Expert Observers Research Articles

A Cold Shower for the Hot Hand Fallacy: Robust Evidence from Controlled Settings

Abstract The canonical hot hand fallacy result was recently reversed, based largely on a single statistic, and a data set that was underpowered for individual-level testing. Here we perform a more robust analysis, testing whether hot hand performance exists across (i) data sets: four different controlled shooting experiments, (ii) time: multiple sessions per individual spread across a six month gap, and (iii) various (improved) approaches to statistical testing. We find strong evidence of hot hand performance, both across data sets and within individuals across time. Moreover, in a study of beliefs, we find that expert observers can successfully predict which shooters get the hottest.

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making

Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology.

Overload Alarm Prediction in Power Distribution Transformers

The growing demand for electricity puts more strain on the grid, requiring automated and proactive strategies such as overload prediction to improve grid maintenance. However, the intermittent nature of power distribution loads makes the prediction more challenging. This paper proposes a novel framework for overload alarm prediction in distribution transformers, aimed at enhancing the reliability and efficiency of grid operations. Leveraging real-world smart meter data and machine learning techniques, the proposed system develops a classification model to predict overloads for distribution transformers. Due to resource constraints, a new strategy is adopted to assess the significance of alarms based on expert observations. Subsequently, a new approach is developed to imitate the experts, leading to an automated decision-making process using random forest. Ultimately, the transfer learning strategy is utilized to predict overload alarms for distribution transformers facing data scarcity in real-world applications. The proposed system demonstrates high accuracy of overload alarm predictions, paving the way for developing more proactive grid maintenance strategies.

A novel approach for the replacement of accuracy and precision measurements with the visual instrument agreement scale (VIAS) in evaluating the performance of four intraoral scanners and one shade measurement device

ObjectivesThe terms 'accuracy' and 'precision' are tightly defined in color science but are often used ambiguously in dental research. This study introduces the visual instrument agreement scale (VIAS), a new method for determining visual-instrumental agreement in dental colorimetry by comparing visually perceived and measured color differences. Materials and MethodsIn-vivo tooth color measurements were taken from 16 participants using four intraoral scanners (Primescan, Medit i700, Carestream CS3700, Trios 3) and one spectrophotometer (Vita Easyshade V). Visual shade assessment was also performed by one expert observer using the 3D Master shade guide. Statistical significance testing was conducted using the STRESS index to calculate VIAS, and the F-statistic was used to evaluate device performance. ResultsCarestream CS3700 achieved the highest visual-instrumental agreement with a VIAS score of 82%, performing significantly better than the other devices. Primescan, Medit i700, and Trios 3 showed scores of 76%, 75%, and 72%, respectively, with no significant differences between them. Vita Easyshade V scored 57%, performing significantly worse than the other devices. ConclusionsThe overall performance of the intraoral scanners was strong, with Carestream CS3700 approaching excellent performance. The VIAS method offers a practical, color science-based framework for evaluating visual-instrumental agreement and can be easily replicated using the freely available toolbox. Clinical SignificanceIntraoral scanners performed surprisingly better than a spectrophotometer specifically designed for tooth color measurement and which is often regarded as the gold standard. Additionally, VIAS offers a new, scientifically grounded approach for testing visual-instrumental agreement in dental colorimetry.

Deep Learning-Based Microscopic Damage Assessment of Fiber-Reinforced Polymer Composites.

Fiber-reinforced polymers (FRPs) are increasingly being used as substitutes for traditional metallic materials across various industries due to their exceptional strength-to-weight ratio. However, their orthotropic properties make them prone to multiple forms of damage, posing significant challenges in their design and application. During the design process, FRPs are subjected to various loading conditions to study their microscopic damage behavior, typically assessed through scanning electron microscopy (SEM). While SEM provides detailed insights into fracture surfaces, the manual analysis of these images is labor-intensive, time-consuming, and subject to variability based on the observer's expertise. To address these limitations, this research proposes a deep learning-based approach for the autonomous microscopic damage assessment of FRPs. Several computationally efficient pre-trained deep learning models, such as DenseNet121, NasNet Mobile, EfficientNet, and MobileNet, were evaluated for their performance in identifying different damage modes autonomously, thus reducing the need for manual interpretation. SEM images of FRPs with five distinct failure modes were used to validate the proposed method. These failure modes include three fiber-based failures such as fiber breakage, fiber pullout, and mixed-mode failure, and two matrix-based failures such as matrix brittle failure and matrix ductile failure. The entire dataset is divided into train, validation, and test sets. Deep learning models were established by training on train and validation sets for five failure modes, while the test set was used as the unseen data to validate the models. The models were assessed using various evaluation metrics on an unseen test dataset. Results indicate that the EfficientNet model achieved the highest accuracy of 97.75% in classifying the failure modes. The findings demonstrate the effectiveness of employing deep learning techniques for microscopic damage assessment, offering a more efficient, consistent, and scalable solution compared to traditional manual analysis.

Automated coordinate system estimation: A preliminary step toward computer-assisted radial head arthroplasty planning.

The success of radial head arthroplasty (RHA) relies on the design of the implant and precision of the surgical technique, with preoperative planning potentially playing a crucial role. The accurate establishment of a patient-specific anatomical coordinate system (ACS) is essential for this planning process. This study tested the hypothesis that an innovative automated method would be an accurate, reliable, and efficient framework to determine the ACS of the proximal radius, which would be a step toward improving the precision of RHA planning. We used advanced computational techniques to analyze 50 forearm CT scans, comparing the accuracy, reproducibility, reliability, and efficiency of the automated method with manually derived ACS using expert observers as benchmarks. The results showed that the automated approach was more accurate in identifying anatomical landmarks, with smaller mean distance discrepancies (0.6 mm) than manual observers (1 mm). Its reproducibility was also superior, with narrower reproducibility limits, particularly for ulnar notch landmarks (0.6 to 0.8 mm compared to manual selection 1.2 to 1.4 mm) (p = .01). In addition, the limits of agreement and the mean absolute rotational and translational differences of the axes were narrower for the automated method, which also reduced the construction time to an average of 46 s compared to 150 s manually (p < .001). These findings suggest that the automated method has the potential to enhance the accuracy and efficiency of preoperative and postoperative computer-assisted procedures for RHA. Further research is needed to fully understand the utility of this automated system for enhancing RHA computer-assisted surgical planning.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images. Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses). The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15mm. Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

Development of a Web-based Tomato Plant Disease Detection and Diagnosis System using Transfer Learning Techniques

A significant obstacle to agricultural productivity that jeopardizes the availability of food is crop diseases and farmer livelihoods by reducing crop yields. Traditional visual assessment methods for disease diagnosis are effective but complex, often requiring expert observers. Recent advancements in deep learning indicate the potential for increasing accuracy and automating disease identification. Developing accessible diagnostic tools, such as web applications leveraging CNNs, can provide farmers with efficient and accurate disease identification, especially in regions with limited access to advanced diagnostic technologies. The main goal is to develop a productive system that can recognize tomato plant diseases. The model was trained on a collection of images of healthy and damaged tomato leaves from PlantVillage using transfer learning techniques. The images from the dataset were cleansed by resizing them from 256 × 256 to 224 × 224 to match the dimensions used in pre-trained models using min-max normalization. An evaluation of VGG16, VGG19, and DenseNet121 models based on performance accuracy and loss value for 7 categories of tomatoes guided the selection of the most effective model for practical application. VGG16 achieved 84.54% accuracy, VGG19 achieved 84.62%, and DenseNet121 achieved 98.28%, making DenseNet121 the chosen model due to its highest performance accuracy. The web application development based on the DenseNet121 architecture was integrated using the Django web framework, which is built on Python. This enables real-time disease diagnosis for uploaded images of tomato leaves. The proposed system allows early detection and diagnosis of tomato plant diseases, helping to mitigate crop losses. This supports sustainable farming practices and increases agricultural productivity.

Exploring communities’ utilization of Jatropha based biofuels to transition towards cleaner energy sources

Abstract Ethiopia is committed to using sustainable energy sources due to the limited availability of traditional fuel and their environmental damage. This study examines the viability of biofuels as an alternative fuel source in Ethiopia for community’s sustainable transition to cleaner energy. The research reviews relevant publications, policies, initiatives, and programs, identifies barriers to implementation, and collects data through literature reviews, community discussions and observations of energy experts. Ethiopia has 8.6 million hectares available for energy crops and governmental policies are encouraging the use of clean fuels. The study suggests that biofuel can support rural development and environmental sustainability. However, challenges to the widespread adoption of alternative fuels by communities exist such as flawed policies, limited technical skills, insufficient maintenance experts, a scarcity of biofuel production companies, and inadequate markets for biofuel seeds. To achieve a sustainable future through the use of clean energy in the country, policy interventions and investment strategies are necessary. All stakeholders must adopt policies and develop capacity-building programs while providing incentives for developers and users.

Stakeholder perceptions of the Commission for the Conservation of Antarctic Marine Living Resources Marine Protected Area planning process

Establishing Marine Protected Areas (MPAs) in international waters is critical for the conservation of marine biodiversity. The Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) is a global leader in high seas conservation, having established two international MPAs in Antarctica’s Southern Ocean, as part of a commitment to establish a representative system of MPAs for the CAMLR Convention Area. CCAMLR MPAs are adopted by consensus. However, consensus decision-making has slowed the MPA adoption process, and since 2016 the Commission has been unable to agree and implement further MPAs, which has stalled the development of the circumpolar representative system. The key aim of this study was to identify how features of the planning process contribute to outcomes, problems and solutions. We conducted semi-structured interviews with 18 scientists, policymakers, and expert observers from fisheries and environmental non-government organisations involved in CCAMLR MPA planning, to explore their perceptions (experiences, opinions, impressions) of the planning process. Our results identify four key features that are influential across multiple areas of the CCAMLR MPA planning and decision-making process: i) a lack of common understanding; ii) the influence of historical relationships and legacy in subsequent planning processes; iii) inconsistencies between expectations of and requirements for MPA planning; and iv) the degree to which CCAMLR principles and practices, norms, and values are shared. We demonstrate how these four features may help or hinder the effectiveness of the current planning process. We then make suggestions regarding how the planning process could be adapted to capitalise on these features.

The The Design of an Interprofessional Health Record Form for Diabetes Mellitus Care at Primary Health Care Facility: A Recommendation

This research focuses on the pillars of primary care transformation by increasing primary services' capacity and capability, especially Diabetes Mellitus (DM) care. The study aims to produce a recording form containing simple, interprofessional records of DM patient care that can be used by various professions and can be applied in multiple primary health facilities. The design was developed through a development research approach with stages of form modification, expert and practitioner review, revision, trial, focus group discussion, and final revision. The trial was carried out by Community Health Center (Puskesmas) nurses in services at the Puskesmas through home visits for DM patients, Posbindu services, and outpatient care. Data from expert opinions, user opinions, and observations of the forms tested were analyzed qualitatively using the Technology Acceptance Model concept approach. The resulting form consists of two pages containing primary patient data and daily progress notes in tabular form. Research participants showed positive beliefs, attitudes, and behavior toward the forms being tested—the actual use of the form on an average of 3 patients per participant. The completeness was 90 percent filled. They expressed their intention to use the form further and recommended widespread dissemination to implement it in practice. The form is acceptable and ready to be applied for outpatient care, health monitoring, and home care for DM cases. Recommendations can be given to policymakers and health service providers to implement in services. Apart from that, the form can be further developed in electronic form by information technology developers.

A Qualitative Systematic Review and Meta-synthesis of Mothers’ Experiences of Parenting Autistic Women and Girls

AbstractThis review systematically explores qualitative evidence of the lived experience of mothers’ parenting autistic women and girls. A SPIDER search strategy was conducted on five databases and in the grey literature. Backward, forward and relevant journal scanning was also carried out. Twenty-two qualitative or mixed-method studies were included and quality appraised using CASP, encompassing over 150 mothers. A thematic synthesis highlighted five super-ordinate themes; ‘The diagnosis process and getting support’, ‘Mother’s role as all consuming’, ‘Experiences of family wellbeing’, ‘Mother as an expert observer’ and ‘The hopes and fears of mothers’. The review highlights areas of motivation to socialise, masking and menstruation needs as key differences in the autistic female presentation from a mothers’ perspective. Implications for practice are discussed.

Intraoperative teamwork and occupational stress during robot-assisted surgery: An observational study

Robot-assisted surgery (RAS) differs from traditional OR set-ups in several ways such as operation of technology and obstructed team communication that potentially affect surgical staff's stress experiences. The current study investigates the effects of key intraoperative job demands and resources on mental workload and perceived stress in RAS. We focused on the role of intraoperative teamwork as a resource that potentially reduces occupational stress. Combining standardized expert observations in the OR with healthcare providers' self-reports, the study involved two types of robot-assisted, urological interventions. The sample consisted of 73 observed surgeries and included 242 post-operative surveys on perceived stress and mental workload from surgeons and surgical nurses. Multilevel regression analyses reveal differential effects for stress and workload. Importantly, whereas better surgical teamwork was associated with lower stress, it was unrelated to workload. Our findings provide a nuanced picture of occupational stress in RAS, particularly regarding the role of intraoperative teamwork.

Artificial intelligence and dental age estimation: development and validation of an automated stage allocation technique on all mandibular tooth types in panoramic radiographs.

Age estimation in forensic odontology is mainly based on the development of permanent teeth. To register the developmental status of an examined tooth, staging techniques were developed. However, due to inappropriate calibration, uncertainties during stage allocation, and lack of experience, non-uniformity in stage allocation exists between expert observers. As a consequence, related age estimation results are inconsistent. An automated staging technique applicable to all tooth types can overcome this drawback.This study aimed to establish an integrated automated technique to stage the development of all mandibular tooth types and to compare their staging performances.Calibrated observers staged FDI teeth 31, 33, 34, 37 and 38 according to a ten-stage modified Demirjian staging technique. According to a standardised bounding box around each examined tooth, the retrospectively collected panoramic radiographs were cropped using Photoshop CC 2021® software (Adobe®, version 23.0). A gold standard set of 1639 radiographs were selected (n31 = 259, n33 = 282, n34 = 308, n37 = 390, n38 = 400) and input into a convolutional neural network (CNN) trained for optimal staging accuracy. The performance evaluation of the network was conducted in a five-fold cross-validation scheme. In each fold, the entire dataset was split into a training and a test set in a non-overlapping fashion between the folds (i.e., 80% and 20% of the dataset, respectively). Staging performances were calculated per tooth type and overall (accuracy, mean absolute difference, linearly weighted Cohen's Kappa and intra-class correlation coefficient). Overall, these metrics equalled 0.53, 0.71, 0.71, and 0.89, respectively. All staging performance indices were best for 37 and worst for 31. The highest number of misclassified stages were associated to adjacent stages. Most misclassifications were observed in all available stages of 31.Our findings suggest that the developmental status of mandibular molars can be taken into account in an automated approach for age estimation, while taking incisors into account may hinder age estimation.

Peculiarities of commissions of forensic medical examinations in criminal cases related to crimes against sexual inviolability

The investigation of criminal cases related to sex crimes determines the range of circumstances to be immediately proved during investigations. Issues arising in the process of conducting such cases must be resolved as part of forensic medical examinations. Illegal acts of a sexual nature generally end in the violation of human health, not only physical with bodily harm but also psychological. During such examinations, specialists of various clinical specialties are included in the composition of expert commissions. These are specialists in the fields of obstetrics-gynecology, venereology, traumatology, psychiatry, and clinical psychology. During investigation and trial, important information can be provided by medical documents on the state of health of the victim, including the testimony of medical workers. Not only forensic medical experts who conducted examinations but also medical workers are called to court hearings as witnesses to incidents. This article analyzed the commissions of forensic medical examinations in a teenager harmed by violent sexual acts. The results of forensic medical examinations were analyzed. This article provides an expert observation of a victim of crimes against sexual inviolability that led to severe complications. During the investigation, the development of a mental disorder in the teenager was examined, and a forensic medical examination was performed. Psychological injuries are no less serious than physical ones. The body’s response to stress causes a pathological state of the psyche. Such a painful reaction can persist for many years. A prolonged depressive reaction due to an adaptation disorder arose in the victim after the unlawful actions committed against her with a direct causal relationship. When conducting forensic medical examinations related to illegal acts of a sexual nature, specialists in the fields of gynecology, traumatology, venereology, psychiatry, and clinical psychology must take part in the examinations.

The Development of an Ethnoscience-Based Mangrove Module in Improving Student’s Critical Thinking Abilities and Concern for the Environment at SMA IT Daarul Hikmah Bontang

A module is an educational material or learning tool that includes teaching materials, methods, and assessments that are developed in a systematic and structured manner to achieve expected competency goals. The aim of this study is to develop a valid, practical and effective mangrove module based on ethnoscience on biodiversity. The type of research is R&amp;D which refers to the ADDIE method developed by Mayfield. Data collection techniques consist of expert verification, observations, tests, questionnaires, and interviews. The results of the study show that, this module was developed based on the manual previously used by SMA IT Darul Hikmah Bontang students to learn biology. The module validity result for the module is 99%, which is in the very valid category. The material validity result of the module was in the very reasonable category at 84.69%. The linguistic validity result of the module was 73.6% in the valid category. The results of the student survey regarding the practicality of the module were 88.65%, which fell into the very practical category. The effectiveness module in improving student’s critical thinking ability and environmental awareness have n-gain values ​​are 0.55 and 0.44. It can be concluded that the development of the ethnoscience-based mangrove module in the intermediate category has been effectively used to improve the critical thinking skills of the students of SMA IT Darul Hikmah Bontang.

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test-retest reproducibility ADPKD patients. Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test-retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

Benign epileptiform variants in EEG: A comprehensive study of 3000 patients

BackgroundThe analysis of EEG demands expertise and keen observation to distinguish epileptiform discharges from benign epileptiform variants (BEVs), a frequent source of erroneous interpretation. The prevalence of BEVs varies based on geographical, racial, and ethnic characteristics. However, most data on BEVs originates from Western populations, and additional studies on different cohorts would enrich the existing literature. MethodsWe reviewed EEGs from our institutional database to study the prevalence of benign epileptiform variants and analyzed their frequency, topography, and other characteristics. Additionally, we investigated the co-existence of epileptiform discharges with BEVs. ResultsWe identified 296 patients with BEVs after reviewing 3000 EEGs (9.9%). The most common BEV was small sharp spikes (SSS), observed in 114 patients (3.8%). Wicket waves, 6 Hz spike and slow wave, 14 and 6 Hz positive bursts, and Rhythmic Temporal Theta of Drowsiness (RTTD) were identified in 67 (2.2%), 40 (1.3%), 39 (1.3%), and 35 (1.16%) patients, respectively and one patient with Subclinical Rhythmic EEG Discharges in Adults (SREDA). Additionally, we observed the co-existence of epileptiform discharges with BEVs, most commonly with SSS (27.8%). ConclusionsThe present study is a large study with 3000 EEGs to describe the BEV characteristics. BEVs were seen in 9.9% of patients, BSSS being the most common. There were minor differences in frequency, gender or age distribution compared to existing literature. We demonstrated the co-existence of epileptiform discharges. Morphological characteristics remain the cornerstone in recognising BEVs. EEG readers need to be aware of features of BEVs to avoid wrongly interpretation.

Evaluation of the Quality and Reliability of YouTube Videos on Gender Dysphoria

Purpose: Many people search for information on YouTube on almost every medical topic. The aim of this study was to assess the quality and reliability of YouTube videos concerning Gender Dysphoria. Methods: We searched for Gender Dysphoria on YouTube on June 7, 2023 and conducted a detailed evaluation of the first 100 unique English videos by two expert observers. The videos were evaluated using modified DISCERN scale, Global Quality Scale (GQS), and Video Power Index (VPI). Results: The kappa coefficient for interobserver agreement was 0.892. For all 100 videos, the median value of the modified DISCERN score was 2.5 (1-4.8), and GQS score was 3 (1-5), and 68% of the videos rated as poor to moderate reliability and quality (modified DISCERN score ≤3, GQS ≤3). Reliable videos were uploaded by professional sources in a higher rate than unreliable videos (48% vs 12%, p

Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This paper develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with combining multi-source uncertainty information for vehicle, such as flying machine. To improve the performance of fault diagnosis for vehicle, a new belief rule base expert system with multi-source uncertainty information (BRB-MU) is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle three problems: unbalanced observation data of system states, uncertain expert knowledge and random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition (SVD) in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of BRB-MU is conduced that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conduced.