoregulation images through Semantic Segmentation (SS). A novel methodology was adapted, combining thermoregulation imaging with SS using the U-Net++ model to investigate temperature distributions at the skin level. This work introduces a novel method that utilizes MobileNetV2 as the encoder for fast Feature Extraction (FE). Results: The results from the suggested model, achieves a segmentation accuracy of 95%, which is significantly more compared to that of DeepLabV3+ and PSPNet models. A mean and Intersection over Union (IoU) of 85% and 87% was reported by the suggested frameworks throughout the training and validation phases. Conclusion: Classifying normal and abnormal regions can be done via the outcomes, as it offers the great visibility in the thermal image for clinicians by detecting the non-thermal regions

Critical Success Factors of Electric Mobility Transition in Indonesia: A DEMATEL-Based ANP Approach

The high index of poor air quality and the increasing demand for oil fuel pose severe problems for Indonesia. The largest sector contributing to both of these issues is the transportation sector. Electric mobility offers a notable opportunity for decreasing carbon emissions within the transportation domain. Despite the Indonesian government's notable backing of this initiative, the implementation has been slow. Therefore, this study aims to identify the critical factors necessary for a successful transition to electric mobility. This study identifies the factors influencing the transition to e-mobility in the initial stage. Subsequently, these factors are validated and assessed by a panel of nine experts. In the validation phase, data processing involves the use of the Content Validity Index (CVI) and Modified Kappa methods. To assess relationships and prioritize essential success factors, the Decision-making Trial and Evaluation Laboratory-based Analytic Network Process (DEMATEL-based ANP) is applied. This study identifies six dimensions and 24 critical success factors. The political dimension emerges as the most significant, with the top five priorities being government funding, charging infrastructure, value proposition, fuel price, and charging time. The findings can be used to provide recommendations to the government or relevant stakeholders in supporting the shift to electric mobility in Indonesia.

Machine Learning Models to Classify Shiitake Mushrooms (Lentinula edodes) According to Their Geographical Origin Labeling.

The shiitake mushroom has gained popularity in the last decade, ranking second in the world for mushrooms consumed, providing consumers with a wide variety of nutritional and healthy benefits. It is often not clear the origin of these mushrooms, so it becomes of great importance to the consumers. In this research, different machine learning algorithms were developed to determine the geographical origin of shiitake mushrooms (Lentinula edodes) consumed in Korea, based on experimental data reported in the literature (δ13C, δ15N, δ18O, δ34S, and origin). Regarding the origin of shiitake in three categories (Korean, Chinese, and mushrooms from Chinese inoculated sawdust blocks), the random forest model presents the highest accuracy value (0.940) and the highest kappa value (0.908) for the validation phase. To determine the origin of shiitake mushrooms in two categories (Korean and Chinese, including mushrooms from Chinese inoculated sawdust blocks in the latter ones), the support vector machine model is chosen as the best model due to the high accuracy (0.988) and kappa (0.975) values for the validation phase. Finally, to determine the origin in two categories (Korean and Chinese, but this time including the mushrooms from Chinese inoculated sawdust blocks in the Korean ones), the best model is the random forest due to its higher accuracy value (0.952) in the validation phase (kappa value of 0.869). The accuracy values in the testing phase for the best selected models are acceptable (between 0.839 and 0.964); therefore, the predictive capacity of the models could be acceptable for their use in real applications. This allows us to affirm that machine learning algorithms would be suitable modeling instruments to determine the geographical origin of shiitake.

Machine learning-based process design of a novel sustainable cooling system

An exponential rise in cooling requirements occurred in the last few years because of rising global surface temperatures, population growth, faster urbanization, and income growth. Developing countries are facing major issues because of the larger impact of these cooling drivers. Conventional vapor compression systems are energy-demanding and involve dangerous chemicals. The current paper proposed an innovative indirect evaporative cooling system with high energy performance, less emissions, and chemical-free operation. To map the full-scale performance, a prototype was developed,and tested in a variety of outside air conditions. Then artificial neural network (ANN)-based machine learning model was developed incorporating important input parameters including outdoor air temperature, air flow rate ratio, working air temperature, and working air wet bulb temperature to predict the supply air temperature. The ANN model having nine neurons in the hidden layer exhibits excellent modeling performance with a coefficient of determination (R2) value of ∼ 1 and root mean square error of 0.046 °C, 0.06 °C, and 0.06 °C in the training, testing, and validation phases respectively. The variable significance analysis carried out by one factor at a time (OFAT) technique reveals that working air inlet temperature is the most important parameter to predict supply temperature with a significance factor of 33 %. According to the combined experimental and ML model, the proposed system generated 130 W of cooling capacity and dropped the temperature by more than 20 °C at 48 °C of outdoor air. The corresponding coefficient of performance achieved (just for cooling) was 32. It is also shown that the enhanced IEC operates steadily in ambient temperatures ranging from 30 to 48 °C and maintains supply air temperatures within the comfort zone of ASHRAE-55 and ISO7730.

Landslide susceptibility assessment using novel hybridized methods based on the support vector regression

Landslide susceptibility assessment is a complex task due to the multitude of causative factors, spatial variability, data availability, modeling uncertainty, and validation issues. This study addresses these challenges by proposing two predictive models that hybridize support vector regression (SVR) with two evolutionary algorithms: grey wolf optimizer (GWO) and cuckoo search algorithm (CSA). These models were developed using an extensive geospatial database from northern Iran. Over the training phase, the basic predictive model, developed using SVR, was enhanced by incorporating the GWO and CSA algorithms, resulting in the development of two hybridized models: SVR-GWO and SVR-CSA. Over the validation phase, the performance and effectiveness of each hybridized model were compared to the standalone SVR using several metrics. Compared to the standalone SVR model, the hybridized models demonstrated significant improvement in generalization and predictive abilities. Despite non-significant difference between the performances of the SVR-GWO and SVR-CSA models, the SVR-GWO model demonstrated superior performance. This could be attributed to the GWO's capabilities, which included generating a variety of solutions, demonstrating robustness against noise and outliers, achieving faster convergence speed, and effectively interacting with SVR. This study highlighted that utilizing intelligence hybridized models can significantly enhance the balance between accuracy, robustness, and objectives compared to single models. This finding holds significant implications for ecological engineers tasked with designing and implementing solutions to mitigate the impact of shallow landslides on the environment and human communities. The predictive models developed in this study serve as valuable tools for these engineers, enabling them to identify high-risk areas and implement preventative measures. This interdisciplinary approach, which combines machine learning, optimization algorithms, and ecological engineering, highlights the potential for pioneering solutions in tackling complex environmental challenges, thereby standing as a testament to the power of innovation in driving progress in landslide susceptibility assessment.

A supervised machine learning procedure for EPMA classification and plotting of mineral groups

An analytical method to automatically characterize rock samples for geological or petrological purposes is here proposed, by applying machine learning approach (ML) as a protocol for saving experimental times and costs.Proper machine learning algorithms, applied to automatically acquired microanalytical data (i.e., Electron Probe Micro Analysis, EPMA), carried out with a SEM-EDS microprobe on randomly selected areas from a petrographic polished thin section, are trained, used, tested, and reported.Learning and Validation phases are developed with literature mineral databases of electron microprobe analyses on 15 main rock-forming mineral groups. The Prediction phase is tested using an eclogite rock from the Western Alps, considered as an unknown sample: randomly selected areas are acquired as backscattered images whose intervals of gray levels, appropriately set in the gray level histogram, allow the automated particle mineral separation: automated separating Oxford Instruments Aztec Feature ® packages and a mineral plotting software are applied for mineral particle separation, crystal chemical formula calculation and plotting.Finally, a microanalytical analysis is performed on each separated mineral particle. The crystal chemical formula is calculated, and the final classification plots are automatically produced for any determined mineral. The final results show good accuracy and analytical ease and assess the proper nature of the unknown eclogite rock sample. Therefore, the proposed analytical protocol is especially recommended in those scenarios where a large flow of microanalytical data is automatically acquired and needs to be processed.

Experimental and performance validation of a full-scale morphing droop nose design based on composite compliant structures

Active camber morphing technology can be used to improve aircraft performance in takeoff and landing flight conditions, while preserving a smooth wing shape. This study begins with the design of a morphing droop nose to be installed on a regional aircraft, and focuses on the manufacturing and testing of a full-scale and fully representative experimental prototype. All work is driven by the morphing shape change, which was optimized to provide the required aerodynamic performance. The adoption of a composite structure that combines a flexible skin with a compliant structure makes this device capable of achieving such a shape change, and sufficiently insensitive to external load variations. These capabilities are successfully demonstrated through experimental testing. A validation phase was conducted based on strain gauge measurements, and a motion capture system was used to identify three-dimensional shape changes due to the morphing. Finally, a validated numerical model is used to assess the aerodynamic performance of the experimental prototype.

Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy

A suitable waste-management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and it requires precise data on the volume of demolition waste (DW) generated. Therefore, we developed an optimal machine learning model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. We constructed a dataset comprising information on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, landfill mix waste, and recyclable minerals). We applied an artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest, and support vector regression. Further, we derived the optimal model through data preprocessing, input variable selection, and hyperparameter tuning. In both the validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies (R2) of 0.987 and 0.972, respectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies (R2) of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. This study confirmed through Shapley Additive exPlanations analysis that the floor area is the most important input variable in the four models (i.e., recyclable mineral waste, recyclable combustible waste, recyclable metals, and landfill mix waste). Additionally, the type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill waste generated. The results of this study can provide more detailed information on the generation of recycling and landfill waste. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Copy number variation and clinical response to chemotherapy and bevacizumab in the Czech metastatic colorectal cancer patients.

Despite bevacizumab being the first biological agent approved for the treatment of metastatic colorectal cancer (mCRC), there is not any established DNA biomarker to improve its efficacy and personalize the treatment. Thirty patients with mCRC on bevacizumab therapy (15 with a good response and 15 with a poor response) from the University Hospital Olomouc were followed. Formalin-fixed paraffin-embedded (FFPE) samples were used for copy number variation (CNV) analysis using the OncoScan FFPE Assay Kit in order to capture approx. 900 tumor genes. In the group of good responding patients, 102 genes (classified as ATPases, type AAA, neuronal signal transmission, regulation of transcription, and superior domain PH type), potentially significant positive predictive tumor biomarkers of bevacizumab treatment, were found. In the poorly responding group, 74 potentially negative predictive genes (classified as galectines, Jak-STAT signalling pathway, MAPK cascade, differentiation, and F-box associated domain) were identified. In the pilot study, we found promising copy number variation biomarkers of bevacizumab response in FFPE samples of mCRC patients. The validation phase should be focused especially on the genes associated with angiogenesis (AGRN, MAPK8, ARHGAP22, LGALS13, LGALS4, ZFP36, and MYC), tumorigenesis (DVL1), and tumor proliferation (IFNL1, IFNL2, IFNL3, MAP3K10, and MAP4K1).

Competency development for pharmacy: adopting and adapting the FIP global advanced development framework.

Pharmacy education shifts toward competency-based training to meet healthcare demands. This study aims to develop and validate the Kuwait Advanced Competency Framework (KACF) for pharmacists. The study adopts the FIP Global Advanced Development Framework (GADF) to develop a country-specific framework, emphasizing the importance of aligning with global standards while adapting to local contexts. The developed framework builds upon the Kuwait Foundation Competency Framework to address the need for advanced pharmacy services. This is a mixed methods study that employed an "adopt and adapt" approach. The KACF was adopted from the FIP GADF and adapted following four phases. Phase one involved checking and validating the Arabic version of the FIP GADF. Phase two employed a series of focus groups to validate accuracy and relevancy of competency statements. Phase three utilized a workshop with different stakeholders as a final step of validation. Phase four involved a national survey to assess the national pharmacy workforce against the framework competencies. Qualitative feedback from focus groups and workshops informed competencies modifications. Quantitative data were analyzed using descriptive and multiple correspondence analyses (MCA). The translation phase verified a bilingual framework that could be utilized by pharmacists in Kuwait. The initial and final validation phases identified 20 behavioral statements (out of 22 in the original document) that are relevant to pharmacy practice in Kuwait. The national survey, comprising 169 respondents, validated the KACF's applicability, revealing variations in career stage progression across competency clusters. Findings highlighted associations between career stages and practice settings, offering insights for tailored workforce development strategies. The KACF emerges as a pivotal tool for advancing pharmacy services in Kuwait, aligning with global trends toward competency-based education. Findings underscored the necessity for context-specific approaches in advancing pharmacy practice, providing a comprehensive understanding of competency progression and readiness for advanced roles.

Building a performance measurement framework for telephone triage services in Finland: a consensus-making study based on nominal group technique

BackgroundThere has been a significant expansion in the measurement of healthcare system performance. However, there is a lack of a comprehensive performance measurement framework to assess the effects of telephone triage services on the urgent care system. The aim of our Delphi study was to construct and validate a performance measurement framework designed explicitly for telephone triage services.MethodsThis study was conducted in Finland with a group of eight experienced senior physicians from the country's 20 largest joint emergency departments, serving over 90% of the population for urgent care. The Nominal Group Technique (NGT) was utilised to achieve consensus on measuring telephone triage performance. Initially, performance indicators (PIs) were identified through Delphi method rounds from December 10th to December 27th, 2021, with eight experts participating, and from December 29th, 2021, to January 23rd, 2022, where five of these experts responded. NGT further deepened these themes and perspectives, aiding in the development of a comprehensive performance measurement framework. The final framework validation began with an initial round from February 13th to March 3rd, 2022, receiving five responses. Due to the limited number of responses, an additional validation round was conducted from October 29th to November 7th, 2023, resulting in two more responses, increasing the total number of respondents in the validation phase to seven.ResultsThe study identified a strong desire among professionals to implement a uniform framework for measuring telephone triage performance. The finalised framework evaluates telephone triage across five dimensions: service accessibility, patient experience, quality and safety, process outcome, and cost per case. Eight specific PIs were established, including call response metrics, service utility, follow-up care type and distribution, ICPC-2 classified encounter reasons, patient compliance with follow-up care, medical history review during assessment, and service cost per call.ConclusionsThis study validated a performance measurement framework for telephone triage services, utilising existing literature and the NGT method. The framework includes five key dimensions: patient experience, quality and safety, outcome of the telephone triage process, cost per case, and eight PIs. It offers a structured and comprehensive approach to measuring the overall performance of telephone triage services, enhancing our ability to evaluate these services effectively.

Guided wave-driven machine learning for damage classification with limited dataset in aluminum panel

The goal of this research study is to classify damages in an aluminum panel using ultrasonic-guided waves (UGWs). The study is carried out on the premise that only a limited dataset, consisting of only two receivers, is available. In the experimental phase, three distinct parts, that is, stainless steel masses, were located on the surface of the panel, assumed to be mapped into four triangular regions, to experimentally simulate the effects induced by circular artificial damage of varied sizes. UGW-driven damage classification was thus performed via machine learning. A data synthesis technique called conditional generative adversarial network (cGAN) was used to generate 2000 samples (signals) for each of the 12 different malfunction scenarios (classes), based on the combination of damage region and size. Damage features were extracted using the wavelet time scattering technique, while the classification task was performed via a long short-term memory network with tuned hyperparameters. A validation procedure was executed to ensure that the classification network was not overfitted. Furthermore, two testing stages were performed to evaluate the designed framework. The designed framework was thus tested on the observations excluded from the training and validation phases, as well as on 10 additional novel experimental samples. The high accuracy and F1-score values of the validation and the two testing stages—97.7%, 98.5%, and 96.6%, respectively—attest to the generality of this methodology, which is neither underfitted nor overfitted. To further assess the robustness of the methodology, the cGAN was tested on a new sensor placement, demonstrating a consistent performance (95.3%) and confirming the framework applicability under different sensor configurations, showcasing potential extension to other classification problems featuring a limited dataset.

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.