Search Results (65)

We develop a convergence framework for Grünwald–Letnikov (GL) fractional and classical integer difference operators acting on sequences in fuzzy-paranormed (fp) spaces, motivated by data that are imprecise and contain sporadic outliers. Fuzzy paranorms provide a resolution-dependent notion of proximity, while statistical and lacunary statistical convergence downweight sparse deviations by natural density; together, they yield robust criteria for difference-filtered signals. Within this setting, we establish uniqueness of fp–${\mathsf{\Delta }}^m$ statistical limits; an equivalence between fp-statistical convergence of ${\mathsf{\Delta }}^m$ (and its GL extension ${\mathsf{\Delta }}^{\alpha }$) and fp-strong p-Cesàro summability; an equivalence between lacunary fp-${\mathsf{\Delta }}^m$ statistical convergence and blockwise strong p-Cesàro summability; and a density-based decomposition into a classically convergent part plus an fp-null remainder. We also show that GL binomial weights act as an ${\ell }^1$ convolution, ensuring continuity of ${\mathsf{\Delta }}^{\alpha }$ in the fp topology, and that nabla/delta forms are transferred by the discrete Q–operator. The usefulness of the criteria is illustrated on simple engineering-style examples (e.g., relaxation with memory, damped oscillations with bursts), where the fp-Cesàro decay of difference residuals serves as a practical diagnostic for Cesàro compliance. Beyond illustrative mathematics, we report engineering-style diagnostics where the fuzzy Cesàro residual index correlates with measurable quantities (e.g., vibration amplitude and energy surrogates) under impulsive disturbances and missing data. We also calibrate a global decision threshold ${\tau }_{\mathrm{glob}}$ via sensitivity analysis across $(\alpha ,p,m)$, where $m\in \mathbb{N}$ is the integer difference order, $\alpha >0$ is the fractional order, and $p\ge 1$ is the Cesàro exponent, and provide quantitative baselines (median/M-estimators, ${\ell }_1$ trend filtering, Gaussian Kalman filtering, and an $\alpha$-stable filtering structure) to show complementary gains under bursty regimes. The results are stated for integer m and lifted to fractional orders $\alpha >0$ through the same binomial structure and duality. Full article

Portfolio optimization is a multifaceted process aimed at achieving a balance between investors’ risk tolerance and expected returns. However, the inherent uncertainty and unpredictability of financial markets significantly hinder the attainment of this balance. Therefore, there is an increasing need for models capable of representing these uncertainties in a more realistic manner. In this study, novel intuitionistic fuzzy mathematical models are proposed to provide alternative portfolio options that align with diverse investor expectations and risk perceptions. By utilizing mathematical programming formulations incorporating intuitionistic fuzzy parameters, the study contributes to the theoretical framework and enables the analysis of portfolio structures that vary in response to imprecisely defined return levels. The intuitionistic fuzzy parameters are modeled using appropriate membership and non-membership functions, and mean absolute deviation is employed as the risk measure within the proposed models. Various alternative solutions are generated by considering different lower and upper bound constraints, thereby allowing for the construction of flexible investment strategies suitable for different investor profiles. The practical applicability of the proposed models is demonstrated using real-world stock data obtained from Borsa Istanbul. The empirical results reveal that the models provide solutions that are sensitive to individual risk preferences and adaptable to changing market conditions. Accordingly, the developed intuitionistic fuzzy models serve as effective tools for determining optimal portfolio allocations and developing resilient investment strategies. Full article

Background/Objectives: Despite advances in minimally invasive procedures, anastomotic leakages (ALs) after esophageal resections mark the most feared complication. Its early detection can lead to quick interventional treatment with improved survival. Nonetheless, early detection remains challenging, and scores are imprecise and complex. Methods: In our study we analyzed mediastinal drainage fluid to find parameters suggesting AL even before it became clinically evident and correlated them to routine biomarkers. All patients with AL after robotically assisted esophageal resections were included and matched 1:1 with uneventful controls. Additionally, transhiatal distal esophageal resections operated during this period were included. Drainage fluid was collected on postoperative days (PODs) 1–4 with consecutive blood gas analysis. Test quality was determined by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: In total, 40 patients were included, with 17 developing AL. There were no significant differences in gender, age, BMI or oncological treatment. The 30-day morbidity rate was 65.0%. The study was restricted to events in the first 12 days. While lactate value in drainage fluid differed significantly from POD 3 onwards in the two groups, serum CRP remained without significant differences. We developed the LacCRP score (CRP/30 + lactate/2). The AUC on POD 3 was 0.96, with a sensitivity and specificity of 100% and 75%, respectively. An estimator of 1.08 was found in multivariate analysis: one-point increase in the LacCRP score increases AL probability by 8%. Conclusions: This study demonstrates that postoperative lactate determinations in drainage fluid can predict AL after esophageal resection, and its combination with serum CRP results in a reliable LacCRP score. Full article

Background and Objectives: Cerebral microbleeds (CMBs) are increasingly being considered as potential biomarkers of small vessel disease and cerebral vulnerability, particularly in patients with acute ischemic stroke (AIS). Accurate detection is crucial for prognosis and therapeutic decision-making, yet the relative utility of susceptibility-weighted imaging (SWI) versus T2*-weighted imaging (T2*) remains uncertain. Materials and Methods: We conducted a systematic review and meta-analysis (SPOT-CMB, Susceptibility-weighted imaging and Prognostic Outcomes in Acute Stroke—Cerebral Microbleeds study) of 80 studies involving 28,383 AIS patients. Pooled prevalence of CMBs was estimated across imaging modalities (SWI, T2*, and both), and stratified analyses examined variation by demographic, clinical, and imaging parameters. Meta-analytic odds ratios assessed associations between CMB presence and key outcomes: symptomatic intracerebral hemorrhage (sICH), hemorrhagic transformation (HT), and poor functional outcome (modified Rankin Scale score 3–6) at 90 days. Diagnostic performance was assessed using summary receiver operating characteristic curves. Results: Pooled CMB prevalence was higher with SWI (36%; 95% CI 31–41) than T2* (25%; 95% CI 22–28). CMB presence was associated with increased odds of sICH (OR 2.22; 95% CI 1.56–3.16), HT (OR 1.33; 95% CI 1.01–1.75), and poor 90-day outcome (OR 1.61; 95% CI 1.39–1.86). However, prognostic performance was modest, with low sensitivity (e.g., AUC for sICH: 0.29) and low diagnostic odds ratios. SWI outperformed T2* in detection but offered limited prognostic gain. Access to SWI remains limited in many settings, posing challenges for global implementation. Conclusions: SWI detects CMBs more frequently than T2* in AIS patients and shows stronger associations with adverse outcomes, supporting its value for risk stratification. However, prognostic accuracy remains limited, and our GRADE appraisal indicated only moderate certainty for functional outcomes, with lower certainty for diagnostic accuracy due to heterogeneity and imprecision. These findings highlight the clinical utility of SWI but underscore the need for standardized imaging protocols and high-quality prospective studies. Full article

Trapezoidal-valued intuitionistic fuzzy numbers (TrVIFNs) are the real generalizations of intuitionistic fuzzy numbers, interval-valued intuitionistic fuzzy numbers, and triangular intuitionistic fuzzy numbers, which effectively model real-life problems that consist of imprecise and incomplete data. This study incorporates the Aczel-Alsina aggregation operators (which consist of parameter-based flexibility) for solving any group of decision-making problems modeled in a trapezoidal-valued intuitionistic fuzzy (TrVIF) environment. In this study, we first define new operations on TrVIFNs based on the Aczel-Alsina operations. Secondly, we introduce new trapezoidal-valued intuitionistic fuzzy aggregation operators, such as the TrVIF Aczel-Alsina weighted averaging operator, the TrVIF Aczel-Alsina ordered weighted averaging operator, and the TrVIF Aczel-Alsina hybrid averaging operator, and we discuss their fundamental mathematical properties by examining various theorems. This study also includes a new algorithm named ‘three-stage multi-criteria group decision-making’, where we obtain the criteria weights using the newly proposed TrVIF-MEREC method. Additionally, we introduce a new modified algorithm called TrVIF-WASPAS to solve the multi-criteria decision-making (MCDM) problem in the trapezoidal-valued intuitionistic fuzzy environment. Then, we apply this proposed method to solve a model case study problem involving location selection for a wind power plant project. Then, we discuss the proposed algorithm’s sensitivity analysis by changing the criteria weights concerning different parameter values. Finally, we compare our proposed methods with various existing methods, like some subclasses of TrVIFNs such as IVIFWA, IVIFWG, IVIFEWA, and IVIFEWG, and also with some MCGDM methods of TrVIFNs, such as the Dombi aggregation operator-based method in TrVIFNs and the TrVIF-Topsis method-based MCGDM, to show the efficacy of our proposed algorithm. This study has many advantages, as it consists of a total ordering principle in ranking alternatives in the newly proposed TrVIF-MCGDM techniques and TrVIF-WASPAS MCDM techniques for the first time in the literature. Full article

Background: More than three years have elapsed since the onset of the full-scale invasion of Ukraine by the Russian Federation, displacing millions of Ukrainians. While preserving cultural identity in the host country is important for gaining resilience among refugees, we found no studies about how cultural identity influences health care experiences during resettlement. Objective: This study explores how cultural identity shapes health care experiences among Ukrainian refugees during resettlement in the United States. Methods: We conducted an interpretive description study using focus groups to elicit the perspectives of Ukrainian refugees who resettled in North Carolina after 24 February 2022. Twelve Ukrainian women participated in one of four focus groups. Thematic content analysis was employed for case comparison, and themes were inductively derived. Results: Two themes were identified: troubled health care partnerships and imprecise notions of preventive practices. Troubled partnerships represented a lack of trust between refugees and U.S. clinicians and the health care system. Imprecise notions of preventive practices represented mistaken beliefs about prevention. Conclusions: This study adds to the science on refugee health in two ways. First, newly arrived refugees often maintain strong ties to their homeland, which shapes their health care decisions and reinforces their cultural identity. Second, despite being well educated, some refugees held misconceptions about preventive health care, highlighting the need for clinicians to provide clear guidance on primary and secondary prevention practices. Findings may help guide clinicians in delivering culturally sensitive care to refugee populations. Full article

With the large-scale deployment of renewable energy, transmission systems for new energy sources are increasingly exposed to transient overvoltage issues induced by DC blockages and commutation failures. To address the challenges of an imprecise response to multiple fault scenarios and the inefficiency of independent device actions in existing dynamic reactive power control schemes, this paper proposes a coordinated optimization control strategy integrating multiple dynamic reactive power sources tailored to different fault characteristics. An equivalent model of the renewable energy DC transmission system is established to elucidate the underlying mechanisms of transient overvoltage formation under various fault conditions. By employing trajectory sensitivity analysis and parameter perturbation methods, the influence patterns of control parameters on transient overvoltage behaviors across different fault scenarios are quantitatively assessed, thereby overcoming the limitations of traditional empirical parameter tuning approaches. Subsequently, a multi-source coordinated optimization model is developed with the objective of minimizing transient overvoltages under simultaneous dual-fault conditions. A multi-objective particle swarm optimization algorithm, incorporating comprehensive trajectory sensitivity and dynamically adaptive inertia weights, is introduced, alongside Pareto front theory, to achieve rapid and balanced optimization among competing objectives. Simulation results validate that the proposed strategy significantly enhances transient overvoltage suppression across diverse fault conditions. The findings provide robust theoretical foundations and practical guidance for the refined parameter tuning and high-efficiency coordinated control of dynamic reactive power sources in renewable energy transmission systems. Full article

This systematic review and meta-analysis aims to: (1) characterize the distribution of published inhibition of emergence (IE₅₀, IE₉₀, and IE₉₅) reference values for pyriproxyfen and methoprene in Culex pipiens [L.], Aedes aegypti [L.], and Aedes albopictus [Skuse]; (2) generate combined-effect IE values using a DerSimonian and Laird (DL) random-effects model to establish benchmarks for future resistance assessments; and (3) compare these combined-effect IE values with previously published literature. A systematic search was conducted in PubMed, SciELO, J-STAGE, and Google Scholar up to 10 February 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible studies were primary, peer-reviewed literature that aligned with World Health Organization (WHO) protocols for insect growth regulator (IGR) resistance testing, specifically those reporting susceptible reference IE values from continuous immersion dose-response bioassays analyzed using probit regression. A total of 72 unique studies that aligned with WHO protocols were assessed for publication bias using a funnel plot and Egger’s regression. Sensitivity and subgroup analyses were conducted to evaluate individual study contributions to the overall combined effect. Heterogeneity (I²) and combined effect values were estimated for 18 different species/active ingredient/IE concentration subgroup pairings. Heterogeneity (I²) ranged from 29.32 to 99.78% between the 18 subgroups, indicating inconsistency within the literature. The DL combined effect IE₅₀ varied from 0.048 ppb for Cx. pipiens exposed to pyriproxyfen to 1.818 ppb for Ae. albopictus exposed to methoprene. A certainty analysis indicated that 1 combined effect value exhibited high certainty, 8 out of 18 pairings were moderately certain, 6 exhibited low certainty and 3 exhibited very low certainty. The main causes of uncertainty (ranked) were inconsistency between studies, imprecision of the combined effect size, and possible publication bias. Our findings indicate that (1) robust DL combined effect IE₅₀ values could be established for all species/IGR pairings, providing essential benchmarks for future resistance assessments; (2) substantial heterogeneity among susceptible laboratory colonies complicates resistance detection in field-collected mosquitoes; and (3) a significant portion of the literature relies on reference mosquito strains that are likely not fully susceptible, further complicating resistance detection. This study was not registered and was supported by the North Shore Mosquito Abatement District. Full article

The recent improvements in streamflow measurement approaches have boosted the reliability and accuracy of river flow measurement. In this study, long-term measurements of river discharge in the Tokoro River, Japan, were conducted. The key objective of this work is to investigate the extent of river flow measurement in a very shallow and narrow silt stream using the fluvial acoustic tomography system (FAT). Despite the preliminary nature of the measurement results, the recorded data were subject to analysis from three different outlooks. First, examinations were performed under very shallow and high-water conditions. Second, examinations were performed using double acoustic frequency. Third, examinations were performed using multiple independent flow datasets. As a new achievement in terms of advanced monitoring capabilities, it was documented that the measurement by the FAT was possible even in extremely shallow conditions. However, the minimum water depth along the measured cross-section must be ≥9 cm. Moreover, the FAT system demonstrated its capability to monitor streamflow in high water levels. In addition, it was found that using high transmission frequency can provide shorter wavelengths, permitting better spatial resolution and higher velocity resolutions and hence desirable measurement accuracy. Nevertheless, measurements in the presence of high suspended sediment particles were lacking. Alternatively, a lower transmission frequency offers a longer wavelength, which might be less sensitive to small-scale variations and results in an imprecise degree of measurements. Nonetheless, measurements can be accomplished even during the mobilization of a high concentration of suspended sediment matters. Finally, using multiple independent streamflow measurement records, the results proved that the flow measured by the FAT system was in very good agreement with the records acquired using sophisticated measurement approaches such as HADCP and STIV with a very low range of uncertainty. Full article

Background: Quantifying urinary catecholamines and metanephrines is essential for the clinical screening and diagnosis of neuroendocrine tumours. HPLC with electrochemical detection (HPLC-ECD) is commonly used for this type of analysis but requires extensive sample cleanup. Simple and rapid dilute-and-shoot LC–multiple-reaction monitoring (MRM)-MS assays have been developed for quantitating these analytes in urine but have not yet been validated according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Methods: A simple dilute-and-shoot sample preparation without derivatization was used. C18 RP-UPLC-MRM-MS and positive-ion ESI were used, usually with two transitions per analyte being monitored. Certified deuterated internal standards were used for each analyte. Results: This assay was validated according to the CLSI C62-A guidelines, including accuracy/trueness, imprecision, sensitivity, specificity, carryover, stability, and linearity. The final MRM-MS method was compared to the established HPLC-ECD clinical chemistry reference method. The run time was reduced from 25 min to 5 min. Conclusions: A simple, robust, rapid, and cost-effective LC-MRM-MS assay for measuring urinary catecholamines and metanephrines was developed and validated according to the CLSI guidelines. This validated method requires minimal sample manipulation before analysis and provides sensitivity, specificity, and improved precision. The implementation of this assay in clinical laboratories will facilitate early and accurate diagnosis. Full article

This work introduces the mathematical framework of the novel “First-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations” (1st-FASAM-NODE). The 1st-FASAM-NODE methodology produces and computes most efficiently the exact expressions of all of the first-order sensitivities of NODE-decoder responses with respect to the parameters underlying the NODE’s decoder, hidden layers, and encoder, after having optimized the NODE-net to represent the physical system under consideration. Building on the 1st-FASAM-NODE, this work subsequently introduces the mathematical framework of the novel “Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations (2nd-FASAM-NODE)”. The 2nd-FASAM-NODE methodology efficiently computes the exact expressions of the second-order sensitivities of NODE decoder responses with respect to the NODE parameters. Since the physical system modeled by the NODE-net necessarily comprises imprecisely known parameters that stem from measurements and/or computations subject to uncertainties, the availability of the first- and second-order sensitivities of decoder responses to the parameters underlying the NODE-net is essential for performing sensitivity analysis and quantifying the uncertainties induced in the NODE-decoder responses by uncertainties in the underlying uncertain NODE-parameters. Full article

In today’s competitive industrial landscape, Reliability Engineering plays a vital role in minimizing costs and expenses in energy projects. The main focus of this paper is to propose the integration of a fuzzy-based FMECA process into a RAM analysis to assess modernization and reconfiguration strategies for LNG facilities. This approach estimates, through a systematic procedure, the system’s failure probabilities and gauges the impact of various maintenance and topological modification initiatives on the asset and the system’s availability as a driver of profitability. A methodology based on fuzzy-FMEA is proposed to collect and process imprecise data about reliability and maintainability of the components of the facility. Furthermore, Monte Carlo-based RAM experiments are performed. The selection of parameters for conducting Monte Carlo experiments is done after the defuzzification of MTBF and MTTR values defined in the FMEA stage. The proposed procedure allows for the prediction of the system’s reliability across hypothetical scenarios, incorporating design tweaks and potential improvements. As a case study, the proposed was applied to a Pumping and Vaporization facility in a Chilean LNG plant. Sensitivity analysis was performed on critical elements, leading to an optimization strategy for key components like Open Rack Vaporizers (ORV) and Submerged Combustion Vaporizers (SCV). The anticipated availability rate was found to be 99.95% over an 8760 h operating period. Final conclusions and managerial insights are discussed. Full article

Alzheimer’s disease (AD) is a progressive irreversible neurodegenerative disorder that represents a major global public health concern. Traditionally, AD is diagnosed using cerebrospinal fluid biomarker analysis or brain imaging modalities. Recently, less burdensome, more widely available blood biomarker (BBM) assays for amyloid-beta (Aβ42/40) and phosphorylated-tau concentrations have been found to accurately identify the presence/absence of brain amyloid plaques and tau tangles and have helped to streamline AD diagnosis. However, few BBMs have been rigorously analytically validated. Herein, we report the analytical validation of a novel liquid chromatography–tandem mass spectrometry (LC-MS/MS) multiplex method for quantifying plasma phosphorylated-tau217 (p-tau217) and non-phosphorylated-tau217 (np-tau217) peptide concentrations. We combined the p-tau217/np-tau217 concentrations ratio (%p-tau217) and the previously validated LC-MS/MS multiplex assay for plasma Aβ42/40 into a new multianalyte assay with algorithmic analysis (MAAA; PrecivityAD2™ test) that identifies brain amyloid status based on brain amyloid positron emission tomography. We found (a) the %p-tau217 assay is precise, accurate, sensitive, and linear over a wide analytical measurement range, and free from carryover and interference; (b) the pre-analytical specimen collection, processing, storage, and shipping conditions that maintain plasma tau peptide stability; and (c) using the measured analytical imprecision for plasma Aβ42/40 and p-tau217/np-tau217 levels in a worst-case scenario model, the PrecivityAD2 test algorithm for amyloid pathology classification changed for only 3.5% of participants from brain amyloid positive to negative, or from negative to positive. The plasma sample preparation and LC-MS/MS methods underlying the PrecivityAD2 test are suitable for use in the clinical laboratory and valid for the test’s intended purpose: to aid in the diagnostic evaluation of individuals aged 55 and older with signs or symptoms of mild cognitive impairment or dementia. Full article

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “typical-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions. Full article

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of “large-scale computations”. The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties. Full article