Data Quality Evaluation Research Articles

Online Nonprobability Samples

Online nonprobability samples provide social scientists with opportunities to conduct surveys and experiments on large, diverse samples at modest prices. Researchers may find bewildering the options offered by the many commercial entities that provide research participants, and our review seeks to orient researchers to key issues about their use. We discuss principles and evidence regarding estimates from nonprobability samples versus those from probability samples. We also describe methods for addressing certain types of problem participants that one encounters in these samples: professional respondents, participants who are inattentive or have low linguistic competence, and bogus participants (increasingly in the form of bots). We urge researchers not to take data quality for granted, not to rely on indirect information to vouch for data quality, and to proactively build methods that allow for the evaluation of data quality into their instruments.

Pig and quail CpG methylation datasets from short and long read sequencing technologies

CpG methylation, a key epigenetic mark involved in gene regulation, development, and other biological processes, is commonly analyzed using Whole-Genome Bisulfite Sequencing (WGBS). However, bisulfite treatment causes significant DNA degradation. Enzymatic Methyl-seq (EM-seq) offers a short-read alternative that preserves DNA integrity but requires conversion steps, limiting its compatibility with downstream analyses. Third-generation sequencing technologies, such as Oxford Nanopore Technologies (ONT) and PacBio, enable direct detection of DNA modifications without altering the DNA, providing simultaneous genome and epigenome information. This work presents a comprehensive dataset combining long- and short-read sequencing data, including ONT, PacBio, Enzymatic Methyl-seq, and WGBS, for two agronomically relevant species: pig (Sus scrofa) and quail (Coturnix japonica). Data quality evaluation reveals high nucleotide quality scores for PacBio and short reads, robust alignment rates for long reads, and inter-method correlations in CpG methylation calling ranging from 0.76 to 0.99. This dataset is a valuable resource for training methylation callers and represents the first combined methylation dataset for these species, providing an essential benchmark for assessing emerging sequencing technologies.

Bridging Perceived and Actual Data Quality: Automating the Framework for Governance Reliability

The discrepancy between perceived and actual data quality, shaped by stakeholders’ interpretations of technical specifications, poses significant challenges in governance, impacting decision-making and stakeholder trust. To address this, we introduce an automated data quality management (DQM) framework, implemented through the NRPvalid toolkit, as a standalone solution incorporating over 100 assessment tools. This framework strengthens data quality evaluation and stakeholder collaboration by systematically bridging subjective perceptions with objective quality metrics. Unlike traditional producer–user models, it accounts for complex, multi-stakeholder interactions to improve data governance. Applied to planned land use (PLU) data, the framework significantly reduces discrepancy, as quantified by error score metrics, and directly enhances building permit issuance by streamlining interactions among administrative units, municipalities, and investors. By evaluating, refining, and seamlessly integrating spatial data into the enterprise spatial information system, this scalable, automated solution supports constant data quality improvement. The DQM and its toolkit have been widely adopted, promoting transparent, reliable, and efficient geospatial data governance.

The Choice of Hip Arthroplasty: HRA or THA? Revealed by Meta-Analysis.

Total hip arthroplasty (THA) is the gold standard for end-stage hip arthrosis, while hip resurfacing arthroplasty (HRA) is considered a more bone-conserving alternative. This meta-analysis aimed to compare the safety and clinical efficacy of HRA and THA. The databases of PubMed, EMBASE, Cochrane Library, and CNKI were searched for RCTs comparing HRA and THA in treating hip arthrosis from database initiation to April 2024. Inclusion and exclusion criteria were defined, and data extraction, quality evaluation, and risk bias assessment were performed. A meta-analysis was conducted using appropriate RevManv5.4 and Stata v14.0 software. Twenty RCTs from six countries were included. HRA had a similar revision rate, function scores (WOMAC, HSS, OHS, UCLA activity score, EQ-D, EQ-5D VAS), and blood levels of cobalt and chromium compared to THA. HRA had fewer complications and less blood loss but required more operating time. HRA is a safe and effective alternative to THA, with similar revision and functional outcomes, less blood loss, and is particularly suitable for young male patients with a demand for high-level activities.

Comparative analysis of the safety and efficacy of fenestrated pedicle screw with cement and conventional pedicle screw with cement in the treatment of osteoporotic vertebral fractures: A meta-analysis.

Comparative analysis of the safety and efficacy of fenestrated pedicle screw with cement and conventional pedicle screw with cement in the treatment of osteoporotic vertebral fractures: A meta-analysis.

Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.

Concerns about the negative impacts of microplastics on human health are increasing in society, while exposure and risk assessments require high-quality, reliable data. Although quality assurance and -control (QA/QC) frameworks exist to evaluate the reliability of data for these purposes, manually assessing studies is too time-consuming and prone to inconsistencies due to semantic ambiguities and evaluator bias. The rapid growth of microplastic studies makes manually screening relevant data practically unfeasible. This study explores the potential of artificial intelligence (AI), specifically large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini, to streamline and standardize the QA/QC screening of data in microplastics research. We developed specific prompts based on previously published QA/QC criteria for the analysis of microplastics in drinking water and its sources, and used these to instruct AI tools to evaluate 73 studies published between 2011 and 2024. Our approach demonstrated the effectiveness of AI in extracting relevant information, interpreting the reliability of studies, and replicating human assessments. The findings indicate that AI-assisted assessments show promise in improving speed, consistency and applicability in QA/QC tasks, as well as in ranking studies or datasets based on their suitability for exposure and risk assessments. This groundbreaking application of LLMs in the environmental sciences suggests that AI can play a vital role in harmonizing microplastics risk assessments within regulatory frameworks and demonstrates how to meet the demands of an increasingly data-intensive application domain.

Vs {Rmerg, Rmeas, Rpim, CC1/2} for Crystal Diffraction Data Quality Evaluation.

X-ray crystal diffraction has provided atomic-level structural information on biological macromolecules. Data quality determines the reliability of structural models. In most cases, multiple data sets are available from different crystals and/or collected with different experimental settings. Reliable metrics are critical to rank and select the data set with the highest quality. Many measures have been created or modified for data quality evaluation. However, some are duplicate in functionality, and some are likely misused due to misunderstanding, which causes confusion or problems, especially at synchrotron beamlines where experiments proceed quickly. In this work, these measures are studied through both theoretical analysis and experimental data with various characteristics, which demonstrated that: 1). {Rmerg, Rmeas, Rpim, CC1/2} all measure the equivalence of reflections, and the low-shell values of these metrics can be used as reliable indicators for correctness (or trueness) of Laue symmetry; 2). High-shell is a reliable and better indicator to select resolution cutoff while the overall value measures the overall strength of the data.

MOTRPAC CPET CORE LAB - QUALITY CONTROL AND THRESHOLD DETERMINATIONS

https://youtu.be/KAfCLUuf1XY INTRODUCTION The Molecular Transducers of Physical Activity Consortium (MoTrPAC) protocol includes collection of cardiopulmonary exercise testing (CPET) data as a key clinical outcome. Due to the large volume of data and institutions involved in MoTrPAC, a CPET Core Lab was created to manage a CPET data repository, evaluate data quality, and analyze metabolic events in the continuous data like ventilatory thresholds (VT). METHODS All clinical centers involved in MoTrPAC conducted CPETs on cycle ergometers following individualized ramp protocols. The CPET Core Lab determines the VTs in all past and current CPETS: VT1 (ventilatory threshold 1) and VT2 (ventilatory threshold 2 or Respiratory Compensation Point) as data permits. VT1 is the point where VE and VCO2 increase at a greater rate than VO2. VT2 occurs when VE increases curvilinearly compensating for excess CO2 production. Two independent adjudicators will review each test and independently determine VT1 and VT2. To ensure quality control between adjudicators, repeated VT readings were performed 2 times on 20 blinded tests by 3 adjudicators. This quality control (QC) evaluation process was performed to establish between- and within-adjudicator concordance and agreement by concordance coefficient correlation (ρc) and Bland and Altman analysis, respectively. Adjudicators analyzed all CPETs without doing any additional confirmatory QC. RESULTS The metrics calculated on these data showed a high within-adjudicator concordance (ρc range 0.965 to 0.744) and agreement without significant proportional or systematic bias. Within-adjudicator coefficient of variation was below 8% (range 4.12% to 7.88%) for all adjudicators for both VT1 and VT2. The overall coefficient of variation for all adjudicators was 9.9% for VT1 and 10.7% for VT2 between adjudicators, and below 150 ml/min as empirically suggested. CONCLUSIONS VT1 and VT2 reading between-adjudicator had a good concordance and agreement. Additional QC procedures between adjudicators are planned to achieve higher levels of reliability and agreement.

Double weighted combat data quality evaluation method based on CVF optimized FAHP

During multi-agent combat simulation exercises, accurately assessing the quality of collected combat data is a critical step. Addressing the current issue of low accuracy in combat data quality evaluation, which fails to effectively support simulation exercises, this paper proposes a Double-Weighted FAHP optimized by CVF (comparative value function) method for assessing combat data quality. First, a three-tiered evaluation framework for combat data quality indicators is established, with threshold values determined for each indicator. The weights obtained from the FAHP method are optimized using the Satisfaction Consistency Approach to derive the first-tier weights. Subsequently, the CVF is constructed to obtain the second-tier weights. The double-weighted evaluation theory combines these two tiers of weights to produce the final assessment. Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. This method provides a more accurate evaluation of the quality of multi-agent combat data, offering robust data support for future combat simulation exercises and military drills.

A network meta-analysis of therapies for hyperphosphatemia in CKD based on randomized trials

To update the efficacy and safety of different drugs for the treatment of patients with hyperphosphatemia in chronic kidney disease, we conducted a network meta-analysis of 22 therapies for the treatment of uncontrolled hyperphosphatemia in patients with chronic kidney disease (CKD). All randomized controlled trials on hyperphosphatemia published from January 2013 to November 2023 were searched from CNKI, VIP database, Wanfang database, PubMed, Scopus, and Cochrane databases. Meta-analysis was used to evaluate the serum phosphorus, calcium levels, total effective rate and adverse events of patients with chronic kidney disease (CKD). Data collection and quality evaluation were carried out by three evaluators, RevMan (5.5.3) and Stata (1.3.0). A total of 71 RCTs, and 22 treatment strategies were included in this NMA. The results showed that all treatment strategies were effective in improving patients’ blood phosphorus levels. Among them, SL + CT, CA + CC, SL and TCM had higher overall efficacy, RT, TCM and SL + CT had lower blood phosphorus levels, SL + CT, SL and NAM had lower blood calcium levels, and OAC, CC, NAM and SL had higher safety. Among them, SL + CT seems to be the most recommended treatment strategy. In addition, multidrug combination strategies usually have a higher efficacy and safety profile.

Research protocol - Evaluating data quality in the Netherlands Perinatal Registry (Perined): A data comparison study using hospital records from the IUGR Risk Selection (IRIS) study.

The quality of registry based studies depends largely on the data accuracy of the registries. The Dutch Perinatal Registry (Perined) is a nationwide database comprising perinatal data digitally provided by different healthcare providers. Perined data are used for comparing outcomes across regions and healthcare institutions as well as for quality analyses and research purposes. However, little is known about the data quality of the Perined database. Therefore, this research protocol depicts our proposed study assessing the quality of Perined data compared to hospital records and case report forms (CRFs) that were part of the IUGR Risk Selection (IRIS) study. In the planned comparison study data from Perined and the IRIS Study will be used. The IRIS study was a large cluster-randomized trial investigating the effectiveness of routine third trimester ultrasonography in reducing severe adverse perinatal outcomes among Dutch low-risk pregnant women. A subsample of the IRIS study of neonates being at risk of severe adverse perinatal outcomes and their mothers will be used. Baseline demographic data were collected by midwives from participating women at inclusion (around 22 weeks' gestation) using CRFs, and in-depth neonatal and maternal clinical data were retrieved from hospital records by trained research assistants. These latter IRIS study data were linked and compared to Perined data. Completeness of Perined data will be calculated for every variable. The reliability will be assessed as the percent of agreement between Perined and hospital record data or the CRF-based data. Additionally, intra-class correlation coefficients will be calculated for continuous variables, and Kappa and 'Prevalence-and-Bias-Adjusted Kappa' will be calculated for categorical variables. The results of the planned comparison study will provide users of Perined data insight in its data quality. This will serve as an example of the accuracy of registry based data used in maternal and neonatal care research.

Psychological Intervention for Depression and Anxiety in Hemodialysis Patients: A Meta-Analysis.

With the advancement of blood purification technology, there is increasing attention to the mental health of hemodialysis patients, particularly concerning depression. This study aims to determine the effect of psychological interventions on anxiety and depression in hemodialysis patients through a meta-analysis. A computerized search was conducted to identify randomized controlled trial (RCT) studies published in PubMed, Embase, Web of Science, ScienceDirect, and Cochrane Library databases from their inception to October 2023, focusing on the effects of psychological interventions on improving depression in hemodialysis patients. Data extraction, quality evaluation, and cross-checking were performed independently by two researchers. The methodological quality of the included studies was assessed according to the criteria recommended by the Cochrane Handbook for Systematic Reviews and the meta-analysis was performed using RevMan 5.4 software (The Nordic Cochrane Centre, Copenhagen, Denmark). The effect of psychological interventions on anxiety and depression in hemodialysis patients was determined by combining effect sizes and I2 statistics. Fifteen studies were included, encompassing a total of 929 hemodialysis patients: 468 in the intervention group and 461 in the control group. The results indicated that psychological interventions could improve depressive moods [mean difference (MD) = -4.91, 95% confidence intervals (CI) (-6.56, -3.26), p < 0.001] and anxiety status [MD = -5.11, 95% CI (-6.97, -3.25), p < 0.001]. A subgroup analysis based on the intervention duration (more or less than 8 weeks) revealed that patients experienced significant improvements in depression and anxiety regardless of the intervention length. Additionally, subgroup analyses focusing on quality of life demonstrated that psychological interventions significantly improved the psychological aspects of patients' quality of life [MD = 7.31, 95% CI (1.06, 13.56), p = 0.001]. Sensitivity analysis, which excluded sources of heterogeneity, indicated that psychological interventions significantly enhanced both the psychological [odds ratios (OR) = 4.14, 95% CI (1.08, 7.20), p = 0.008] and physical [MD = 2.52, 95% CI (0.10, 4.95), p = 0.04] aspects of patients' quality of life. Psychological interventions can significantly alleviate depression and anxiety in hemodialysis patients and improve their quality of life. Psychotherapy holds promise as an effective method for improving depression in dialysis patients.

Fosaprepitant as combination therapy to prevent chemotherapy-induced vomiting in children: a meta-analysis.

To systematically evaluate the clinical efficacy and safety of fosaprepitant combined with 5-hydroxytryptamine 3 receptor antagonists (5-HT3RA) (with or without dexamethasone) on the chemotherapy-induced vomiting in pediatric cancer patients. PubMed, Embase, Cochrane Library, China Journal full-text database (CNKI), Wanfang data knowledge service platform (Wanfang) and VIP Chinese sci-tech Journal full-text database (VIP) were searched by computer (retrieval time from database establishment to Apr. 2024), randomized controlled trials (RCTs) and cohort studies about fosaprepitant and 5-HT3RA with or without dexamethasone (observation group) versus 5-HT3RA, with or without dexamethasone, as the control group for chemotherapy-induced vomiting were collected, after data extraction and quality evaluation, meta-analysis was carried out by Rev Man 5.3 software. A total of 731 patients were included in 7 trials. Meta-analysis results showed that the complete response (CCR, no vomiting/rescue medication) rates were higher in the observation group compared to that in the control group during the acute [the relative risk: RR = 1.64, 95% confidence interval: 95%CI = 1.35-1.99, P < 0.00001], delayed vomiting [RR = 2.05, 95%CI = 1.32-3.17, P = 0.001] and overall phases [RR = 2.08, 95%CI = 1.69-2.57, P < 0.00001], with statistical significance (P < 0.05). The subgroup analysis of salvage treatment proportion revealed that the need for rescue medication was higher for patients in the control than fosaprepitan regimens [RR = 0.20, 95%CI = 0.08-0.54, P = 0.001] There was no difference in the incidence of adverse drug reaction between two groups [RR = 0.95, 95%CI = 0.75-1.19, P = 0.66]. Fosaprepitant in combination with 5-HT3RA (with or without dexamethasone) has the same safety and more effective in preventing chemotherapy-induced vomiting than 5-HT3RA with or without dexamethasone.

ATM is associated with the prognosis of colorectal cancer: a systematic review.

Chemosensitivity and radiosensitivity are associated with the prognosis of colorectal cancer, and the expression of the ataxia-telangiectasia mutated (ATM) protein plays an essential role in these processes. The present study examined the relationship between ATM expression and the survival outcomes of colorectal cancer patients and explored the underlying mechanism and promising therapeutic strategies. A search including medical subject headings (MeSH), free terms, and combined words was conducted using Pubmed, EMBASE, and Cochrane. Studies had to meet the inclusion criteria as well as include processes such as data extraction and quality evaluation. The survival outcomes were assessed using hazard ratio (HR) and 95% confidence interval (CI). Heterogeneity, and publication bias were analyzed, and a P value <0.05 was considered statistically significant. Nine studies with 2883 patients were included in the meta-analysis. Low ATM expression level was related to poor overall survival (HR=0.542, 95% CI=0.447-0.637; P=0.000). Disease-free, progression-free, and recurrence-free survival rates were lower in patients with low ATM expression than in those with high ATM expression. There was no significant difference between Stage I-II and Stage III-IV colorectal cancer patients [risk ratio (RR)=1.173, 95% CI=0.970-1.417, P=0.690]. Low ATM expression level may be a marker of poor survival in colorectal cancer and contributes to resistance to therapy. Targeting related factors in these pathways to sensitize tumors to treatment is a potential therapeutic strategy, and monitoring ATM status could be a valuable guide independent of the immunotherapy or chemotherapy strategy used.

Effectiveness of midwife/nurse-led non-invasive uterine massage in preventing postpartum haemorrhage: a quantitative systematic review protocol

IntroductionPostpartum haemorrhage (PPH) negatively impacts women’s health. Preventing and managing PPH is important, and nurse-led interventions are needed. However, no systematic review has evaluated the effectiveness of non-invasive uterine massage...

Logistics Management Information System (LMIS) for Health Commodities at Public Health Facilities in Amhara National Regional State of Ethiopia: A Data Quality Evaluation Survey.

Access to essential medicines is limited in developing countries mainly due to inefficiencies in health supply chain management, such as the absence of standard monitoring frameworks and poorly designed Logistics Management Information Systems (LMIS). Health supply chain managers need accurate and timely data for decision-making. However, routine health information systems suffer from poor data quality, reliance on paper-based reports, insufficient logistic formats, inadequate infrastructure, and limited human resources. This study evaluates the data quality of LMIS for health commodities in public health facilities in the Amhara National Regional State of Ethiopia. The study was conducted in Ethiopia's Amhara National Regional State. The study employed an institution-based concurrent mixed-methods design. Data collection involved 102 facilities selected through multi-stage stratified random sampling, adhering to sampling criteria set by USAID's Logistics Indicators Assessment Tool (LIAT). Data abstraction checklists were used to collect data. Of the seven tracer medicines selected to evaluate data quality, there was substantial variability in inventory accuracy rates. Inventory discrepancies were significant, highlighting potential issues with manual and digital record-keeping systems, with overall mean physical and electronic inventory accuracy rates of 74.7% and 70.6%, respectively. Additionally, the Report and Requisition Form (RRF) showed trends of timely submission, with the overall mean percentage completeness for the seven tracer medicines at 90.2%. However, the data quality experienced fluctuations, with the overall average percentage of legality (authorization of LMIS reports) and the accuracy of the RRF at 77.2% and 76%, respectively. The evaluation of data quality revealed significant discrepancies in physical and electronic records, with notable fluctuations in completeness, legality, legibility, and accuracy within the health LMIS. To rectify these issues, robust data quality verification processes, clear guidelines, targeted interventions, strengthened monitoring systems, regular audits, and comprehensive training for health supply chain staff are needed.

Operational Capability of Drone‐Based Meteorological Profiling in an Urban Area

AbstractDuring the Uncrewed Aircraft Systems Demonstration Campaign (UAS‐DC), led by the World Meteorological Organization (WMO), twice‐daily meteorological profiling was conducted for 2 months at the Meteorological Research Institute in Tsukuba City (Japan), which is identified as a densely inhabited district. This campaign was instigated to assess the feasibility of obtaining continuous daily measurements for the long term (over a period of more than 1 month), to distribute the data in a format designated for numerical weather prediction, and to evaluate data quality compared to conventional meteorological data. Three types of uncrewed aircraft systems were utilized, that is, a meteorological medium‐sized hexacopter and medium‐ and small‐sized commercial drones with meteorological sensors attached. The maximum flight height was limited to 900 m above ground level owing to airspace regulations around the observation site. Compared with routine radiosonde data, the bias of air temperature, relative humidity, and wind speed measurements was less than 0.3 K, 1.5%, and 0.6 m , respectively, thereby meeting WMO requirements. Moreover, data transfer to the WMO‐prepared repository was completed within 30 min after measurement acquisition. Based on user experience, several aspects regarding the UAS‐DC campaign were discussed from the perspective of sustainable operation and atmospheric boundary layer research.

Ecological Momentary Assessment of Mental Health Problems Among University Students: Data Quality Evaluation Study.

The use of ecological momentary assessment (EMA) designs has been on the rise in mental health epidemiology. However, there is a lack of knowledge of the determinants of participation in and compliance with EMA studies, reliability of measures, and underreporting of methodological details and data quality indicators. This study aims to evaluate the quality of EMA data in a large sample of university students by estimating participation rate and mean compliance, identifying predictors of individual-level participation and compliance, evaluating between- and within-person reliability of measures of negative and positive affect, and identifying potential careless responding. A total of 1259 university students were invited to participate in a 15-day EMA study on mental health problems. Logistic and Poisson regressions were used to investigate the associations between sociodemographic factors, lifetime adverse experiences, stressful events in the previous 12 months, and mental disorder screens and EMA participation and compliance. Multilevel reliability and intraclass correlation coefficients were obtained for positive and negative affect measures. Careless responders were identified based on low compliance or individual reliability coefficients. Of those invited, 62.1% (782/1259) participated in the EMA study, with a mean compliance of 76.9% (SD 27.7%). Participation was higher among female individuals (odds ratio [OR] 1.41, 95% CI 1.06-1.87) and lower among those aged ≥30 years (OR 0.20, 95% CI 0.08-0.43 vs those aged 18-21 years) and those who had experienced the death of a friend or family member in the previous 12 months (OR 0.73, 95% CI 0.57-0.94) or had a suicide attempt in the previous 12 months (OR 0.26, 95% CI 0.10-0.64). Compliance was particularly low among those exposed to sexual abuse before the age of 18 years (exponential of β=0.87) or to sexual assault or rape in the previous year (exponential of β=0.80) and among those with 12-month positive alcohol use disorder screens (exponential of β=0.89). Between-person reliability of negative and positive affect was strong (RkRn>0.97), whereas within-person reliability was fair to moderate (Rcn>0.43). Of all answered assessments, 0.86% (291/33,626) were flagged as careless responses because the response time per item was <1 second or the participants gave the same response to all items. Of the participants, 17.5% (137/782) could be considered careless responders due to low compliance (<25/56, 45%) or very low to null individual reliability (raw Cronbach α<0.11) for either negative or positive affect. Data quality assessments should be carried out in EMA studies in a standardized manner to provide robust conclusions to advance the field. Future EMA research should implement strategies to mitigate nonresponse bias as well as conduct sensitivity analyses to assess possible exclusion of careless responders.

Role of Statistics in Artificial Intelligence Technology

Artificial intelligence (AI) research and applications have sparked a broad scientific, economic, social, and political debate. Statistical approaches and techniques are fundamental to AI because they allow robots to learn from data and make intelligent decisions. It's possible even to view statistics as a fundamental component of AI. Statistics is an ideal partner for other disciplines in teaching, research, and practice because of its specialized knowledge of data evaluation, which begins with the correct phrasing of the research question and continues through a study design stage to analysis and interpretation of the results. This work aims to demonstrate how statistical methodology is relevant to the development of AI. In terms of methodological development, planning, research design, evaluation of data quality and collection, distinction of causation and relationships, and evaluation of result uncertainty, we talk about the contributions of statistics to the field of artificial intelligence. This study thoroughly analyzes the crucial role statistics play in AI, demonstrating the numerous applications of statistical ideas like probability, regression, classification, and clustering. The essay highlights the value of statistical analysis in handling uncertainty, making predictions, and training AI models all of which improve the efficiency and precision of AI systems.

Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data

This paper focuses on enhancing the resilience of microgrids—localized power systems that integrate multiple energy sources—against challenges such as natural disasters, technological obstacles, and human errors. It begins by defining the specific connotation of microgrid resilience and then proposes an innovative solution centered on the use of advanced sensor technology to continuously monitor the microgrid and its operational environment, ensuring accurate and timely data collection under dynamic conditions. Subsequently, a decision risk assessment framework is constructed, integrating data quality evaluation and operational risk considerations, to drive strategy optimization through in-depth data analysis. At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. Both decision processes skillfully utilize Monte Carlo simulation and multi-objective genetic algorithms to effectively manage the uncertainty risks in the decision-making process, thereby significantly enhancing the overall resilience of the microgrid.