Multiple Observers Research Articles

Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Multiple observers ranked set samples for shrinkage estimators

Ranked set sampling (RSS) is used as a powerful data collection technique for situations where measuring the study variable requires a costly and/or tedious process while the sampling units can be ranked easily (e.g. osteoporosis research). In this paper, we develop ridge and Liu-type shrinkage estimators under RSS data from multiple observers to handle the collinearity problem in estimating coefficients of linear regression, stochastic restricted regression and logistic regression. Through extensive numerical studies, we show that shrinkage methods with the multi-observer RSS result in more efficient coefficient estimates. The developed methods are finally applied to bone mineral data for analysis of bone disorder status of women aged 50 and older.

Application of Spinal Subtraction and Bone Background Fusion CTA in the Accurate Diagnosis and Evaluation of Spinal Vascular Malformations.

Accurate pretreatment diagnosis and assessment of spinal vascular malformations using spinal CTA are crucial for patient prognosis, but the postprocessing reconstruction may not be able to fully depict the lesions due to the complexity inherent in spinal anatomy. Our purpose was to explore the application value of the spinal subtraction and bone background fusion CTA (SSBBF-CTA) technique in precisely depicting and localizing spinal vascular malformation lesions. In this retrospective study, patients (between November 2017 and November 2022) with symptoms similar to those of spinal vascular malformations were divided into diseased (group A) and nondiseased (group B) groups. All patients underwent spinal CTA using Siemens dual-source CT. Multiplanar reconstruction; routine bone subtraction, and SSBBF-CTA images were obtained using the snygo.via and ADW4.6 postprocessing reconstruction workstations. Multiple observers researched the following 3 aspects: 1) preliminary screening capability using original images with multiplanar reconstruction CTA, 2) the accuracy and stability of the SSBBF-CTA postprocessing technique, and 3) diagnostic evaluation of spinal vascular malformations using the 3 types of postprocessing images. Diagnostic performance was analyzed using receiver operating characteristic analysis, while reader or image differences were analyzed using the Wilcoxon signed-rank test or the Kruskal-Wallis rank sum test. Forty-nine patients (groups A and B: 22 and 27 patients; mean ages, 44.0 [SD, 14.3] years and 44.6 [SD,15.2] years; 13 and 16 men) were evaluated. Junior physicians showed lower diagnostic accuracy and sensitivity using multiplanar reconstruction CTA (85.7% and 77.3%) than senior physicians (93.9% and 90.9%, 98% and 95.5%). Short-term trained juniors achieved SSBBF-CTA image accuracy similar to that of experienced physicians (P > .05). In terms of the visualization and localization of spinal vascular malformation lesions (nidus/fistula, feeding artery, and drainage vein), both multiplanar reconstruction and SSBBF-CTA outperformed routine bone subtraction CTA (P = .000). Compared with multiplanar reconstruction, SSBBF-CTA allowed less experienced physicians to achieve superior diagnostic capabilities (comparable with those of experienced radiologists) more rapidly (P < .05). The SSBBF-CTA technique exhibited excellent reproducibility and enabled accurate pretreatment diagnosis and assessment of spinal vascular malformations with high diagnostic efficiency, particularly for junior radiologists.

Remote state preparation by multiple observers using a single copy of a two-qubit entangled state

Remote state preparation by multiple observers using a single copy of a two-qubit entangled state

Competing judgments: Multiple election observers and post-election contention

Competing judgments: Multiple election observers and post-election contention

Sharing Genuine Entanglement of Generalized Tripartite States by Multiple Sequential Observers

Sharing Genuine Entanglement of Generalized Tripartite States by Multiple Sequential Observers

Peer assessment in medical communication skills training in programmatic assessment: A qualitative study examining faculty and student perceptions

Introduction Current literature recommends assessment of communication skills in medical education combining different settings and multiple observers. There is still a gap in understanding about whether and how peers assessment facilitates learning in communication skills training. Methods We designed a qualitative study using focus group interviews and thematic analysis, in a medical course in the Netherlands. We aimed to explore medical students’ and teachers’ experiences, perceptions, and perspectives about challenges and facilitating factors in PACST (Peer assessment in medical communication skills training). Results Most of the participants reported that peer feedback was a valuable experience when learning communication skills. The major challenges for the quality and credibility of PACST reported by the participants are the question whether peer feedback is critical enough for learning and the difficulty of actually engaging students in the assessment process. Conclusion Teachers reviewing students’ peer assessments may improve the quality and their credibility and the reviewed assessments can best be used for learning purposes. We suggest to pay sufficient attention to teachers’ roles in PACST, ensuring a safe and trustworthy environment and additionally helping students to internalize the value of being vulnerable during the evaluation process.

Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement.

Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

Parallel Multiple Extended State Observers Based ADRC With Application to High-Speed Precision Motion Stage

Parallel Multiple Extended State Observers Based ADRC With Application to High-Speed Precision Motion Stage

Measuring vacillations in reasoning

Abstract Our experience of reasoning is replete with conflict. People phenomenologically vacillate between options when confronted with challenging decisions. Existing experimental designs typically measure a summary of the experience of the conflict experienced throughout the choice process for any individual choice or even between multiple observers for a choice. We propose a new method for measuring vacillations in reasoning during the time-course of individual choices, utilizing them as a fine-grained indicator of cognitive conflict. Our experimental paradigm allows participants to report the alternative they were considering while deliberating. Through 3 experiments, we demonstrate that our measure correlates with existing summary judgments of conflict and confidence in moral and logical reasoning problems. The pattern of deliberation revealed by these vacillations produces new constraints for theoretical models of moral and syllogistic reasoning.

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.

Building a metric of color reproduction difference by combining multiple observers in a modular online experiment

AbstractA web‐hosted online experiment was previously developed to find the visual difference between four reproduction gamuts using direct magnitude estimation (Proc. IS&amp;T 29th Color and Imaging Conf, 2021:317–322). In order to increase the size of the data set, but without overburdening observers, a modular approach was adopted. The original methodology was therefore extended across 10 linked sub‐experiments to make comparisons between some 36 gamuts, which were designed to exhibit a variety of different gamut shapes, contrast ratios, and substrate colors within the constraints of a desktop display. In addition to each set of test images, a common normalization set was included in all sub‐experiments in order to adjust each observer's choice of modulus to a global average observer, and thus combine the results into a larger data set. Finally, an interval scale was inferred from the normalized magnitude data using a categorical judgment approach to calculate scale values. The fitted data revealed a power function close to a square‐root between the interval and magnitude scales.

Automated Egg-Counting Approaches for Aedes aegypti Oviposition Experiments.

Egg-laying preferences of mosquitoes can reveal key neurosensory mechanisms informing the decision-making process for this critical behavior. A single blood meal results in a gravid female Aedes aegypti mosquito laying more than 100 eggs. Therefore, egg counting represents a potentially time-consuming and laborious component to behavioral assays, such as those that measure oviposition preference or fecundity. Automated algorithms that count eggs from images can dramatically reduce the time required for this step of data processing and analysis and increase reproducibility associated with having multiple human observers count the eggs. Here, we present two distinct approaches for counting melanized Ae. aegypti eggs laid on white filter paper.

Reconstructing daily group sunspot numbers since the maunder minimum with objective inter-calibration algorithms

We present a new daily Group Sunspot Numbers (GSN) reconstruction from the Maunder minimum to Solar Cycle 24/25. This new reconstruction was obtained as a result of rectifying the raw and uncalibrated GSN database of Vaquero et al. (2016) and incorporating data from previously unconsidered GSN sources. Notably, a pivotal challenge encountered during the reconstruction of this GSN activity record, lay in the inter-calibration of different databases reported by multiple observers. The challenge is further compounded by data gaps that are particularly prevalent during the Maunder minimum. We have applied a novel methodology to solve these obstacles, entailing the calibration and standardization of all GSN databases relative to a reference observer. Our novel approach employs calibration techniques, ensuring standardized visual acuity between all individual observers. Moreover, our methodology accommodates variations in climatic, geographic, and instrumental conditions encountered by observers during their respective sunspot observations, thus establishing a uniform comparative framework. Furthermore, a comparative analysis was conducted with a total of four other different GSN reconstructions, revealing notable distinctions and resemblances that can be explained by the implementation of diverse methodologies in the respective GSN reconstruction processes and steps. In addition to the temporal differences found, both the wavelet and multi-cross wavelet analyses were carried out, which allowed a quantification of the differences and similarities in the spectral contents of various GSN records. The wavelet analysis results show the similarities and differences of the new GSN reconstruction with the inter-calibration algorithm when comparing it both temporally and spectrally with other GSN reconstructions. The periodicities of this analysis are 1.2, 5.5, 10.9, 20.6, 30.9, 58.4, and 119.8 years and 3.47, 5.4, 9.8, 27.8, 66.1, 117.8, and 160.5 days respectively.

Quantum Correlation Resource Recycling via Sequential Measurements: Theoretical Models and Optical Experiments

Quantum correlation is a key resource for a variety of quantum information processing and communication tasks, the efficient utilization of which has been a longstanding concern, and it is also one of the main challenges in the application of quantum technology. In this review, we focus on the interaction between quantum measurements and quantum correlations by designing appropriate measurement strategies, specifically exploring the trade-off between information gain and disturbance degree in weak measurements to ensure that quantum correlations from the same source can be shared among multiple independent observers. We introduce the basic knowledge and classification of quantum measurements, investigate the weak measurement scenario, and show the theoretical model construction of quantum correlation recycling in the original works. We summarize the theoretical and experimental development process and the latest progress in this field. Finally, we provide an outlook for more quantum resource applications that can profit from the optimization of quantum measurement strategies.

Comparison of visual and semi-automated kilovoltage cone beam CT image QA analysis.

Established kilovoltage cone-beam computed tomography (kV-CBCT) image quality assurance (QA) guidelines often rely on recommendations provided by the American Association of Physicists in Medicine (AAPM) task group (TG) reports with metrics that use visual analysis. This can result in measurement variations by different users, especially in visually subjective analyzes such as low contrast resolution. Consequently, there is a growing interest in more automated means of image QA analysis that can offer increased consistency, accuracy, and convenience. This work compares visual QA to semi-automated software QA analysis to establish the performance and viability of a semi-automated method. In this study, a commercial product (RIT Radia. Radiological Imaging Technology, Colorado Springs, CO) was used to evaluate 68 months of kV-CBCT images of a Catphan® 504 phantom obtained from a Varian TrueBeam® linear accelerator. Six key metrics were examined: high contrast resolution, low contrast resolution, Hounsfield unit constancy, uniformity and noise, and spatial linearity. The results of this method were then compared to those recorded visually using Bland-Altman, and/or paired sample t-test. Comparison of all modules showed a non-random, statistically significant difference between visual and semi-automated methods except for LDPE and Teflon in the Hounsfield unit constancy analysis, which falls outside the paired sample t-test's 5% significance level. A small high contrast resolution bias indicates the two analysis methods are largely equivalent, while a large low contrast resolution bias indicates greater semi-automated target detection. Wide limits of agreement in the uniformity module suggests variability due to multiple visual observers. Spatial linearity results measured differences of less than 0.17%. Semi-automated QA analysis offered greater stability over visual analysis. Additionally, semi-automated QA results satisfied or exceeded visual QA passing criteria and allowed for fast and consistent image quality analysis.

Assessment of Attitudes, Ethics and Communication Skills in a Competency-Based Undergraduate Medical Curriculum

Affective learning outcomes are difficult to objectively analyse and assess as they refer to feelings and internal processes of the mind and heart. These cannot be quantified by traditional testing methods. The available methods rely on qualitative self-reflection and the observation of simple to complex internally consistent qualities of character. Just as in the other domains of learning, the validity and reliability of these assessments need to be ensured. The assessment of the affective domain requires careful observation of specified behaviours by multiple observers. Miller’s pyramid, Krathwohl’s levels and Epstein’s conceptual frameworks can be used to devise suitable assessment plans for the complex and varied competencies in this domain. Many techniques including paper-based tests, self-administered rating scale, faculty and peer assessment, simulations, reflections, portfolios, observed clinical encounters, collated views of co-workers, record of incidents of unprofessionalism, critical incident reports, patient surveys and global view of supervisor have been used for assessment of affective domain. The effective utilization of these assessment tools needs to factor in the ground realities that exist in different medical colleges. The acceptability and feasibility of using resource intensive assessment methods needs to be carefully balanced against their contributions towards increasing validity and reliability

Evaluation of a method to measure the ratio of pelvic limb to thoracic limb girth in dogs

Objective To determine if a landmark-incorporated limb girth measurement in sound dogs, utilising the greater trochanter and the acromion process, would be both reliable and have a consistent ratio between pelvic limb (PL) and thoracic limb (TL) muscle measurements. Background To establish a reliable and reproducible reference range for sound dogs. This reference range may help clinicians further evaluate dogs during lameness and musculoskeletal examinations. Evidentiary value Prospective study of 115 sound dogs measured by a single observer. Methods Examinations were performed by one observer, using a Gullick II tape measure for PL to TL ratio (PL:TL) measurements for 115 dogs. Bodyweight, breed, body condition score (BCS), and sex were recorded. Each limb was measured three times. The average PL:TL per dog was calculated. Further statistical analysis was used to calculate intra-observer variance and the correlation of limb girth to body weight, BCS, and sex, with significance set at P &lt; 0.05. Results The average PL:TL of the sample was 1.515 ± 0.049. Fifty-two dogs of the 115 cases (45%) had a PL:TL ratio of 1.500. PL:TL measurements were not related to dog weight, BCS, or sex. The intra-class correlation was reported to be 0.99. Conclusion Our study suggests that a landmark-incorporated measurement in a weight-bearing position can be reproducible. Further investigation is required to determine if this measurement can be reproducible between multiple observers. Application A landmark-incorporated limb girth measurement may guide clinicians in case progression and help pinpoint subclinical musculoskeletal disease in dogs.

Quantifying innervation facilitated by deep learning in wound healing

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R2 = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.

Hybrid mark–resight approaches account for heterogeneous detection bias in aerial surveys

Abstract Population abundance estimation is a critical yet challenging task for wildlife management decision‐making. For aerial surveys of terrestrial mammals in highly heterogenous landscapes, estimation models that address multiple sources of detection bias are needed. A typical hybrid mark–resight model includes an additive parameter that accounts for detection probabilities derived from marked animals. While this additive hybrid method improves abundance estimates, there remains a need to account for heterogeneity due to the conditional dependence in detection probability inherent in aerial surveys with multiple observers. We propose a modified approach, referred to as a conditional hybrid model, to address this heterogeneity directly. The conditional hybrid model applies a mark–resight approach typically used to account for tag loss in mark–recapture studies to aerial surveys with a subset of the population marked with GPS collars. We conduct a structured comparison between the additive and conditional hybrid estimation models and fit these two hybrid models to data from a 2020 aerial survey of mule deer in southern California. Both the additive and conditional hybrid models had general agreement in overall model structure and variable importance, however, the conditional model outperformed the additive model in capturing sources of detection heterogeneity and overall abundance estimates differed by 9%. Our top‐ranked models suggest that deer movement, larger group sizes, and rougher topography increased detection probability, whereas high cloud cover, obscuring vegetation and high observer fatigue and helicopter height decreased detection probability. Our findings illustrate the need to assess multiple correction methods when estimating abundance from aerial survey data to ensure accurate abundance estimates are being used to inform wildlife management strategies, with the caveat that specific survey conditions may dictate which method is the most appropriate for achieving robust estimates. These approaches represent the cutting‐edge science on wildlife abundance estimation from aerial survey data, a critical ingredient for effective wildlife management. By testing innovative hybrid methods, we present the first formal comparison of the strengths and weaknesses of each method within a highly heterogeneous landscape, providing practitioners with necessary information for future application.