Applications of distance measure between dual hesitant fuzzy sets in medical diagnosis and weighted dual hesitant fuzzy sets in making decision.

This paper introduces a novel distance measure for dual hesitant fuzzy sets (DHFS) and weighted dual hesitant fuzzy sets (WDHFS), with a rigorous proof of the triangular inequality to ensure its mathematical validity. The proposed measure extends the normalized Hamming, generalized, and Euclidean distance measures to dual hesitant fuzzy elements (DHFE), offering a broader framework for handling uncertainty in fuzzy environments. Additionally, the utilization of a score function is shown to simplify the computation of these distance measures. The practical relevance of the proposed measure is demonstrated through its application in medical diagnosis and decision-making processes. A comparative analysis between the newly introduced distance measure denoted as χ , and an existing measure, χ 1 is performed to underscore the superiority and potential advantages of the new approach in real-world scenarios.

Gravitational redshift revisited: Inertia, geometry, and charge.

Gravitational redshift effects undoubtedly exist; moreover, the experimental setups which confirm the existence of these effects-the most famous of which being the Pound-Rebka experiment-are extremely well-known. Nonetheless-and perhaps surprisingly-there remains a great deal of confusion in the literature regarding what these experiments really establish. Our goal in the present article is to clarify these issues, in three concrete ways. First, although (i) Brown and Read (2016) are correct to point out that, given their sensitivity, the outcomes of experimental setups such as the original Pound-Rebka configuration can be accounted for using solely the machinery of accelerating frames in special relativity (barring some subtleties due to the Rindler spacetime necessary to model the effects rigorously), nevertheless (ii) an explanation of the results of more sensitive gravitational redshift outcomes does in fact require more. Second, although typically this 'more' is understood as the invocation of spacetime curvature within the framework of general relativity, in light of the so-called 'geometric trinity' of gravitational theories, in fact curvature is not necessary to explain even these results. Thus (a) one can often explain the results of these experiments using only the resources of special relativity, and (b) even when one cannot, one need not invoke spacetime curvature. And third: while one might think that the absence of gravitational redshift effects would imply that spacetime is flat (indeed, Minkowskian), this can be called into question given the possibility of the cancelling of gravitational redshift effects by charge in the context of the Reissner-Nordström metric. This argument is shown to be valid and both attractive forces as well as redshift effects can be effectively shielded (and even be repulsive or blueshifted, respectively) in the charged setting. Thus, it is not the case that the absence of gravitational effects implies a Minkowskian spacetime setting.

A unified multimodal classification framework based on deep metric learning.

Multimodal classification algorithms play an essential role in multimodal machine learning, aiming to categorize distinct data points by analyzing data characteristics from multiple modalities. Extensive research has been conducted on distilling multimodal attributes and devising specialized fusion strategies for targeted classification tasks. Nevertheless, current algorithms mainly concentrate on a specific classification task and process data about the corresponding modalities. To address these limitations, we propose a unified multimodal classification framework proficient in handling diverse multimodal classification tasks and processing data from disparate modalities. UMCF is task-independent, and its unimodal feature extraction module can be adaptively substituted to accommodate data from diverse modalities. Moreover, we construct the multimodal learning scheme based on deep metric learning to mine latent characteristics within multimodal data. Specifically, we design the metric-based triplet learning to extract the intra-modal relationships within each modality and the contrastive pairwise learning to capture the inter-modal relationships across various modalities. Extensive experiments on two multimodal classification tasks, fake news detection and sentiment analysis, demonstrate that UMCF can extract multimodal data features and achieve superior classification performance than task-specific benchmarks. UMCF outperforms the best fake news detection baselines by 2.3% on average regarding F1 scores.

Technical note: Measuring bicondylar length in computed tomography data.

OBJECTIVES: With the increased use of 3D-generated images in biological research, there is a critical need to adapt classical anatomical measurements, traditionally conducted with calipers, to a virtual environment. We present detailed protocols for measuring bicondylar length, a critical dimension of the femur, using three different imaging software programs-3D Slicer™, Amira™, and Simpleware™. These protocols provide researchers and practitioners in radiology, orthopedics, biomechanics, and biological anthropology with accurate and reproducible measurement techniques. The objective is to standardize and support virtual osteology in biomechanical research, stature estimation, and related medical and anthropological studies. MATERIALS AND METHODS: Adhering to standardized protocols, we adapted femoral bicondylar length measurements for computed tomography images from a New Mexican collection (n = 10). The method was designed for applicability and reproducibility across three software platforms. By comparing measurements from the same sample across different observers and different platforms, this study validates the accuracy and consistency of the adapted protocol, demonstrating its utility for research and clinical assessments. RESULTS: We present a step-by-step guide for each program, detailing bone alignment and measurement. We illustrate each step and provide video tutorials via links for an enhanced understanding of the process. DISCUSSION: Bicondylar length can be measured effectively in each software program following the provided instructions. However, ease of measurement varied among the programs, with some offering a more straightforward process. This variability underscores the importance of choosing appropriate software for the user's needs and proficiency. It also suggests areas for improvement and standardization in software design and instructional clarity.

A Coupled Spatial-Network Model: A Mathematical Framework for Applications in Epidemiology.

There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph's edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.

A new metric method for sex estimation using three-dimensional imaging of the nuchal crest.

In Walker's nonmetric method, the nuchal crest serves as the representative region for indicating sexual dimorphism in cranial bones. However, the accuracy of sex estimation using the nuchal crest is lower than that using other anatomical regions. Furthermore, because of the protruding processes and structurally challenging features characterized by uneven and rough surfaces, there is a lack of metric methods for sex estimation, making quantification challenging. In this study, we aimed to validate a derived metric method for sex estimation by reconstructing the nuchal crest region in three-dimensional (3D) images obtained from computed tomography scans of cranial bones and compare its accuracy with that of the nonmetric method. A total of 648 images were collected, with 100 randomly selected for use in the nonmetric method. We applied our metric method to the remaining 548 images. Our findings showed that the surface area of the nuchal crests was greater in male individuals than in female individuals. The nuchal crest surface area quantified by the metric method increased the accuracy of sex estimation by 48% compared with that by the nonmetric method. Our metric method for sex estimation, which quantifies the nuchal crest surface area using 3D images of the skull, led to a high sex estimation accuracy of 93%. Future studies should focus on proposing and quantifying new measurement methods for areas showing sexual characteristics in the skull that are difficult to measure, thereby enhancing the accuracy and reliability of sex estimation in human skeletal identification across various fields.

Sequence-Similar Protein Domain Pairs With Structural or Topological Dissimilarity.

For a variety of applications, protein structures are clustered by sequence similarity, and sequence-redundant structures are disregarded. Sequence-similar chains are likely to have similar structures, but significant structural variation, as measured with RMSD, has been documented for sequence-similar chains and found usually to have a functional explanation. Moving two neighboring stretches of backbone through each other may change the chain topology and alter possible folding paths. The size of this motion is compatible to a variation in a flexible loop. We search and find domains with alternate chain topology in CATH4.2 sequence families relatively independent of sequence identity and of structural similarity as measured by RMSD. Structural, topological, and functional representative sets should therefore keep sequence-similar domains not just with structural variation but also with topological variation. We present BCAlign that finds Alignment and superposition of protein Backbone Curves by optimizing a user chosen convex combination of structural derivation and derivation between the structure-based sequence alignment and an input sequence alignment. Steric and topological obstructions from deforming a curve into an aligned curve are then found by a previously developed algorithm. For highly sequence-similar domains, sequence-based structural alignment better represents the chains motion and generally reveals larger structural and topological variation than structure-based does. Fold-switching protein pairs have been reported to be most frequent between X-ray and NMR structures and estimated to be underrepresented in the PDB as the alternate configuration is harder to resolve. Here we similarly find chain topology most frequently altered between X-ray and NMR structures.

How the Crystal Ball Helps to Beat Ejection Fraction as Predictor of Mortality After Myocardial Infarction.

Ejection fraction (EF) is an incomplete metric and requires consideration of an associated companion (EFC) metric. This figure is based on 96 cardiac patients, including those with acute myocardial infarction (MI). For mid-range EF (with values ranging from 40% to 50%) the brown-colored area indicates the distribution of the EFC for these patients. Only the combination of EF and EFC can define the unique location of each patient. Likewise, data points are spread for any other EF range, for example, those with 55

Personalized Intervention Strategy Based on a Risk Score Generated From Subcutaneous Insertable Cardiac Monitor: Results From Phase 1 of ALLEVIATE-HF.

BACKGROUND: Diagnostic variables from insertable cardiac monitors may be useful in identifying patients at increased risk of heart failure (HF) events. High-risk alerts must be coupled with interventions to improve outcomes. We aim to assess the safety of a predefined protocolized intervention pathway activated by insertable cardiac monitor high-risk alerts. METHODS AND RESULTS: ALLEVIATE-HF (Algorithm Using LINQ Sensors for Evaluation and Treatment of Heart Failure) Phase 1 was a randomized interventional study enrolling patients with New York Heart Association class II/III and a recent HF event. A HF risk score based on insertable cardiac monitor diagnostics, including impedance, respiration rate, atrial fibrillation burden, heart rate during atrial fibrillation, heart rate variability, and activity duration, was calculated. A protocolized intervention pathway was activated when high-risk scores were detected that involved physician-prescribed nurse-implemented uptitration of diuretic for 4 days, unless safety rule-out conditions were met. Interventions could be repeated if high-risk scores persisted and did not require worsening symptoms. In total, 59 patients were randomized (mean age 68.2±11.8 years; 59.3% male); 67.8% with ejection fraction ≥50%. The mean follow-up was 11.8±8.1 months. Overall, 146 high-risk scores were recorded in 33 patients and 118 interventions occurred in 75 (51.4%) high-risk alerts that did not meet safety rule-out criteria. There were no serious adverse events and 13 adverse events related to interventions. In patients with symptoms at intervention initiation, symptoms resolved in 37 interventions (80%) and worsened in 8 (17%). In asymptomatic patients, symptoms developed in 3 interventions (7%). CONCLUSIONS: A personalized medication intervention based on insertable cardiac monitor risk score can be safely instituted in patients with HF, irrespective of symptoms. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04452149.

Application of Kolmogorov-Sinai's Metric Entropy for the Analysis of Mechanical Properties in the Bending Test of Epoxy-Rubber-Glass Composites.

The article presents an analysis of the results obtained during the three-point bending test for seven variants of epoxy rubber-glass composites manufactured according to innovative technology. Different contents of rubber recyclate (3, 5, and 7%) and different methods of distribution of the recyclate in the composite structure (1, 2, and 3 layers with a constant share of 5% of the recyclate) were used in the tested materials. To determine the stress values at which critical failures of the tested materials are initiated in the bending test, an analysis was carried out using the Kolmogorov-Sinai (EK-S) metric entropy calculations. The analysis results showed that for each of the above-mentioned variants of the tested epoxy-glass composites, the onset of critical changes occurring in the material structure occurs below the recorded values of the flexural strength Rmg. The decrease in the RmgK-S value in relation to Rmg is different for different material variants and depends mainly on the % content of rubber recyclate and the amount and method of decomposition of rubber recyclate in the layers of the analyzed materials. The research showed that the introduction of rubber recyclate into the composition of composites has a positive effect on their strength properties. This process allows for the efficient use of hard to degrade waste and opens up the possibility of using the newly developed materials in many industrial sectors.

Investigating fractal fractional PDEs, electric circuits, and integral inclusions via (ψ,ϕ)-rational type contractions.

The fixed point theory has been generalized mainly in two directions. One is the extension of the spaces, and the other is relaxing and generalizing the contractions. This paper aims to establish novel fixed point results of rational type generalized ( ψ , ϕ ) -contractions in the context of extended b-metric spaces. This will allow us to analyze generalized rational type contraction in a more relaxed and diversified framework in the light of the characteristics of ( ψ , ϕ ) . Some existing rational-type contractions have been recalled in this direction, and others are defined. New fixed point results have been established by utilizing the properties of ψ and ϕ and applying the iteration technique. Moreover, the established results are employed to investigate the stability of fractal and fractional differential equations and electric circuits. For the reliability of the established results, examples and applications to the system of integral inclusions and system of integral equations are presented.

Morphometric analysis of the hard palate in sex estimation among koreans using three-dimensional computed tomography.

Accurate sex estimation is crucial for comprehensive analysis of the biological profiles of unidentified human skeletal remains. However, there is a notable lack of research specifically addressing the morphometrics of the hard palate. Therefore, this study aimed to derive discriminant equations using the hard palate and assess their applicability for sexing partial skeletal remains in a contemporary Korean population. Statistical analyses were performed for 24 measurements derived from three-dimensional models of the hard palate, generated using computed tomography scans of 301 individuals (156 males, 145 females). Descriptive statistics revealed significant sexual dimorphism in the mean comparison of hard palate sizes between Korean males and females, with males exhibiting larger palates across all measurements (p < 0.05). Discriminant function score equations were generated to aid in sex determination. Univariate analysis yielded an accuracy range of 57.8-75.1%, whereas the stepwise method achieved an accuracy of 80.7% with five selected variables: IF-PNS, GFL-GFR, IF-GFR, Pr-EcL, and Pr-EnR. The results of this metric analysis demonstrate the usefulness of the hard palate for sex estimation in the contemporary Korean population. These findings have potential implications for forensic investigations, archeological studies, and population-specific anatomical research.

Log-Cholesky filtering of diffusion tensor fields: Impact on noise reduction.

Diffusion tensor imaging (DTI) is a powerful neuroimaging technique that provides valuable insights into the microstructure and connectivity of the brain. By measuring the diffusion of water molecules along neuronal fibers, DTI allows the visualization and study of intricate networks of neural pathways. DTI is a noise-sensitive method, where a low signal-to-noise ratio (SNR) results in significant errors in the estimated tensor field. Tensor field regularization is an effective solution for noise reduction. Diffusion tensors are represented by symmetric positive-definite (SPD) matrices. The space of SPD matrices may be viewed as a Riemannian manifold after defining a suitable metric on its tangent bundle. The Log-Cholesky metric is a recently developed concept with advantages over previously defined Riemannian metrics, such as the affine-invariant and Log-Euclidean metrics. The utility of the Log-Cholesky metric for tensor field regularization and noise reduction has not been investigated in detail. This manuscript provides a quantitative investigation of the impact of Log-Cholesky filtering on noise reduction in DTI. It also provides sufficient details of the linear algebra and abstract differential geometry concepts necessary to implement this technique as a simple and effective solution to filtering diffusion tensor fields.

Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs.

Patients who are undergoing physical rehabilitation, benefit from feedback that follows from reliable assessment of their cumulative performance attained at a given time. In this paper, we provide a method for the learning of the recovery trajectory of an individual patient, as they undertake exercises as part of their physical therapy towards recovery of their loss of movement ability, following a critical illness. The difference between the Movement Recovery Scores (MRSs) attained by a patient, when undertaking a given exercise routine on successive instances, is given by a statistical distance/divergence between the (posterior) probabilities of random graphs that are Bayesianly learnt using time series data on locations of 20 of the patient's joints, recorded on an e-platform as the patient exercises. This allows for the computation of the MRS on every occasion the patient undertakes this exercise, using which, the recovery trajectory is drawn. We learn each graph as a Random Geometric Graph drawn in a probabilistic metric space, and identify the closed-form marginal posterior of any edge of the graph, given the correlation structure of the multivariate time series data on joint locations. On the basis of our recovery learning, we offer recommendations on the optimal exercise routines for patients with given level of mobility impairment.

Metric learning guided sinogram denoising for cone beam CT enhancement.

BACKGROUND: Cone beam computed tomography (CBCT) is a widely available modality, but its clinical utility has been limited by low detail conspicuity and quantitative accuracy. Convenient post-reconstruction denoising is subject to back projected patterned residual, but joint denoise-reconstruction is typically computationally expensive and complex. PURPOSE: In this study, we develop and evaluate a novel Metric-learning guided wavelet transform reconstruction (MEGATRON) approach to enhance image domain quality with projection-domain processing. METHODS: Projection domain based processing has the benefit of being simple, efficient, and compatible with various reconstruction toolkit and vendor platforms. However, they also typically show inferior performance in the final reconstructed image, because the denoising goals in projection and image domains do not necessarily align. Motivated by these observations, this work aims to translate the demand for quality enhancement from the quantitative image domain to the more easily operable projection domain. Specifically, the proposed paradigm consists of a metric learning module and a denoising network module. Via metric learning, enhancement objectives on the wavelet encoded sinogram domain data are defined to reflect post-reconstruction image discrepancy. The denoising network maps measured cone-beam projection to its enhanced version, driven by the learnt objective. In doing so, the denoiser operates in the convenient sinogram to sinogram fashion but reflects improvement in reconstructed image as the final goal. Implementation-wise, metric learning was formalized as optimizing the weighted fitting of wavelet subbands, and a res-Unet, which is a Unet structure with residual blocks, was used for denoising. To access quantitative reference, cone-beam projections were simulated using the X-ray based Cancer Imaging Simulation Toolkit (XCIST). In both learning modules, a data set of 123 human thoraxes, which was from Open-Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression challenge, was used. Reconstructed CBCT thoracic images were compared against ground truth FB and performance was assessed in root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). RESULTS: MEGATRON achieved RMSE in HU value, PSNR, and SSIM were 30.97 ± 4.25, 37.45 ± 1.78, and 93.23 ± 1.62, respectively. These values are on par with reported results from sophisticated physics-driven CBCT enhancement, demonstrating promise and utility of the proposed MEGATRON method. CONCLUSION: We have demonstrated that incorporating the proposed metric learning into sinogram denoising introduces awareness of reconstruction goal and improves final quantitative performance. The proposed approach is compatible with a wide range of denoiser network structures and reconstruction modules, to suit customized need or further improve performance.

Tackling the elephant in the room - Large-scale salmon farming and the potential for far-field ecosystem effects.

Significant expansion in salmon production globally has been partially enabled through the establishment of large-capacity sea-farms in high-energy environments that collectively produce substantial quantities of organic waste with potential to cause regional scale environmental degradation. We analyse results from comprehensive spatial and temporal surveys of water column particulates and seabed environmental indicators for responses to farm production, and residual effects. Results confirmed that while the particles can and do reach a relatively wide area, benthic effects do not necessarily follow suit. There was limited evidence of longer-term environmental degradation at some near-field locations and spatially removed deeper sites. We concluded that evidence for regional biological effects was negligible, suggesting: i) modern waste tracing techniques are more sensitive than traditional effects indicators, and ii) waste fluxes in the far-field were being assimilated without causing environmental perturbation. Monitoring at potential accumulation points, especially for sites with complex bathymetry and hydrodynamics is advised.

Evaluation of Pharmacist-led Telehealth Medicare Annual Wellness Visits to Satisfy Quality Metrics and USPSTF Recommendations After Implementation of a Post-Visit Follow-Up Protocol.

Background: A Medicare Annual Wellness Visit (MAWV) serves Medicare patients by identifying and addressing gaps in preventive services and health screenings, often aligning with outpatient practice quality metrics. Objective: Evaluate an existing pharmacist-led MAWV telehealth service, determine the baseline quality metric satisfaction rate of telehealth MAWVs, and assess for improvement after implementing a post-MAWV follow-up protocol at a suburban, lower-income primary care clinic. Methods: This IRB-exempt, single-center retrospective chart review utilized the electronic health record at Christ Health Center, Birmingham, AL. From August 2020 through May 2022, 288 charts were assessed between 2 retrospective chart reviews that included patients 18 years or older with Medicare insurance and the ability to conduct a telehealth MAWV. The first chart review assessed metric and recommendation satisfaction within 12 months of the visit. The second chart review was performed after follow-up protocol implementation to assess for additional improvement within 3 months of the visit. Results: The percentage of MAWV recommendations completed groups after implementing a follow-up protocol. For the first chart review, 57.1% of the assessed Health Resources and Services Administration (HRSA), Uniform Data System (UDS) quality metrics, and Centers for Medicare and Medicaid Services (CMS)-required MAWV components were satisfied from the first chart review compared to 53.3% of satisfied quality metrics post-protocol implementation in spite of a substantially shorter follow-up timeframe. Conclusion: Telehealth MAWVs improve preventive care and quality metric satisfaction for Medicare patients. Post-visit follow-up protocols enhance satisfaction rates. Pharmacist-led MAWVs foster interprofessional collaboration and comprehensive patient care.

Image tampering detection based on RDS-YOLOv5 feature enhancement transformation.

Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered images, and a nonlinear loss metric of aspect ratio was introduced into the original SIOU loss function to better optimize the training process of the model. Finally, RDS-YOLOv5 is trained by combining the features of the original image and the enhancement image to improve the robustness of the detection model. A total of 6187 images containing three forms of tampering: splice, remove, and copy-move were used to comprehensively evaluate the proposed algorithm. In ablation test, compared with the original YOLOv5 model, RDS-YOLOv5 achieved a performance improvement of 6.46%, 5.13%, and 3.15% on F1-Score, mAP50 and mAP95, respectively. In comparative experiments, using SRIOU as the loss function significantly improved the model's ability to search for the real tampered regions by 2.54%. And the RDS-YOLOv5 model trained by the fusion dataset further improved the comprehensive detection performance by about 1%.

A novel metastatic tumor segmentation method with a new evaluation metric in clinic study.

Background: Brain metastases are the most common brain malignancies. Automatic detection and segmentation of brain metastases provide significant assistance for radiologists in discovering the location of the lesion and making accurate clinical decisions on brain tumor type for precise treatment. Objectives: However, due to the small size of the brain metastases, existing brain metastases segmentation produces unsatisfactory results and has not been evaluated on clinic datasets. Methodology: In this work, we propose a new metastasis segmentation method DRAU-Net, which integrates a new attention mechanism multi-branch weighted attention module and DResConv module, making the extraction of tumor boundaries more complete. To enhance the evaluation of both the segmentation quality and the number of targets, we propose a novel medical image segmentation evaluation metric: multi-objective segmentation integrity metric, which effectively improves the evaluation results on multiple brain metastases with small size. Results: Experimental results evaluated on the BraTS2023 dataset and collected clinical data show that the proposed method has achieved excellent performance with an average dice coefficient of 0.6858 and multi-objective segmentation integrity metric of 0.5582. Conclusion: Compared with other methods, our proposed method achieved the best performance in the task of segmenting metastatic tumors.