A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.

Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models.

Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.

Predicting binding affinities of emerging variants of SARS-CoV-2 using spike protein sequencing data: observations, caveats and recommendations.

Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein-protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures.

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

Comprehensive evaluation of computational methods for predicting cancer driver genes.

Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein-protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework.

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques.

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data's underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data's complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

Energetic Comparison of Flow-Electrode Capacitive Deionization and Membrane Technology: Assessment on Applicability in Desalination Fields.

Flow-electrode capacitive deionization (FCDI) is a promising technology for sustainable water treatment. However, studies on the process have thus far been limited to lab-scale conditions and select fields of application. Such limitation is induced by several shortcomings, one of which is the absence of a comprehensive process model that accurately predicts the operational performance and the energy consumption of FCDI. In this study, a simulation model is newly proposed with initial validation based on experimental data and is then utilized to elucidate the performance and the specific energy consumption (SEC) of FCDI under multiple source water conditions ranging from near-groundwater to high salinity brine. Further, simulated pilot-scale FCDI system was compared with actual brackish water reverse osmosis (BWRO) and seawater reverse osmosis (SWRO) plant data with regard to SEC to determine the feasibility of FCDI as an alternative to the conventional membrane processes. Analysis showed that FCDI is competent for operation against brackish water solutions under all possible operational conditions with respect to the BWRO. Moreover, its distinction can be extended to the SWRO for seawater conditions through optimization of its total effective membrane area via scale-up. Accordingly, future directions for the advancement of FCDI was suggested to ultimately prompt the commercialization of the FCDI process.

Performance evaluation of a novel high-sensitivity cardiac troponin T assay: analytical and clinical perspectives.

OBJECTIVES: To evaluate the analytical characteristics of a novel high-sensitivity cardiac troponin T (hs-cTnT) test on the automatic light-initiated chemiluminescent assay (LiCA®) system, and validated its diagnostic performance for non-ST-segment elevation myocardial infarction (NSTEMI). METHODS: Studies included an extensive analytical evaluation and established the 99th percentile upper reference limit (URL) from apparently healthy individuals, followed by a diagnostic performance validation for NSTEMI. RESULTS: Sex-specific 99th percentile URLs were 16.0 ng/L (1.7 % CV: coefficient of variation) for men (21-92 years) and 13.4 ng/L (2.0 % CV) for women (23-87 years) in serum, and 30.6 ng/L (0.9 % CV) for men (18-87 years) and 20.2 ng/L (1.4 % CV) for women (18-88 years) in heparin plasma. Detection rates in healthy individuals ranged from 98.9 to 100 %. An excellent agreement was identified between LiCA® and Elecsys® assays with a correlation coefficient of 0.993 and mean bias of -0.7 % (-1.8-0.4 %) across the full measuring range, while the correlation coefficient and overall bias were 0.967 and -1.1 % (-2.5-0.3 %) for the lower levels of cTnT (10-100 ng/L), respectively. At the specific medical decision levels (14.0 and 52.0 ng/L), assay difference was estimated to be <5.0 %. No significant difference was found between these two assays in terms of area under curve (AUC), sensitivity and specificity, negative predictive value (NPV) and positive predictive value (PPV) for the diagnosis of NSTEMI. CONCLUSIONS: LiCA® hs-cTnT is a reliable 3rd-generation (level 4) high-sensitivity assay for detecting cardiac troponin T. The assay is acceptable for practical use in the diagnosis of NSTEMI.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

OBJECTIVES: Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. METHODS: QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. RESULTS: Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size and offering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. CONCLUSIONS: In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering. Specifically, the role of feature selection and extraction in enhancing model accuracy has been underexplored. OBJECTIVES: This scoping review aims to fulfill two primary objectives: To identify pivotal features for predicting sepsis across a variety of ML models, providing valuable insights for future model development, and To assess model efficacy through performance metrics including AUROC, sensitivity, and specificity. RESULTS: The analysis included 29 studies across diverse clinical settings such as Intensive Care Units (ICU), Emergency Departments, and others, encompassing 1,147,202 patients. The review highlighted the diversity in prediction strategies and timeframes. It was found that feature extraction techniques notably outperformed others in terms of sensitivity and AUROC values, thus indicating their critical role in improving sepsis prediction models. CONCLUSION: Key dynamic indicators, including vital signs and critical laboratory values, are instrumental in the early detection of sepsis. Applying feature selection methods significantly boosts model precision, with models like Random Forest and XG Boost showing promising results. Furthermore, Deep Learning models (DL) reveal unique insights, spotlighting the pivotal role of feature engineering in sepsis prediction, which could greatly benefit clinical practice.

Air quality modeling in the metropolitan area of São Paulo, Brazil: A review.

Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models' physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.

Hospital performance evaluation indicators: a scoping review.

BACKGROUND: Hospitals are the biggest consumers of health system budgets and hence measuring hospital performance by quantitative or qualitative accessible and reliable indicators is crucial. This review aimed to categorize and present a set of indicators for evaluating overall hospital performance. METHODS: We conducted a literature search across three databases, i.e., PubMed, Scopus, and Web of Science, using possible keyword combinations. We included studies that explored hospital performance evaluation indicators from different dimensions. RESULTS: We included 91 English language studies published in the past 10 years. In total, 1161 indicators were extracted from the included studies. We classified the extracted indicators into 3 categories, 14 subcategories, 21 performance dimensions, and 110 main indicators. Finally, we presented a comprehensive set of indicators with regard to different performance dimensions and classified them based on what they indicate in the production process, i.e., input, process, output, outcome and impact. CONCLUSION: The findings provide a comprehensive set of indicators at different levels that can be used for hospital performance evaluation. Future studies can be conducted to validate and apply these indicators in different contexts. It seems that, depending on the specific conditions of each country, an appropriate set of indicators can be selected from this comprehensive list of indicators for use in the performance evaluation of hospitals in different settings.

The role of perspective in refereeing: Insights from a video-based decision-making experiment.

Decision accuracy is a crucial factor in the evaluation of refereeing performance. In sports research, officials' decision-making is frequently assessed outside real games through video-based decision experiments, where they evaluate recorded game situations from a third-person perspective. This study examines whether the inclusion of the first-person perspective influences decision accuracy and certainty. Twenty-four professional officials from the first and second German basketball leagues participated in the study. The officials assessed 50 game situations from both first-person and third-person perspectives, indicating their decisions and certainty levels. The statistical analysis utilises signal detection theory to evaluate the efficacy of the first-person perspective compared to the third-person perspective in identifying rule violations and no-calls in video recordings. The findings indicate that the first-person perspective does not yield superior accuracy in identifying foul calls. However, scenes from the first-person perspective exhibit a significant 9% increase in correctly identifying no-calls. Furthermore, officials report significantly higher levels of decision certainty and comfort when using the first-person perspective. The study suggests that sports officials may benefit from incorporating additional scenes from the first-person perspective into video-based decision training. Future studies should explore whether this additional perspective improves the training effect and translates into enhanced in-game performance.

Performance evaluation of a computer-aided polyp detection system with artificial intelligence for colonoscopy.

OBJECTIVES: A computer-aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand-alone performance of this device under blinded conditions. METHODS: This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. RESULTS: Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8-98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3-95.8%). For the frame-based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8-88.4%), 84.7% (95% CI 83.8-85.6%), 34.9% (95% CI 32.3-37.4%), and 98.2% (95% CI 97.8-98.5%), respectively. TRIAL REGISTRATION: University Hospital Medical Information Network (UMIN000044622).

Novel Reference Method for the Characterization of PD Measuring Systems Using HFCT Sensors.

During their lifespan, high-voltage (HV) electrical systems are subjected to operating conditions in which electrical, mechanical, thermal and environmental-related stresses occur. These conditions over time lead to unforeseen failures caused by various types of defects. For this reason, there are several technologies for measuring and monitoring the electrical systems, with the aim of minimizing the number of faults. The early detection of defects, preferably in their incipient state, will enable the necessary corrective actions to be taken in order to avoid unforeseen failures. These failures generally lead to human risks and material damage, lack of power supply and significant economic losses. An efficient maintenance technique for the early detection of defects consists of the supervision of the dielectrics status in the installations by means of on-line partial discharge (PD) measurement. Nowadays, there are numerous systems in the market for the measurement of PD in HV installations. The most efficient with a reasonable cost will be those that offer greater security guarantees and the best positioned in the market. Currently, technology developers and users of PD measuring systems face difficulties related to the lack of reference procedures for their complete characterization and to the technical and economic drawback of performing the characterization tests on site or in laboratory installations. To deal with the previous difficulties, in this paper a novel method for the complete and standardized characterization of PD measuring systems is presented. The applicability of this method is mainly adapted for the characterization of systems operating in on-line applications using high-frequency current transformer (HFCT) sensors. For the appropriate application of the method, an associated and necessary scale modular test platform is used. In the test platform, the real on-site measuring conditions of an HV insulated distribution line are simulated in a controlled way. Practical characterizations, showing the convenience and advantages of applying the method using the modular test platform, are also presented.

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm.

The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination and high-dust environments in the coal mine working face, a coal and gangue recognition model based on the improved YOLOv7-tiny target detection algorithm is proposed. This paper proposes three model improvement methods. The coordinate attention mechanism is introduced to improve the feature expression ability of the model. The contextual transformer module is added after the spatial pyramid pooling structure to improve the feature extraction ability of the model. Based on the idea of the weighted bidirectional feature pyramid, the four branch modules in the high-efficiency layer aggregation network are weighted and cascaded to improve the recognition ability of the model for useful features. The experimental results show that the average precision mean of the improved YOLOv7-tiny model is 97.54%, and the FPS is 24.73 f·s-1. Compared with the Faster-RCNN, YOLOv3, YOLOv4, YOLOv4-VGG, YOLOv5s, YOLOv7, and YOLOv7-tiny models, the improved YOLOv7-tiny model has the highest recognition rate and the fastest recognition speed. Finally, the improved YOLOv7-tiny model is verified by field tests in coal mines, which provides an effective technical means for the accurate identification of coal and gangue.

Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs.

This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.

Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors.

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system's functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems.