Consistent Evaluation Method Research Articles

Rank Aggregation Methods and Tools in Genomic Data Analysis

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. These encompass a broad range of approaches, from distributional and heuristic methods to Bayesian and stochastic optimization algorithms. By examining these techniques, we aim to equip researchers with the background knowledge needed to navigate the intricacies of RA in genomics data integration effectively. We review the practical applications to highlight the relevance and impact of RA methods in advancing genomics research. As the field continues to evolve, we identify open problems and suggest future directions to enhance the effectiveness of rank aggregation in genomics, by addressing the challenges related to data heterogeneity, single-cell omics and spatial transcriptomics data, and the development of clear and consistent evaluation methods. In summary, RA stands as a powerful tool in genomics research, which can offer deeper insights and more comprehensive data integration solutions.

Validation of an Instrument for Detecting Problematic Internet Use in Adolescents.

Problematic internet use (PIU) has drawn attention due to its potentially negative consequences on individuals' social and personal lives. At present, a consensus on diagnostic criteria for problematic internet use remains elusive, leaving uncertainty regarding its classification as a distinct mental disorder. Extensive research efforts are underway to investigate its underlying causes, risk factors, and correlated adverse consequences. Nonetheless, research on problematic internet use (PIU) frequently faces challenges due to the absence of consistent and dependable evaluation methods, with many existing assessment tools lacking a solid theoretical basis. This study introduces a novel instrument that incorporates positive emotional regulation along with negative emotional regulation, compulsive use, and cognitive preoccupation, all crucial aspects of PIU. The study involved 3054 adolescents from Madrid, Spain, and employed exploratory and confirmatory factor analyses to validate the instrument's structure. The resulting four-factor model includes Positive Emotional Regulation, Negative Emotional Regulation, Compulsive Use, and Cognitive Preoccupation. The instrument demonstrates good internal consistency and an association with risk factors, as evidenced by correlations with time spent on various internet-related activities. This comprehensive tool enhances our understanding of PIU and its underlying cognitive and emotional processes and provides a valuable resource for assessing and addressing problematic internet use in adolescents.

Psychological distress, sexual satisfaction and quality of life of gynaecological cancer patients and their spouses during cancer survivorship: A comparison of husbands and wives.

To investigate the psychological distress, sexual satisfaction, and quality of life of gynaecological cancer survivors and their spouses during cancer survivorship. The survival rate of patients with cancer is increasing owing to advances in medical treatment technology. Spouses are the closest companions of gynaecological cancer survivors. Patients with gynaecological cancer and their spouses face different situations and challenges after experiencing cancer invasion. Questionnaire-based cross-sectional study. Convenience sampling was employed, and 180 participants, including patients with gynaecological cancer and their spouses, were enrolled. A structured questionnaire was used to investigate the psychological distress, sexual satisfaction, and quality of life of gynaecological cancer survivors and their spouses during acute, extended, and permanent survivorship. The STROBE checklist guided the study preparation. For gynaecological cancer survivors and their spouses, (1) severe psychological distress was present during acute survivorship, with anxiety extending until permanent survivorship; (2) no significant differences were observed in pre- and post-treatment sexual satisfaction, although pre-treatment sexual satisfaction was higher than post-treatment sexual satisfaction in all three cancer survivorship stages and (3) quality of life decreased during acute survivorship and gradually improved with time. Psychological distress, sexual satisfaction and quality of life of gynaecological cancer survivors and their spouses worsened during acute survivorship and improved over time until permanent survivorship. Gynaecological cancer survivors and their spouses experience anxiety and depression from diagnosis confirmation until permanent survivorship (>5 years survival). Therefore, clinical nurses' sensitivity to emotional distress in cancer survivors and their spouses can be improved and a consistent and routine evaluation method has been established for the early detection of such emotional distress. The results of this study can provide a reference for clinical healthcare professionals and contribute to a better quality of care.

Parsonage-Turner Syndrome and SARS-CoV-2 Infection: A Literature Review With Case Presentation.

Neuralgic amyotrophy, also known as Parsonage-Turner syndrome (PTS), is characterized by severe pain and muscle wasting affecting the anterior body, including the head, shoulder, upper limb, and chest wall. Often triggered by an antecedent event, such as infection, PTS encompasses various conditions historically identified as separate entities. In 1948, Parsonage and Turner unified these conditions under the term neuralgic amyotrophy based on shared features of intense pain and muscular atrophy. Recent studies have highlighted PTS as a spectrum disorder with diverse manifestations, including pure sensory neuropathy, extensive neuropathy, spinal accessory nerve involvement, and diaphragmatic palsy. We reviewed 26 documented cases of PTS following SARS-CoV-2 infection, emphasizing the importance of considering PTS in individuals with a history of COVID-19 due to varied clinical presentations. Standardized diagnostic methods and comprehensive evaluations are crucial for accurate diagnosis and management. Future research should focus on consistent evaluation methods and employing a comprehensive differential diagnosis approach.

Consistency evaluation of multidimensional quality attributes of Atractylodis Macrocephalae Rhizoma by fusion of multi-source information

The quality control of Chinese medicinal decoction pieces is one of the key tasks in the traditional Chinese medicine industry. In this study, multi-source information fusion was employed to fuse the data from near-infrared spectroscopy, electronic tongues, and other tests and establish an overall quality consistency evaluation method for Atractylodis Macrocephalae Rhizoma, which provided methodological support for the overall quality evaluation of Atractylodis Macrocephalae Rhizoma. The near-infrared spectroscopy information was measured in both static and dynamic states for 23 batches of Atractylodis Macrocephalae Rhizoma samples from different sources, and the electronic tongue sensory information, moisture content, and leachate content were measured. The overall quality of Atractylodis Macrocephalae Rhizoma was evaluated by multi-source information fusion. The results showed that the near-infrared spectroscopy information of 16122103, 801000509, 801000352, 701003656, HX21L01, and 160956 was different from that of other batches of Atractylodis Macrocephalae Rhizoma powder in the static state, and 701003298, 16122103, 701003656, 701003107, 801000229, and 18090404 were the different batches in the dynamic state. The moisture content showed no significant difference between batches. The leachate content in the batch 801000509 was different from that in other batches. The electronic tongue sensory information of 150721004, 151237, 160703004, HX21M01, HX21K04, HX21K01, and 601003516 was different from that of other batches. Furthermore, data layer fusion was employed to analyze the overall quality of Atractylodis Macrocephalae Rhizoma. Four batches, 150721004, HX21M01, HX21K04, and HX21K01, showed the parameters exceeding the 95% control limits and differed from the other samples in terms of the overall quality. This study integrated the information of moisture, near-infrared spectroscopy, and other sources to evaluate the quality consistency among 23 batches of Atractylodis Macrocephalae Rhizoma samples, which provides a reference for the quality consistency evaluation of Chinese medicinal decoction pieces.

Chinese expert consensus on clinical diagnosis and treatment of non-neurogenic lower urinary tract dysfunction in children

Non-neurogenic lower urinary tract dysfunction (NNLUTD) is a common clinical problem in children, which is an important cause of lower urinary tract symptoms, urinary tract infection and vesicoureteral reflux in children, often accompanied by behavioral and psychological problems. At present, there is no consistent evaluation method and treatment for NNLUTD in children. Experts of Pediatric Urodynamics and Pelvic Floor Group of Pediatric Surgery Branch of Chinese Medical Association formulated this consensus according to the NNLUTD related diagnosis and treatment guidelines designated by International Children's Continence Society (ICCS) and International Continence Society (ICS), and combined with the latest relevant literatures at home and abroad, to provide reference for the clinical standard diagnosis and treatment of NNLUTD in children.

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Evaluation method for consistency of lithium-ion battery packs in electric vehicles based on the Mahalanobis-Taguchi system

The consistency of battery packs is vital for safety and reliability during electric vehicle (EV) operations. Many consistency evaluation methods based on laboratory conditions are time-consuming and difficult to implement in the real-world. This study proposes an evaluation method for the consistency of lithium-ion battery packs in EVs based on the Mahalanobis-Taguchi system (MTS). First, a Douglas-Peucker (DP) algorithm was developed to compress high-dimensional cell voltage data, which reduced the feature extraction time by 81.64 %. Next, the consistency features were extracted based on sample entropy and fast-dynamic time warping (Fast-DTW) to quantify inconsistencies in battery parameters. Finally, the consistency evaluation method of the two-level warning based on MTS was established, and the hierarchical inconsistency warning threshold was identified based on the Chebyshev theorem. The results showed that the inconsistencies in battery packs were identified and quantitatively evaluated, and the occurrences of inconsistencies are occasionally discontinuous. The inconsistencies in battery packs were detected at high state of charge (SOC) levels at the end of charging. This method can evaluate the consistency of battery packs online based on EV operation data to monitor battery safety and provide detailed information for maintenance.

Advantage analysis of flow-through cell method in quality evaluation of Chinese patent medicine: a case study of Danshen Tablets

To explore the quality consistency evaluation method for multi-component traditional Chinese medicine and establish a dissolution evaluation method suitable for the characteristics of multi-component Chinese patent medicine, this study discussed the characteristics and advantages of the flow-through cell method in the dissolution evaluation of Chinese patent medicine by comparing the impact of the small cup method and the flow-through cell method on the dissolution behavior of water-soluble and lipid-soluble major active components of Danshen Tablets. Dissolution tests were performed using the small cup method as described in the 2020 edition of the Chinese Pharmacopoeia and the newly introduced flow-through cell method(closed-loop method) with water solution containing 0.5% SDS as dissolution medium. Cumulative dissolution curves of the water-soluble component salvianolic acid B and the lipid-soluble component tanshinone Ⅱ_A in Danshen Tablets were plotted, and fitting and similarity analysis of the dissolution models was conducted to identify the characteristics and advantages of the flow-through cell method. For the small cup method, 150 mL of water containing 0.5% SDS was used as the dissolution medium, with a rotation speed of 75 r·min~(-1) and a temperature of(37±0.5) ℃, and 3 mL of samples were taken at 15, 30 min, 1, 2, and 4 h, with fresh dissolution medium added at the same temperature and volume. For the flow-through cell method, a closed-loop system was used. Danshen Tablets were placed in the flow-through cell with approximately 6.7 g of glass beads, and 150 mL of water containing 0.5% SDS was used as the dissolution medium. The flow rate was set at 20 mL·min~(-1), and the temperature and sampling were the same as the small cup method. The results showed that compared with the small cup method, the flow-through cell method had stronger discriminative power and higher sensitivity in distinguishing the dissolution behavior of the two components, and could better reflect the differences in formulation quality, especially for water-insoluble lipid-soluble components. Given that there were no essential differences in the in vitro release kinetics between the two methods, the flow-through cell method could not only replace the traditional small cup method but also better guide the formulation development and identify quality issues of formulations.

Improving consistency classification: An innovative benchmark‐based approach for the AHP

AbstractThe analytic hierarchy process (AHP) is a cornerstone of multi‐criteria decision analysis, enabling well‐informed choices across diverse contexts. This paper introduces an original benchmark‐based framework designed to enhance the precision of consistency classification for pairwise comparison matrices (PCMs) within the AHP methodology. This innovative approach quantifies the discrepancy between a given PCM and its benchmark matrix, comprising comparison ratios that faithfully reflect the relative preferences encapsulated within principal eigenvector values, thereby capturing the true degree of coherence. To ensure benchmark alignment with human perception, elements of the benchmark PCM are further rounded to the nearest values on the Fundamental Scale. The potency of our framework derives from two pivotal factors: the inherent Priority Preference Range within the principal eigenvector and the order of the PCM. Statistical thresholds for consistency are established using a technique based on simulated, logical PCMs, proposed by Bose [2022]. This rigorous method ensures an unbiased, objective and pragmatic evaluation of consistency, eliminating the subjectivity inherent in arbitrary thresholds based on random PCMs. Our approach rectifies the inconsistencies in the conventional CR method that yields false positives for PCMs of orders 3 and 4, and false negatives for higher orders. By harnessing customized benchmarks and eschewing random matrices, our framework systematically confronts the inherent consistency challenges within AHP, thus enhancing its decision‐making capability. The practical utility of our approach is aptly demonstrated through AHPtools, an R‐based library package designed to showcase our novel consistency evaluation method. The demonstration of the package in Appendix B will facilitate readers to easily apply our methodology to real‐world PCM classification scenarios within the AHP. In conclusion, our benchmark‐based framework heralds a transformative era in consistency classification within the AHP, empowering real‐world multi‐criteria decision‐making with unprecedented precision and reliability, and ushering in a new paradigm of informed and astute outcomes.

Evaluating the quality consistency of antiviral oral liquid by high-performance liquid chromatography five-wavelength matched average fusion fingerprint combined with electrochemical fingerprint and ultraviolet spectral quantum fingerprint

The antiviral oral liquid (AOL) was an antiviral drug currently in clinical trials against coronavirus disease 2019. This study aimed to improve its quality consistency evaluation method using fingerprint techniques from several aspects. First, the five-wavelength matched average fusion fingerprint (FMAFFP) for HPLC, electrochemical fingerprint (ECFP), and ultraviolet spectral quantum fingerprint (UVFP) was established for 22 samples, respectively. Their quality was then assessed using the average linear quantitative fingerprint method, and 22 samples were classified into eight quality grades. OPLS and PCA were then used further to explore the characteristic parameters of these three fingerprints. Five compounds were quantified simultaneously for the first time, and then the relationship between the average linear quantitative similarity (PL) and the sum of the five quantitative components (P5c) was investigated. A linear correlation (r ≥ 0.9735) between PL and P5c suggested that PL may be used to predict chemical content. Finally, to investigate the antioxidant potential of the AOL, correlation analyses were performed for FMAFFP peaks-PEC and UVFP peaks-PEC, respectively, where the PEC value was defined as the quantitative similarity of ECFP. The Pearson correlation coefficient and gray correlation analysis were consistent, allowing us to initially explore the antioxidant capacity of the unidentified components of the samples. This study researched AOL using multidimensional fingerprints to provide a comprehensive and reliable method for quality consistency control of herbal compound preparations.

A novel biological sources consistency evaluation method reveals high level of biodiversity within wild natural medicine: A case study of Amynthas earthworms as "Guang Dilong".

For wild natural medicine, unanticipated biodiversity as species or varieties with similar morphological characteristics and sympatric distribution may co-exist in a single batch of medical materials, which affects the efficacy and safety of clinical medication. DNA barcoding as an effective species identification tool is limited by its low sample throughput nature. In this study, combining DNA mini-barcode, DNA metabarcoding and species delimitation method, a novel biological sources consistency evaluation strategy was proposed, and high level of interspecific and intraspecific variations were observed and validated among 5376 Amynthas samples from 19 sampling points regarded as "Guang Dilong" and 25 batches of proprietary Chinese medicines. Besides Amynthas aspergillum as the authentic source, 8 other Molecular Operational Taxonomic Units (MOTUs) were elucidated. Significantly, even the subgroups within A. aspergillum revealed here differ significantly on chemical compositions and biological activity. Fortunately, this biodiversity could be controlled when the collection was limited to designated areas, as proved by 2796 "decoction pieces" samples. This batch biological identification method should be introduced as a novel concept regarding natural medicine quality control, and to offer guidelines for in-situ conservation and breeding bases construction of wild natural medicine.

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.

Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

DeepErythema: A Study on the Consistent Evaluation Method of UV SPF Index Through Deep Learning

This study presents a deep learning-based approach for consistent Sun Protection Factor (SPF) grading by quantifying skin erythema. Despite strict international standards, the current human-based Minimum Erythema Dose (MED) determination method for SPF evaluation suffers from inconsistency due to subjective criteria and the visual characteristics of erythema’s large variance. We propose DeepErythema, a novel method that uses erythema quantification for MED determination. The proposed method comprises pre-processing methods, a deep learning segmentation model, and post-processing methods. The UV irradiation area pointing is a pre-processing method that accurately detects the inspection area as a UV irradiated port, while Median Gradation Reduction eliminates the gradation that arises due to the digital image collection environment. The deep learning segmentation model parts introduce a methodology for improving performance using various feature extractors and methodologies like SeLu, Reverse Attention Gate, and MixedLoss. For post-processing, Perspective Transform Rectangle Restoration restores a distorted inspection area, and Relative Density Evaluation calculates a density score to consider skin characteristics and tone. We verified that utilizing the DeepErythema score in the UV SPF MED Evaluation (USME) dataset reduced the distribution of human-based MED decisions from 36 to 9 in the experimental results. This reduction in MED decision scope leads to improved consistency in SPF Index grading. Overall, the study contributes to the development of an objective and consistent evaluation method for SPF grading.

A Consistency Evaluation Method of Pavement Performance Based on K-Means Clustering and Cumulative Distribution

This paper proposes a cumulative distribution modelling method for pavement performance indexes based on the sampling theorem and implements clustering analysis of similar road sections through the K-means algorithm. The results show that: (1) The modelling method proposed in this paper can convert discrete pavement performance data into a continuous function of pavement performance indexes and a continuous function of pavement performance cumulative distribution and achieve the acquisition of a large amount of pavement performance data. (2) Based on the cumulative distribution and K-means clustering, it is possible to understand the overall pavement performance status of the network and identify road sections with similar decay models and poor decay status for focused attention, which constructed the pavement performance evaluation system of the three-level system of road network–road section–unit road section.

Serious games for health promotion in adolescents – a systematic scoping review

Digital gaming has broad appeal globally, with a reported 2.7 billion gamers worldwide. There is significant interest in using games to enhance learning, with ‘serious games’ being included in classrooms to engage adolescents’ learning across a range of domains. A systematic scoping review of serious games used for health promotion with adolescents was conducted to identify serious games, review the methods used to evaluate these games, and outline evidence available to support the efficacy of these games in improving knowledge, beliefs/attitudes and behaviours in the target groups. Player engagement/enjoyment was reported if assessed. A total of 21 studies were found to have met the inclusion criteria domains: ‘healthy lifestyle’ ‘sexual health’ and ‘substance use’. A heterogenous approach across studies to game design and development, duration of game play, use of a control group and measurement of outcome(s) was observed. Game efficacy was difficult to assess due to broad generalisations and lack of consistent evaluation methods. Several studies demonstrate serious games can be engaging and pedagogically effective as a learning device and behaviour-change agent. Several studies, however, had less rigorous evaluation and lacked longer-term follow up. The ability for developers to demonstrate positive short- and long-term impacts of serious games with high-quality evidence is essential to the ongoing acceptance and use of these serious games as part of the school curriculum.

A consistency evaluation method for digital twin models

Digital twin has shown its great application values in various fields in the past few years, especially manufacturing. During the process of digital twin implementation, the digital twin model is one of the most important foundations. Because the effectiveness and accuracy of digital twin models could directly affect the quality of related services. Nowadays, some efforts have been made on digital twin modeling methods, e.g. multi-disciplinary modeling method, multi-dimension modeling method, and multi-scale modeling method. Besides, the research on Industrial Internet of Things makes it possible to realize the synchronization between digital twin models and the corresponding physical objects. However, the attention on how to conduct a comprehensive consistency evaluation between the models and corresponding physical objects is not enough. Against the background, this paper proposed a consistency evaluation framework towards digital twin shop-floor (DTS) models. The consistency evaluation method mainly contains two phases, i.e. before and after model assembly and model fusion. Before model assembly and model fusion, the geometry models, physics models, behavior models, and rule models are mainly considered. After model assembly and fusion, the overall performance of assembled models and fusion models is paid more attention. Furthermore, the analytic hierarchy process(AHP) is used to form a comprehensive consistency evaluation result for DTS models. At last, a specific application scenario in complex product AIT (Assembly, Integration and Test) shop-floor is given to explain how to apply the proposed framework and method.

Consistency re‐evaluation in analytic hierarchy process based on simulated consistent matrices

AbstractA new approach to re‐evaluating consistency in the analytic hierarchy process (AHP) using simulated consistent matrices is presented. The proposed consistency evaluation method makes use of statistically significant deviations from the average consistency measure for the simulated matrices. This addresses most of the deficiencies of the conventional consistency ratio (CR) method. A pairwise comparison matrix (PCM) is adjudged inconsistent by the proposed method if its consistency measure exceeds the modeled consistency threshold. Comparison of the consistency evaluation for simulated nearly‐consistent matrices using the proposed method shows a statistically significant reduction of the order‐specific bias in comparison with the CR method. The proportion of nearly consistent matrices which are evaluated as ‘inconsistent’ increases more than three‐folds when the evaluation is done using the CR method. Several examples are presented which illustrate the advantages of the proposed method and differences in classification with the CR approach. Evaluation of consistency using the proposed method of statistically derived thresholds from simulated, nearly consistent matrices is more nuanced and objective, as well as intuitive in its interpretability.

Methods for geothermal resource assessment of hot dry rock: A case study in the Gonghe Basin, China

Hot dry rock (HDR) geothermal resources are renewable energy source. Many of the findings of HDR resource evaluations have been used in energy planning and EGS design. However, to assess the amount of HDR resources in different locations, a consistent classification scheme and evaluation methods are still lacking. Considering geological credibility and economic feasibility, HDR resources are separated into three categories: vision, reserve, and exploitable. Vision and reserve are stationary resources that can be evaluated using the volume technique, and the exploitable resources can be evaluated using the numerical simulation approach. The HDR vision resource of the Gonghe Basin is evaluated to be 4.076 × 1022 J, and the reserve resource of the Qiabuqia HDR mass is evaluated to be 2.11 × 1020 J. At the Qiabuqia HDR development site, a discrete fracture network (DFN) model is applied for numerical simulation computations, which is based on the notion of local thermal nonequilibrium. The K1 and K2 wells produce varying amounts of heat due to the heterogeneous features of the fractured medium model, which is primarily due to differences in fracture density, heat exchange area, and fluid migration pattern. The categorization system and assessment technique can be used as a guide for evaluating HDR resources in the future.

Evaluation of Lithium-Ion Battery Pack Capacity Consistency Using One-Dimensional Magnetic Field Scanning

The capacity inconsistency among commercial lithium-ion battery packs is an important factor affecting their service life. However, there is still a lack of detection methods to accurately test the capacity consistency of lithium-ion battery packs at cell level. To solve this problem, a non-destructive testing method for capacity consistency of lithium-ion battery pack based on 1-D magnetic field scanning is proposed in this article. First, a magnetic field simulation model and measurement setup of lithium-ion battery are developed to study the principle of detection technology. On such basis, a capacity consistency evaluation method of lithium-ion battery packs is proposed using magnetic field feature extraction and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NNs), and the effectiveness of the method is verified by experimental testing. Finally, measuring factors affecting magnetic field distribution features and practicality of the method are discussed using multi-cells’ magnetic field simulation models. The results show that the proposed method can accurately diagnose the capacity consistency of the tested battery pack, which provides a basis for battery pack performance testing and maintenance.