Reliable Data Analysis Research Articles

Enhancing real world data interoperability in healthcare: A methodological approach to laboratory unit harmonization

ObjectiveThe primary aim of this study is to address the critical issue of non-standardized units in clinical laboratory data, which poses significant challenges to data interoperability and secondary usage. Despite UCUM (Unified Code for Units of Measure) offering a unique representation for laboratory test units, nearly 60% of laboratory codes in healthcare organizations use non-standard units. We sought to design, implement and test a methodology for the harmonization of units to the UCUM standards across a large research network. MethodsUsing dimensional analysis and a curated equivalence table, the proposed methodology harmonizes disparate units to UCUM standards. The process focused on identifying and converting non-UCUM conforming units, with the goal of enhancing data comparability and interoperability across different systems. ResultsThe methodology successfully achieved over 90% coverage of laboratory data with units in UCUM standards across the TriNetX research network, a significant improvement from baseline measurements. This enhancement in unit standardization directly contributed to increased interoperability of laboratory data, facilitating more reliable and comparable data analysis across various healthcare organizations. ConclusionThe successful harmonization of laboratory data units to UCUM standards represents a significant advancement in the field of biomedical informatics. By demonstrating a practical and effective approach to overcoming the challenges of non-standardized units, our study contributes to the broader efforts to improve data interoperability and usability for secondary purposes such as research and observational studies. Future work will focus on addressing the remaining gaps in unit standardization and exploring the implications of this methodology on clinical outcomes and research capabilities.

Transient Analysis of Flow Unsteadiness And Machine Instabilities In a Centrifugal Compressor at Surge and Second Quadrant Operation

Abstract Instabilities in turbomachinery operation originate from intrinsic flow unsteadiness, sudden variations of operating conditions, and interactions with the surrounding system, affecting both system stability and machine reliability. The flow unsteadiness, presenting a characteristic behaviour, requires a deep understanding of the underlying flow fundamentals, a careful analysis using advanced experimental methods and a clear distinction on how the system might lead to instabilities. The identified approach addresses deep surge and second quadrant characterization. The analysis presented here benefits from the responsiveness and flexibility of the experimental test rig, fine tuning of the system layout, the instrumentation installation, and the transient tests procedure. The main focus area is the time-frequency analysis of flow and pressure signals at transients, thus providing a proper reconstruction of flow mechanisms and instability evolution. Valuable techniques include: short-time fast Fourier transform, wavelet analysis, and Wigner-Ville distribution; coherence between flow and pressure, to trace waves propagation, is considered too. These, presented outlining their strengths and weaknesses, are evaluated considering real time field applicability with regards to computational resources too. The experimental campaigns and data processing are presented, with a test matrix covering a wide range of parameters, including system layout and surge volume size, rotational speeds, flow rates ranging from nominal point to full second quadrant operation. These data are vital for system modeling validation for a full compressor-system digital twin. Results indicate the importance of ensuring reliable data acquisition and analysis, with adequate instrumentation range, accuracy, synchronization, responsiveness and positioning.

Relationship between temporomandibular and sleep disorders in adults: An overview of systematic reviews

ObjectiveThis study emphasizes the general relevance of sleep disorders (SD) and temporomandibular disorders (TMD), explores their bidirectional relationship, and describes the importance of systematic reviews in the critical analysis of the literature. This review aimed to comprehensively summarize the relationship between SD and TMD in adults while ensuring a reliable and objective analysis of data from the existing literature. MethodsSystematic reviews were evaluated to investigate this association between two conditions in adults. The study was registered with Prospero and followed the PECOT structure in identifying the research question. Searches in multiple databases were conducted until February 2024 using relevant keywords. The risk of bias was assessed using the Risk of Bias in Systematic Reviews tool, involving two independent reviewers. ResultsData were extracted using different evaluation instruments, and results were analyzed and presented through the synthesis of information collected in selected systematic reviews. The selection included seven systematic reviews of observational studies, with exclusion criteria defined to ensure methodological quality. The results showed that reviewed studies presented clarity and relevance in defining the eligibility criteria, but that the identification and selection of studies, data collection, and synthesis of results varied. Most studies considered the potential risks, but some require greater transparency and methodological rigor. ConclusionsThis review points out the association between SD and TMD in adults, with most studies presenting a low risk of bias, although some uncertainties were observed.

An Ensemble Learning Algorithm with Hyperparameter Optimization (ELAHO) Model for Early Detection of Maternal Health Risk (MHR) Level Using Machine Learning

Despite the advancement in the healthcare system, maternal health risk remains high, which is the most challenging aspect nowadays. There is a need to develop an effective model for early detection, monitoring, and prediction of maternal health risk levels during pregnancy. The machine learning intelligence model has proven its effectiveness and robustness in providing accurate and reliable prediction, analysis, and interpretation of medical data, reducing several risk factors for early diagnosis in healthcare. In this research work, we proposed an ensemble learning algorithm with a hyperparameter optimization (ELAHO) model using machine learning algorithms to improve its robustness, effectiveness, and model performance. The proposed method uses a hybrid model of logistic regression and support vector machines (LG-SVM) to predict maternal health risk levels during pregnancy. The method utilized Python software for training, testing, and validation. We evaluated the performance of our proposed model using accuracy, precision, sensitivity, f1-score, and ROC-AUC score. The proposed models outperformed the conventional models and achieved 100% predictive accuracy. The proposed approach has the potential to be adapted as an intelligence-monitoring system for early medical diagnosis during pregnancy. The proposed techniques will help medical professionals make quick decisions accurately and enhance monitoring to improve the level of care offered to pregnant mothers and their unborn children.

An unbiased comparison of immunoglobulin sequence aligners.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our understanding of the adaptive immune system's dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate rearranged immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, a modular simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.

Identification of a New and Effective Marker Combination for a Standardized and Automated Bin-Based Basophil Activation Test (BAT) Analysis.

(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

AI-assisted ultrasonic wave analysis for automated classification of steel corrosion-induced concrete damage

Early detection of cracks in reinforced concrete caused by chloride intrusion is crucial for effective maintenance. This paper develops and compares AI-assisted models that use ultrasonic pulse waves to automatically assess early-stage concrete damage, particularly cracks caused by steel corrosion. Data were collected from 108 concrete cubes with various mixture designs, cover depths, and steel corrosion levels induced by the impressed current technique. The AI models, particularly convolutional neural networks (CNNs), outperformed traditional regression models, achieving up to 84% classification accuracy. The systematic and automated features of the AI-assisted algorithm enable reliable, consistent and rapid ultrasonic wave data analysis, which is especially beneficial for the condition assessment of large infrastructure systems. This advancement inspires further research into integrating automated IoT and robotics-assisted systems for comprehensive infrastructure monitoring.

Persepsi Masyarakat Terhadap Akhlak Remaja Dalam Berinteraksi Sosial Di Desa Purbasari Kec. Tapian Dolok Kab. Simalungun

This research provides a fairly comprehensive picture of the importance of understanding adolescent morals in the social context of society, especially in Purbasari Village. Among the emphases discussed are the role of adolescent morals in social interaction, the urgency of fostering adolescent morals, and the impact of community perceptions on adolescent morals. Descriptions of the definition of adolescence, social change, and the pressures faced by adolescents provide a strong foundation for the reader's understanding of the relevance of this research. This study aims to determine the community's perception of adolescent morals and to determine the morals of adolescents in Purbasari Village, Tapian Dolok Subdistrict, Simalungun Regency. The method used in this research is a descriptive quantitative method that provides a good structure in describing the results of the research. The data collection technique in this research is in the form of observation and distributing questionnaires using a Likert scale. Data validity in this study uses validity and reliability data analysis. The results showed that the community's perception of adolescent morals from the religious, social, and ethical aspects provided deep insight into how the community in Purbasari Village assessed adolescent morals in social interaction. Furthermore, the discussion of community perceptions of adolescent morals provides good insight into the social dynamics in society, the factors that influence community perceptions, and the factors that influence community perceptions.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

Improving the Information and Legal Support of the Judicial System of Ukraine: Experience of the European Court of Human Rights

The research proposes a comprehensive approach to modernizing the information and legal support (ILS) of the judicial system of Ukraine, given European integration processes and the war of Russia against Ukraine. The need for substantial modernization of existing court automation systems to integrate with European systems and improve analytical capabilities for processing large data sets was identified. It was specified that the legal framework for the functioning of court information systems (IS) must be adapted to European legislation, and the organization of data warehouses and information flows must comply with EU standards. The principles of the functioning of court information systems should ensure the possibility of easy integration with the IS of the EU. To investigate war crimes committed by Russia in Ukraine, it is proposed to establish reliable ILS, given the case law of the European Court of Human Rights (ECHR) in cases of human rights violations during conflicts. An innovative approach based on the use of artificial intelligence (AI) tools is proposed to increase the efficiency of judicial decision analysis and improve the ILS of the judicial system of Ukraine. It has been proven that the integration of the judicial systems of Ukraine and the EU is possible, provided there are reliable data analysis tools, process standardization, digitalization, data security, and integrated registers. The effectiveness of applying IT tools in the ILS of courts has been confirmed. It is proposed to establish specialized hybrid courts in Ukraine with international experts to address war crimes, harmonize legislation with international law, and implement the case law of the ECHR.

Analisis Butir Soal Ulangan Harian Siswa Mata Pelajaran PPKn Kelas IV Menggunakan Rach Model di Madrasah Ibtidaiyah Negeri

The study aimed to analyze the items on students' daily test questions on PPKN subjects made by the teacher to measure learning outcomes during the previous lesson process. Conducted on fourth-grade students at the 01 West Pasaman State Madrasah Ibtidayah. The method used in this research is descriptive quantitative using Rasch model data analysis with Win Step software. The results of the data analysis carried out were on the reliability data analysis using Winstep Cronbach Alpha 0.49 software. From the existing data, it is clear that the level of reliability of the questions is still relatively weak. The results of the Rasch model of person reliability and item reliability are 0.47 and 0.52. The results of the analysis of the quality of the items in terms of the validity of the items using the Rasch model obtained 6 questions that were said to be valid and 24 questions that were said to be invalid because they did not meet the requirements for the MNSQ outfit, ZSTD outfit, and the Point Measure correlation (PT measure Corr). In using the Rasch model it is felt to be more accurate because it meets these three criteria, so it is considered valid. above the P value or the level of difficulty of the items below 0.25, there are 3 items, meaning the item is very difficult. Questions that are above 0.75 have 5 items meaning the question is too easy. And questions that are scored between 0.25-0.75 have 17 questions. That is, 18 questions have a good level of difficulty. Analysis of distracting items using the Winstep Rasch Model software for 25 daily test questions for students of Civics class IV at Madrasah Ibtidayah Negeri 01 Pasaman Barat shows that there are 23 questions with good functioning distractors. Items where the distractor does not function properly are 5 questions.

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

From Lima to Canton and Beyond: Mobile and Digital Research Infrastructure for Closing the Gap Between Resource-rich and Resource-poor Organisations

ABSTRACT Access to research infrastructure for heritage science is the best way of narrowing the collections knowledge gap between resource-rich and resource-poor cultural institutions. Here we demonstrate a novel workflow that streamlines the use of a mobile laboratory for data collection (MOLAB) followed by a digital platform for data processing, analysis and interpretation (DIGILAB). The data collection and processing are taken care of by heritage science specialists, while the data analysis and interpretation can be performed collaboratively with conservators and curators. MOLAB with specialist personnel and mobile scientific instruments can sometimes be aided by in-house fixed laboratory (FIXLAB) equipment and personnel from resource-rich institutions to assist the resource-poor institutions if their collection can be hosted by the resource-rich institutions for analysis. The aim is to widen participation in the adoption of heritage science and material analysis in the understanding and conservation of collections, while ensuring high-quality, efficient and reliable data acquisition, processing, analysis and interpretation. The approach taken in this project combines access to various research infrastructure platforms to achieve extra benefit through adding further expert-assisted data analysis and interpretation capabilities, enabled by a new digital platform for data processing and visualisation.

Design and Implementation of a Physical Education Teaching and Training Mode Management System

With the advent of the smart age, all industries are undergoing revolutionary changes, and more reliable, faster, and unmanned data analysis and mining have become a prominent feature of today's industry. With basketball as an iconic sport, better data processing models will drive sports games to be played more efficiently. As an essential sport in China, basketball has more than 100 years of history. With the leap development of basketball, data analysis technology has also been widely used. The data seen in basketball games is a set of systematic data formed by professional workers through division of labor and close cooperation, classification and aggregation, and statistical analysis. In addition, based on the information on the court, spectators get a general idea of how the players perform in the Game and predict where the data will go. This article explores the significance and application of basketball game data analysis methods and shows that the analysis of basketball game data can effectively promote the development of basketball.

Evaluating Source, Scale Risk, and Corrosion Risk of the Produced Water Samples from the Appalachian Basin Based on a Geochemical Database

Summary Statistically, oil and gas production can generate up to 20 times the oil equivalent of produced water. The composition of produced water samples reflects its source, its interactions with reservoir rocks, and downhole (DH) facilities, which are critical for basin evolution, water source determination, and the monitoring, management, and optimization of oil and gas production. For example, scale and corrosion, two of the most severe flow assurance issues accompanied by produced water, can lead to billions of dollars lost every year. However, few studies have developed a standard protocol to extract such valuable information from produced water compositions due to a lack of data and professional models. Using produced water geochemical data from the Appalachian Basin, one of the largest natural gas producers in the US, from the United States Geological Survey (USGS) Produced Waters Geochemical Database (PWGD), we developed a standard protocol to investigate the produced water source, evolution history, and scale and corrosion risks under both DH and surface conditions by means of incorporating the professional models for water-rock interaction and corrosion. The results show that the produced water from the Appalachian Basin possibly evolves from seawater evaporation following a typical evolution pattern of ion concentration and water isotopes, while a group of time-elapsed samples indicates that such an evolution pattern can also be due to the mixture of the injected water and reservoir water. In addition, most produced water samples show obvious risks of mineral scaling (e.g., calcite, barite, and siderite) and CO2 corrosion with corresponding mitigation strategies recommended. This study not only developed a reliable data processing and analysis protocol but also showed the valuable information a systematic analysis of produced water samples can provide for actual oil and gas production.

HealthyPlan.City: A Web Tool to Support Urban Environmental Equity and Public Health in Canadian Communities

Urban environmental factors such as air quality, heat islands, and access to greenspaces and community amenities impact public health. Some vulnerable populations such as low-income groups, children, older adults, new immigrants, and visible minorities live in areas with fewer beneficial conditions, and therefore, face greater health risks. Planning and advocating for equitable healthy urban environments requires systematic analysis of reliable spatial data to identify where vulnerable populations intersect with positive or negative urban/environmental characteristics. To facilitate this effort in Canada, we developed HealthyPlan.City (https://healthyplan.city/), a freely available web mapping platform for users to visualize the spatial patterns of built environment indicators, vulnerable populations, and environmental inequity within over 125 Canadian cities. This tool helps users identify areas within Canadian cities where relatively higher proportions of vulnerable populations experience lower than average levels of beneficial environmental conditions, which we refer to as Equity priority areas. Using nationally standardized environmental data from satellite imagery and other large geospatial databases and demographic data from the Canadian Census, HealthyPlan.City provides a block-by-block snapshot of environmental inequities in Canadian cities. The tool aims to support urban planners, public health professionals, policy makers, and community organizers to identify neighborhoods where targeted investments and improvements to the local environment would simultaneously help communities address environmental inequities, promote public health, and adapt to climate change. In this paper, we report on the key considerations that informed our approach to developing this tool and describe the current web-based application.

Sibyll[formula omitted]

In the last decade, an increasing number of datasets have revealed a consistent discrepancy between the number of muons measured in ultra-high-energy extensive air showers (EAS) and the numbers predicted by simulations. This gap persists despite incorporating Large Hadron Collider (LHC) data into the tuning of current hadronic interaction models, leading to the phenomenon often termed the “muon puzzle”. To gain a deeper understanding of the potential origins of this muon puzzle, we have developed Sibyll★, a series of phenomenologically modified versions of Sibyll 2.3d. In these models, we have increased muon production by altering ρ0, baryon–antibaryon pair, or kaon production in hadronic multiparticle production processes. These variants remain within bounds from provided by accelerator measurements, including those from the LHC and fixed-target experiments, notably NA49 and NA61, showing a level of consistency comparable to Sibyll 2.3d. Our findings show that these modifications can increase the muon count in EAS by up to 35%, while minimally affecting the depth of shower maximum (Xmax) and other shower variables. Additionally, we assess the impact of these modifications on various observables, including inclusive muon and neutrino fluxes and the multiplicities of muon bundles in deep underground and water/ice Cherenkov detectors. We aim for at least one of these model variants to offer a more accurate representation of EAS data at the highest energies, thereby enhancing the quality of Monte Carlo predictions used in training neural networks. This improvement is crucial for achieving more reliable data analyses and interpretations.

STATISTICAL TECHNIQUES IN PRECISION METROLOGY, APPLICATIONS AND BEST PRACTICES

Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.
 Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.

Hydrogen production via electrolysis: State-of-the-art and research needs in risk and reliability analysis

Water electrolysis is the primary production technology for clean hydrogen, and thus ensuring safe and reliable deployment and operation of electrolyzers is essential to global sustainability efforts. In this paper, electrolytic hydrogen production pathways and upcoming deployment capacities are briefly discussed. Then we examine the main risk and reliability challenges and analyze the state of the art in quantitative risk assessment (QRA) of electrolysis technologies. We identify gaps in electrolysis QRA studies and present recommendations to enable more robust risk and reliability analysis for these technologies. Recommendations and opportunities for research to fill these gaps include: developing and adopting a system reliability approach in electrolysis risk analysis, developing and disseminating detailed electrolysis system design documentation, developing reliability data collection and analysis methods and tools, conducting importance measures analysis, conducting uncertainty analysis and propagation, developing electrolysis QRA standards, and creating programs to develop and educate the hydrogen risk and reliability workforce.