Search Results (27)

Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is proposed. When sparse PU activity is masked by irregular interference bursts, traditional sensing algorithms misclassify weak transmissions as noise, leading to low detection reliability. To resolve this, the first hidden layer employs Discrete Wavelet Sparse Bayesian Kernel Analysis (DW-SBK), integrating Discrete Wavelet Packet Transform (DWPT), Sparse Bayesian Learning (SBL), and Kernel PCA. This restores the true sparse pattern of the spectrum, separates interference from actual PU signals, and enhances detection of weak channels. Additionally, PU signals are fragmented due to cross-scale activity drift, where dynamic bandwidth switching and variable burst durations disrupt temporal continuity. Therefore, the second layer incorporates Gradient Boosted Multi-Head Fuzzy Clustering (GB-MHFC), where Gradient Boosted Decision Trees (GBDT) model nonlinear spectral–temporal patterns, Multi-Head Self-Attention (MHSA) captures long- and short-range temporal dependencies, and Fuzzy C-Means Clustering (FCM) groups feature representations into stable PU activity modes, thereby reducing misclassifications and enhancing robustness under highly dynamic CRN conditions. The proposed method demonstrates superior performance with a maximum detection probability of 0.98, classification accuracy of 98%, lowest sensing error of 5.412%, and the fastest sensing time of 3.65 s. Full article

Traditional inspection and diagnosis methods for civil infrastructure operation and maintenance (CI O&M) rely heavily on human efforts. Such efforts are always affected by subjective judgment and human errors due to engineering knowledge and prior experiences of field engineers. On the other hand, recent development of AI-driven tools could achieve effective information acquisition but lacks interpretability and engineering credibility. How to integrate human knowledge with AI capacity for safe and effective CI O&M is thus necessary in this new era. This paper presents a human-in-the-loop digital twin (HITL-DT) framework that enables safety risk sensing, prediction and control for smart CI O&M. The proposed framework fuses human cognition (i.e., individual perception and team situation awareness), AI and engineering knowledge for 1) risk sensing and diagnosis based on spatiotemporal changes and 2) risk prediction and control for smart CI O&M. Qualitative analysis indicates that the HITL-DT approach produces more explainable, trustworthy, and actionable diagnostic outputs, which enhance the reliability and proactivity of CI O&M. Full article

Suction anchors play an important role in the exploration and development of marine natural gas hydrate (NGH). Suction anchors increase the bearing capacity and reduce tilting or sinking risk of underwater wellheads in the exploration and development process. This study proposes a dynamic failure analysis procedure for suction anchor installation based on the DBN-GO method. Firstly, a Goal-Oriented (GO) model is established by analyzing the human and equipment factor nodes in the suction anchor installation operation process. A Bayesian Network (BN) analysis model is set up by mapping the key nodes in the GO model. Then, the Cognitive Reliability and Error Analysis Method (CREAM) and the Dempster–Shafer (D-S) evidence theory are used to quantify the failure probabilities of human and equipment factor nodes in the BN model. The main risk factors are identified using Bayesian backward inference. Finally, the dynamic risk assessment of the suction anchor installation operation is conducted, considering the equipment node transition probability of the BN. Tkae the second production test of natural gas hydrates in the South China Sea as a case study. The study result shows that the failure probability of the suction anchor installation operation is 0.298%, which is at a low-risk level. Suction pump pressure control is the most critical factor leading to human errors. Among the equipment factor, the reliability of the suction pump and the ROV is the most important. Dynamic Bayesian inference shows the risk gradually increases with time. A reasonable maintenance strategy is conducive to reducing the accumulated risks caused by the time-varying degradation of equipment performance. The results could provide significant support in risk management and decision-making for the suction anchor installation operation, which will further promote the environmental sustainability, operational safety and economic feasibility of marine natural gas hydrate development. Full article

Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article

Background: The Beck Depression Inventory Second Edition (BDI-II) is used to assess depression worldwide. In Mexico, the BDI-II Spanish translation is widely used. Despite more than 23 million people being identified as indigenous, there is no empirical evidence on the BDI-II psychometric properties among indigenous languages, including Otomi. Therefore, this study aimed to cross-culturally adapt the BDI-II for the Otomi population and evaluate its psychometric properties. Methods: This cross-sectional instrumental study with non-probability sampling was conducted with 228 participants from the Otomi community. The cross-cultural adaptation of the BDI-II followed Beaton’s guidelines for self-report measures: (i) translation, (ii) synthesis, (iii) back translation, (iv) expert committee review, (v) pretesting, and (vi) submission of documentation to the developers. Reliability was assessed using Cronbach’s alpha. Exploratory and confirmatory factor analyses were used to determine structural and construct validity. Results: The cross-culturally adapted instrument showed adequate reliability, with a total Cronbach’s α of 0.756, comprising 14 items and four factors (with alpha coefficients ranging from 0.505 to 0.633). These factors included three cognitive–affective dimensions and one somatic dimension, which conceptually align with Beck’s original model. Confirmatory factor analysis (CFA) presented adequate indices: Comparative Fit Index (CFI) = 0.901, Root Mean Square Error of Approximation (RMSEA) = 0.056, IC_90% [0.028–0.079], and Goodness-of-Fit Index = 0.908, which indicate a balanced and parsimonious fit of the model. Conclusions: The BDI-II is a reliable and culturally valid instrument for measuring depressive symptoms among the Otomi people of the Mezquital Valley. Full article

Background: Quantitative gait analysis is essential in both clinical and research contexts; however, traditional marker-based motion capture systems are costly and burdensome. Advances in three-dimensional markerless motion capture (3D-MMC) offer more accessible alternatives; however, they lack standardized protocols. Objectives: The present study aimed to establish a standardized protocol and procedures for 3D MMC-based gait analysis using OpenCap and to quantify the reliability and within-session precision of key spatiotemporal gait parameters. Methods: Fifty healthy university students (mean age = 22.15 ± 2.12 years) completed walking trials along a 10 m walkway under single-task (ST) and five dual-task (DT) conditions of varying cognitive complexity. Gait data were collected using a two-camera OpenCap 3D-MMC system, with standardized calibration, lighting, clothing, and trial segmentation. Spatiotemporal parameters were extracted, and within-session relative reliability was quantified using two-way mixed-effects intraclass correlation coefficients, and absolute reliability was quantified using general linear model–derived within-subject error (standard error of measurement, SEM) and minimal detectable change (MDC). Repeated-measures ANOVA with Bonferroni corrections were used to examine condition-related differences. Results: Of 500 trials, 491 (98.2%) were successfully processed. Within-subject test–retest reliability ranged from moderate to excellent for all variables, with gait speed, stride length, and cadence showing the highest ICCs and smallest SEM and MDC values, and step width and double support exhibiting larger measurement error. Conclusions: This study establishes a standardized 3D-MMC protocol for gait analysis using OpenCap and demonstrates good to excellent within-session relative and absolute reliability for most spatiotemporal gait parameters in healthy young adults. Dual-task walking is used here to illustrate how trial-averaged OpenCap measurements and their SEM/MDC can be used to determine which condition-related changes in gait exceed measurement error. Full article

Background: Physical activity (PA) provides significant health benefits, yet inactivity remains high in Spain, especially among adolescents and increasingly in children. Identifying barriers to PA is essential, but available tools are mainly designed for adolescents. This study aimed to adapt the “Brief Scale of Perceived Barriers to Physical Activity” for Spanish schoolchildren aged 6–12 and examine its validity and reliability. Methods: The “Brief Scale of Perceived Barriers to Physical Activity for Children” was linguistically and culturally adapted. Comprehension was assessed through cognitive interviews, and reliability was examined via a test–retest procedure with 137 Spanish schoolchildren. Several analyses were conducted, including confirmatory factor analysis (CFA) to assess the factor structure, along with reliability metrics: Cronbach’s alpha (α) for internal consistency and the intraclass correlation coefficient (ICC) for test–retest reliability. Results: CFA confirmed a four-factor structure (self-concept, motivation–interest, social support, and task incompatibility) in a sample of 137 with excellent fit indices (χ²/df = 1.394, RMSEA = 0.054, CFI = 0.976, TLI = 0.966). Internal consistency ranged from good to excellent (α = 0.831–0.979). Temporal stability was substantial to near perfect (ICC = 0.708–0.979). Measurement error was low for all items and the total score (SEM% = 6.1–37.2; MDC% = 17.0–103.0), demonstrating accuracy. Conclusions: The “Brief Scale of Perceived Barriers to Physical Activity for Children” was proven to be a reliable and valid tool for assessing perceived barriers to PA in Spanish children. It offers developmentally appropriate insights that can guide strategies to enhance supportive environments and promote long-term active behaviours. As part of the social domain, it contributes to the Spanish Physical Literacy Assessment for Children (SPLA-C) model, the first physical literacy (PL) assessment instrument developed in Spain. Full article

Background/Objectives: In Spain, a high proportion of children do not meet the recommended daily levels of physical activity (PA), which highlights the urgent need to understand the motivational factors that could influence PA behavior. Self-Determination Theory is a widely used approach for assessing motivation toward exercise, employing instruments such as the Behavioral Regulation in Exercise Questionnaire (BREQ-3). However, despite the cognitive and linguistic differences that limit its direct application, this tool has not yet been adapted for children aged 6–12 years. This study aimed to adapt the BREQ-3 for use with Spanish schoolchildren and to evaluate its validity and reliability in this age group. Methods: The BREQ-3 for children (BREQ-3C) was linguistically and culturally adapted. Comprehension was tested through cognitive interviews, and reliability was assessed via a test–retest with 125 Spanish schoolchildren. Statistical analyses: Confirmatory factor analysis (CFA), Cronbach’s alpha, and the intraclass correlation coefficient (ICC) were used to evaluate validity and reliability. Results: CFA supported the factorial structure of the adapted BREQ-3 for primary schoolchildren, showing acceptable model fit indices (chi-square minimum discrepancy/degrees of freedom (CMIN/df) = 1.552, root mean square error of approximation (RMSEA) = 0.053, comparative fit index (CFI) = 0.891, Tucker-Lewis index (TLI) = 0.870). Internal consistency ranged from poor to excellent for all items and the total score of the questionnaire (Cronbach’s alpha (α): 0.535 to 0.911), except for items 3, 13, 20, and 21, where the internal consistency was unacceptable. Test–retest reliability was generally satisfactory, with ICC values indicating fair to excellent temporal stability (ICC: 0.248 to 0.911). The measurement error indicators (standard error of measurement percentage (SEM%) and minimal detectable change percentage (MDC%)) varied widely, particularly for the less reliable items. Most item scores were not significantly different between the test and retest groups, although items 2, 3, 5, 9, 17, 19, and 20 were significantly different. Conclusions: The BREQ-3C has promising psychometric properties for assessing exercise motivation in children aged 6–12 years. This tool shows potential for use in research, education, and health interventions to understand and promote physical activity motivation in primary schools. Full article

This project looks deeply at the integration of human and equipment reliability in hydrogen bunkering operations, focusing on human corrective response actions (HCRAs) to unwanted events in the process. Human responses are also actions shaped by human performance thereby not totally devoid of human error. The possibility of the occurrence of accidents even when both personnel and equipment are reliable draws great attention to examine human responses to occurrences in the bunkering process of hydrogen. The Cognitive Reliability and Error Analysis Method (CREAM) is adopted alongside equipment reliability data from the maritime industry to assess the connection between system performance and human decision-making in the bunkering operation process. The findings show that enhanced equipment reliability significantly improves human corrective responses, leading to great operational efficiency. This study proposes an integrated reliability framework to optimize hydrogen bunkering procedures vis-à-vis an enhanced safety response, providing recommendations for improving safety regulations, and necessitate operator training, equipment management, and risk mitigation approaches. By ensuring industrial compliance and enhancing overall reliability in ship propulsion, these insights contribute to the use of hydrogen as an alternative fuel in the maritime sector for ship propulsion. Full article

Background and Objectives: General-purpose multimodal large language models (LLMs) are increasingly used for medical image interpretation despite lacking clinical validation. This study evaluates the diagnostic reliability of ChatGPT-4o and Claude 2 in photographic assessment of adolescent idiopathic scoliosis (AIS) against radiological standards. This study examines two critical questions: whether families can derive reliable preliminary assessments from LLMs through analysis of clinical photographs and whether LLMs exhibit cognitive fidelity in their visuospatial reasoning capabilities for AIS assessment. Materials and Methods: A prospective diagnostic accuracy study (STARD-compliant) analyzed 97 adolescents (74 with AIS and 23 with postural asymmetry). Standardized clinical photographs (nine views/patient) were assessed by two LLMs and two orthopedic residents against reference radiological measurements. Primary outcomes included diagnostic accuracy (sensitivity/specificity), Cobb angle concordance (Lin’s CCC), inter-rater reliability (Cohen’s κ), and measurement agreement (Bland–Altman LoA). Results: The LLMs exhibited hazardous diagnostic inaccuracy: ChatGPT misclassified all non-AIS cases (specificity 0% [95% CI: 0.0–14.8]), while Claude 2 generated 78.3% false positives. Systematic measurement errors exceeded clinical tolerance: ChatGPT overestimated thoracic curves by +10.74° (LoA: −21.45° to +42.92°), exceeding tolerance by >800%. Both LLMs showed inverse biomechanical concordance in thoracolumbar curves (CCC ≤ −0.106). Inter-rater reliability fell below random chance (ChatGPT κ = −0.039). Universal proportional bias (slopes ≈ −1.0) caused severe curve underestimation (e.g., 10–15° error for 50° deformities). Human evaluators demonstrated superior bias control (0.3–2.8° vs. 2.6–10.7°) but suboptimal specificity (21.7–26.1%) and hazardous lumbar concordance (CCC: −0.123). Conclusions: General-purpose LLMs demonstrate clinically unacceptable inaccuracy in photographic AIS assessment, contraindicating clinical deployment. Catastrophic false positives, systematic measurement errors exceeding tolerance by 480–1074%, and inverse diagnostic concordance necessitate urgent regulatory safeguards under frameworks like the EU AI Act. Neither LLMs nor photographic human assessment achieve reliability thresholds for standalone screening, mandating domain-specific algorithm development and integration of 3D modalities. Full article

Human reliability analysis (HRA) is widely used to evaluate the impact of human errors on various complex human–machine systems for enhancing their safety and reliability. Nevertheless, it is hard to estimate the human error probability (HEP) in reality due to the uncertainty of state assessment information and the complex relations among common performance conditions (CPCs). In this paper, we aim to present a new integrated cognitive reliability and error analysis method (CREAM) to solve the HRA problems under probabilistic linguistic environment. First, the probabilistic linguistic term sets (PLTSs) are utilized to handle the uncertain task state assessments provided by experts. Second, the minimum conflict consensus model (MCCM) is employed to deal with conflict task state assessment information to assist experts reach consensus. Third, the entropy weighting method is used to determine the relative objective weights of CPCs. Additionally, the CPC effect indexes are introduced to assess the overall effect of CPCs on performance reliability and obtain the HEP estimation. Finally, the reliability of the proposed CREAM is demonstrated via a healthcare practical case. The result shows that the new integrated CREAM can not only effectively represent experts’ uncertain task state assessments but also determine more reliable HEP estimation in HRA. Full article

In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments. Full article

(1) Background: To develop an instrument in Spanish to assess beliefs and feelings about vaginal penetration and assess its psychometric properties. (2) Methods: This study translated and adapted the Vaginal Penetration Cognition Questionnaire into Spanish, and a total of 225 women who suffered from Genito-Pelvic Pain/Penetration Disorder were included in the study. The psychometric properties, including construct, convergent and discriminant validity, test–retest reliability, and internal consistency of the translated version were assessed. (3) Results: The Spanish version of the Vaginal Penetration Cognition Questionnaire is a valid, reliable, and consistent tool to assess beliefs and thoughts about vaginal penetration in women suffering from Genito-Pelvic Pain/Penetration Disorder. The exploratory factor analysis yielded four domains that explained 62.5% of the variance. Convergent and discriminant validity was also confirmed. Test–retest reliability was high, with an intraclass correlation coefficient value of 0.90, a standard error of measurement of 4.21, and a minimal detectable change of 11.66 points. Every domain also showed good internal consistency levels, with Cronbach’s α values ranging from 0.84 to 0.89. (4) Conclusion: The Spanish version of the Vaginal Penetration Cognition Questionnaire is a valid, reliable, and consistent tool to assess vaginal penetration cognition in women suffering from Genito-Pelvic Pain/Penetration Disorder. Full article

In recent years, chemical experiment accidents have frequently occurred, resulting in injuries and fatalities among researchers. It is crucial to address this issue to improve laboratory safety. Based on many publications, it is clear that human error makes a major contribution to many laboratory accidents which contain hazardous processes. However, there is limited research focusing on human error in laboratory safety, and there is also a lack of effective measures to assess Human Error Probability (HEP) for experimental process safety. Therefore, we propose an improved Cognitive Reliability and Error Analysis Method (CREAM) which is based on risk data to assess the HEP during hazardous processes in chemical experiments. The proposed method adjusts nine Common Performance Conditions (CPCs) in conventional CREAM to make them suitable to describe chemical experiments. Then, in contrast to the traditional approach, this study uses the definition of risk as the support to collect CPC data from the perspectives of possibility and severity, so as to improve the rationality of the data and decrease the subjectivity of expert judgment. Afterwards, the weight value of each CPC is calculated through Gray Relation Analysis (GRA) based on the collected risk data of each CPC. Meanwhile, the collected risk data are used to determine the fuzzy degrees of each CPC, the activated fuzzy If-Then rules, and the corresponding rule weights. Finally, the CPCs’ membership degrees, the CPCs’ weights, and If-Then rule weights are integrated together to acquire the HEP by defuzzification. In short, the proposed method changes the CPCs to ensure they are suitable, and then it innovatively uses risk data as the source to directly and indirectly determine the CPC’s fuzzy degree, the CPC’s importance weight, and the If-Then rule weight by fuzzy theory and GRA for collecting final HEP results. This method was tested on a selected chemical experiment, “preparation of active ferrous sulfide”, which contains hazardous processes. Through the proposed method, the HEP of each procedure in the selected risky chemical experiment could be determined, and among the procedures, the highest HEP was 1.51 × 10⁻³. In addition, with the HEP results, several subtasks with a high risk of human error could be identified. The results matched the real situations. Full article

Background: Literature on the prevention of medication errors is growing, highlighting that knowledge, attitude and behavior with regard to medication errors are strategic to planning of educational activities and evaluating their impact on professional practice. In this context, the present pilot study aims to translate and validate nursing professionals’ knowledge, attitudes and behavior (KAB theory) concerning medication administration errors in ICU from English into Persian. Furthermore, two main objectives of the project were: performing a pilot study among Iranian nurses using the translated questionnaire and carrying out a cultural measurement of the KAB theory concerning medication administration errors in an ICU questionnaire across two groups of Italian and Iranian populations. Methods: A cross-cultural adaptation of an instrument, according to the Checklist for reporting of survey studies (CROSS), was performed. The convenience sample was made up of 529 Iranian and Italian registered nurses working in ICU. An exploratory factor analysis was performed and reliability was assessed. A multi-group confirmatory factor analysis was conducted to test the measurement invariance. Ethical approval was obtained. Results: There was an excellent internal consistency for the 19-item scale. Results regarding factorial invariance showed that the nursing population from Italy and Iran used the same cognitive framework to conceptualize the prevention of medication errors. Conclusions: Findings from this preliminary translation and cross-cultural validation confirm that the questionnaire is a reliable and valid instrument within Persian healthcare settings. Moreover, these findings suggest that Italian and Persian nurses used an identical cognitive framework or mental model when thinking about medication errors prevention. The paper not only provides, for the first time, a validated instrument to evaluate the KAB theory in Iran, but it should promote other researchers in extending this kind of research, supporting those countries where attention to medical error is still increasing. Full article