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Nevin Manimala Statistics

Location selection for offshore wind power station using interval-valued intuitionistic fuzzy distance measure-RANCOM-WISP method

Sci Rep. 2024 Feb 27;14(1):4706. doi: 10.1038/s41598-024-54929-6.

ABSTRACT

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment.

PMID:38409321 | DOI:10.1038/s41598-024-54929-6

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Nevin Manimala Statistics

Estimation of footprints of the canine stifle ligaments using deformable shape templates of bones

Sci Rep. 2024 Feb 26;14(1):4639. doi: 10.1038/s41598-024-55116-3.

ABSTRACT

Knowledge regarding the ligament footprints in the canine stifle is essential for biomechanical modeling of the joint and patient-specific surgical planning for anatomical ligament reconstruction. The present study aimed to establish and evaluate deformable shape templates (DSTs) of the femur and tibia with footprints of the cruciate and collateral ligaments embedded for the noninvasive estimation of ligament footprint positions. To this end, a data set of computed tomography (CT)-derived surface models of the femur and tibia were established and used to build statistical shape models (SSMs). The contours of the stifle ligaments were obtained from CT scans of 27 hindlimb specimens with radio-opaque markings on the ligament footprints. The DST, constructed by embedding averaged footprint contours into the SSM, was used to estimate subject-specific ligament footprints in a leave-one-out cross-validation framework. The DST predictions were compared with those derived from radio-opaque-marked footprints. The results showed that the averaged Euclidean distances between the estimated and reference footprint centroids were less than 1.2 mm for the cruciate ligaments and 2.0 mm for the collateral ligaments. The DST appeared to provide a feasible alternative approach for noninvasively estimating the footprints of the stifle ligaments in vivo.

PMID:38409316 | DOI:10.1038/s41598-024-55116-3

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Nevin Manimala Statistics

Efficacy and safety of a submucosal injection solution of sodium alginate for endoscopic resection in a porcine model

Sci Rep. 2024 Feb 26;14(1):4592. doi: 10.1038/s41598-024-55226-y.

ABSTRACT

Endoscopic resection techniques require the use of submucosal injection. Normal saline and sodium hyaluronate solutions are mainly used for this purpose, but an ideal solution has not yet been developed. The aim of this study was to assess a new solution, MC-003-a novel submucosal injection solution developed with sodium alginate as the main ingredient. Normal saline, a commercial sodium hyaluronate solution (Endo-Ease), and MC-003 were examined. A total of 18 gastric submucosal cushions were created in the stomachs of six pigs. The height of mucosal elevation was measured sequentially using endoscopic sonography. After euthanizing the animals either 2 h or 5 days after the procedure, pathologic examination was performed for each injection site. Although not statistically significant over the entire study period, MC-003 showed a superior result to normal saline and an equivalent result to Endo-Ease in the submucosal cushion height and its rate of decrease. There were no adverse outcomes after injection of the three solutions and there was no pathologically identified detrimental change in the resected specimens. MC-003 creates a sufficient submucosal fluid cushion without apparent tissue damage. It can be considered as an effective submucosal injection material.

PMID:38409310 | DOI:10.1038/s41598-024-55226-y

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Nevin Manimala Statistics

Core network traffic prediction based on vertical federated learning and split learning

Sci Rep. 2024 Feb 26;14(1):4663. doi: 10.1038/s41598-024-53193-y.

ABSTRACT

Wireless traffic prediction is vital for intelligent cellular network operations, such as load-aware resource management and predictive control. Traditional centralized training addresses this but poses issues like excessive data transmission, disregarding delays, and user privacy. Traditional federated learning methods can meet the requirement of jointly training models while protecting the privacy of all parties’ data. However, challenges arise when the local data features among participating parties exhibit inconsistency, making the training process difficult to sustain. Our study introduces an innovative framework for wireless traffic prediction based on split learning (SL) and vertical federated learning. Multiple edge clients collaboratively train high-quality prediction models by utilizing diverse traffic data while maintaining the confidentiality of raw data locally. Each participant individually trains dimension-specific prediction models with their respective data, and the outcomes are aggregated through collaboration. A partially global model is formed and shared among clients to address statistical heterogeneity in distributed machine learning. Extensive experiments on real-world datasets demonstrate our method’s superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting.

PMID:38409301 | DOI:10.1038/s41598-024-53193-y

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Nevin Manimala Statistics

The value of C-reactive protein, leucocytes and vital signs in detecting major complications after oncological colorectal surgery

Langenbecks Arch Surg. 2024 Feb 27;409(1):76. doi: 10.1007/s00423-024-03266-3.

ABSTRACT

PURPOSE: To assess the association of postoperative C-reactive protein (CRP), leucocytes and vital signs in the first three postoperative days (PODs) with major complications after oncological colorectal resections in a tertiary referral centre for colorectal cancer in The Netherlands.

METHODS: A retrospective cohort study, including 594 consecutive patients who underwent an oncological colorectal resection at Maastricht University Medical Centre between January 2016 and December 2020. Descriptive analyses of patient characteristics were performed. Logistic regression models were used to assess associations of leucocytes, CRP and Modified Early Warning Score (MEWS) at PODs 1-3 with major complications. Receiver operating characteristic curve analyses were used to establish cut-off values for CRP.

RESULTS: A total of 364 (61.3%) patients have recovered without any postoperative complications, 134 (22.6%) patients have encountered minor complications and 96 (16.2%) developed major complications. CRP levels reached their peak on POD 2, with a mean value of 155 mg/L. This peak was significantly higher in patients with more advanced stages of disease and patients undergoing open procedures, regardless of complications. A cut-off value of 170 mg/L was established for CRP on POD 2 and 152 mg/L on POD 3. Leucocytes and MEWS also demonstrated a peak on POD 2 for patients with major complications.

CONCLUSIONS: Statistically significant associations were found for CRP, Δ CRP, Δ leucocytes and MEWS with major complications on POD 2. Patients with CRP levels ≥ 170 mg/L on POD 2 should be carefully evaluated, as this may indicate an increased risk of developing major complications.

PMID:38409295 | DOI:10.1007/s00423-024-03266-3

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Nevin Manimala Statistics

First isolation and genotyping of pathogenic Leptospira spp. from Austria

Sci Rep. 2024 Feb 26;14(1):4467. doi: 10.1038/s41598-024-53775-w.

ABSTRACT

Leptospirosis is a globally distributed zoonotic disease. The standard serological test, known as Microscopic Agglutination Test (MAT), requires the use of live Leptospira strains. To enhance its sensitivity and specificity, the usage of locally circulating strains is recommended. However, to date, no local strain is available from Austria. This study aimed to isolate circulating Leptospira strains from cattle in Austria to enhance the performances of the routine serological test for both humans and animals. We used a statistical approach combined with a comprehensive literature search to profile cattle with greater risk of leptospirosis infection and implemented a targeted sampling between November 2021 and October 2022. Urine and/or kidney tissue were sampled from 410 cattle considered at higher risk of infection. Samples were inoculated into EMJH-STAFF culture media within 2-6 h and a real-time PCR targeting the lipL32 gene was used to confirm the presence/absence of pathogenic Leptospira in each sample. Isolates were further characterised by core genome multilocus sequence typing (cgMLST). Nine out of 429 samples tested positive by PCR, from which three isolates were successfully cultured and identified as Leptospira borgpetersenii serogroup Sejroe serovar Hardjobovis, cgMLST cluster 40. This is the first report on the isolation and genotyping of local zoonotic Leptospira in Austria, which holds the potential for a significant improvement in diagnostic performance in the country. Although the local strain was identified as a cattle-adapted serovar, it possesses significant zoonotic implications. Furthermore, this study contributes to a better understanding of the epidemiology of leptospirosis in Europe.

PMID:38409294 | DOI:10.1038/s41598-024-53775-w

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Nevin Manimala Statistics

Fully automated deep learning based auto-contouring of liver segments and spleen on contrast-enhanced CT images

Sci Rep. 2024 Feb 26;14(1):4678. doi: 10.1038/s41598-024-53997-y.

ABSTRACT

Manual delineation of liver segments on computed tomography (CT) images for primary/secondary liver cancer (LC) patients is time-intensive and prone to inter/intra-observer variability. Therefore, we developed a deep-learning-based model to auto-contour liver segments and spleen on contrast-enhanced CT (CECT) images. We trained two models using 3d patch-based attention U-Net ([Formula: see text] and 3d full resolution of nnU-Net ([Formula: see text] to determine the best architecture ([Formula: see text]. BA was used with vessels ([Formula: see text] and spleen ([Formula: see text] to assess the impact on segment contouring. Models were trained, validated, and tested on 160 ([Formula: see text]), 40 ([Formula: see text]), 33 ([Formula: see text]), 25 (CCH) and 20 (CPVE) CECT of LC patients. [Formula: see text] outperformed [Formula: see text] across all segments with median differences in Dice similarity coefficients (DSC) ranging 0.03-0.05 (p < 0.05). [Formula: see text], and [Formula: see text] were not statistically different (p > 0.05), however, both were slightly better than [Formula: see text] by DSC up to 0.02. The final model, [Formula: see text], showed a mean DSC of 0.89, 0.82, 0.88, 0.87, 0.96, and 0.95 for segments 1, 2, 3, 4, 5-8, and spleen, respectively on entire test sets. Qualitatively, more than 85% of cases showed a Likert score [Formula: see text] 3 on test sets. Our final model provides clinically acceptable contours of liver segments and spleen which are usable in treatment planning.

PMID:38409252 | DOI:10.1038/s41598-024-53997-y

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Nevin Manimala Statistics

Mathematical modeling of cholera dynamics with intrinsic growth considering constant interventions

Sci Rep. 2024 Feb 26;14(1):4616. doi: 10.1038/s41598-024-55240-0.

ABSTRACT

A mathematical model that describes the dynamics of bacterium vibrio cholera within a fixed population considering intrinsic bacteria growth, therapeutic treatment, sanitation and vaccination rates is developed. The developed mathematical model is validated against real cholera data. A sensitivity analysis of some of the model parameters is also conducted. The intervention rates are found to be very important parameters in reducing the values of the basic reproduction number. The existence and stability of equilibrium solutions to the mathematical model are also carried out using analytical methods. The effect of some model parameters on the stability of equilibrium solutions, number of infected individuals, number of susceptible individuals and bacteria density is rigorously analyzed. One very important finding of this research work is that keeping the vaccination rate fixed and varying the treatment and sanitation rates provide a rapid decline of infection. The fourth order Runge-Kutta numerical scheme is implemented in MATLAB to generate the numerical solutions.

PMID:38409239 | DOI:10.1038/s41598-024-55240-0

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Nevin Manimala Statistics

Integration of B-to-B trade network models of structural evolution and monetary flows reproducing all major empirical laws

Sci Rep. 2024 Feb 26;14(1):4628. doi: 10.1038/s41598-024-54719-0.

ABSTRACT

We develop a single two-layered model framework that captures and replicates both the statistical properties of the network as well as those of the intrinsic quantities of the agents. Our model framework consists of two distinct yet connected elements that were previously only studied in isolation, namely methods related to temporal network structures and those associated with money transport flows. Within this context, the network structure emerges from the first layer and its topological structure is transferred to the second layer associated with the money transactions. In this manner, we can explain how the micro-level dynamics of the agents within the network lead to the exogenous manifestation of the aggregated system statistical data en-wrapping the very same agents within the system. This is done by capturing the essential dynamics of collective motion in complex networks that enable the simultaneous emergence of tent-shaped distributions in growth rates within the agents, together with the emergence of scaling properties within the network in the study. We can validate the model framework and dynamics by applying these to the context of the real-world inter-firm trading network of firms in Japan and comparing the results of the statistical distributions at both network and agent levels in a temporal manner. In particular, we compare our results to the fundamental quantities supporting the seven empirical laws observed in data: the degree distribution, the mean degree growth rate over time, the age distribution of the firms, the preferential attachment, the sales distribution in steady states, their growth rates, their scaling relations generated by the model. We find these results to be nearly identical to the real-world data. The framework has the potential to be transformed into a forecasting tool to support decision-makers on financial and prudential policies.

PMID:38409204 | DOI:10.1038/s41598-024-54719-0

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Nevin Manimala Statistics

Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer

Sci Rep. 2024 Feb 26;14(1):4684. doi: 10.1038/s41598-024-54533-8.

ABSTRACT

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g. decision-making in uncertain situations) were more important in post-pandemic conditions. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.

PMID:38409195 | DOI:10.1038/s41598-024-54533-8