Towards the assimilation of tree-ring-width records using ensemble Kalman filtering techniques

Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction

In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement to an existing system without seriously modifying the original CTM, and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM2.5 observations at ground stations were assimilated in this DA system every 6 hours over South Korea for the period of the KORUS–AQ campaign, from 1 May to 12 June, 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA, as well as a run with three-dimensional variational (3DVAR) DA. Consistent improvements due to the ICs assimilated with the EnKF were found in the DA experiments with 6 h interval, compared to the CTR run, and to the run with 3DVAR. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary concentrations (BCs) on the PM2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be about 27.2 % for the 6 h reanalysis run. A series of 24 hour PM2.5 predictions were additionally conducted each day at 00 UTC with the optimized initial concentrations (ICs). The relative reduction of the NMB was 17.3 % for the 24 h prediction run, when the updated ICs were applied at 00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM2.5 predictions in South Korea.

PurposeThis paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.Design/methodology/approachAfter a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.FindingsThe utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.Originality/valueThis study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.

This paper proposes a possibility of developing an axiomatic set theory, as first-order theory within the framework of fuzzy logic in the style of Hajek's Basic fuzzy logic BL. In classical Zermelo-Fraenkel set theory, we use an analogy of the construction of a Boolean-valued universe-over a particular algebra of truth values-we show the nontriviality of our theory. We present a list of problems and research tasks.

Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration

The Kuramoto–Sivashinsky equation plays an important role as a low‐dimensional prototype for complicated fluid dynamics systems having been studied due to its chaotic pattern forming behavior. Up to now, efforts to carry out data assimilation with this 1‐D model were restricted to variational adjoint methods domain and only Chorin and Krause (Proc. Natl. Acad. Sci. 2004; 101(42):15013–15017) tested it using a sequential Bayesian filter approach. In this work we compare three sequential data assimilation methods namely the Kalman filter approach, the sequential Monte Carlo particle filter approach and the maximum likelihood ensemble filter methods. This comparison is to the best of our knowledge novel. We compare in detail their relative performance for both linear and nonlinear observation operators. The results of these sequential data assimilation tests are discussed and conclusions are drawn as to the suitability of these data assimilation methods in the presence of linear and nonlinear observation operators. Copyright © 2009 John Wiley & Sons, Ltd.

Introducing validity in fuzzy probability for judicial decision-making

This study aims to explore the application of fuzzy logic in multi-criteria decision-making (MCDM) processes, particularly in complex environments. A fuzzy logic framework was developed, integrating fuzzy sets, membership functions, and fuzzy rules to evaluate diverse criteria and enhance decision-making. The findings demonstrate that fuzzy logic effectively manages uncertainty and imprecision, leading to improved decision outcomes. A case study conducted in a private hospital in Istanbul validated the framework's applicability, showcasing its ability to handle non-homogeneous information and facilitate informed decisions. The implications of this research highlight the potential of fuzzy logic methodologies to enhance decision-making accuracy and robustness in various fields, including healthcare and engineering.

Fuzzy Logic (Fuzzy Logic) is a branch of science based on thinking like human beings and solving them with mathematical functions. Fuzzy logic theory is a mathematical theory. Based on fuzzy set theory, it also uses intermediate values. The fuzzy logic that emerged in 1965 is used in many fields. In the production of pacemakers, in the production of artificial organs, in many electronic devices, company efficiency estimation, etc. situations are used. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. With the help of machines, people-specific data and experiences are studied using the fuzzy logic approach. In this study, by using Matlab Fuzzy Toolbox, it was aimed to design a system that gives information about the breeding performances of cows. The expert system was designed based on the optimal values under the ideal conditions specified in the literature. The architecture of the system presented in this paper is designed as three input parameters and one output. The designed system was tested with 100 sample values. Afterwards, expert results were evaluated and system decisions were compared. The success of the decision support system was 94%. As a result, the reproductive efficiency of cows can be determined with this designed system. With this determination, the handling or disposal of cows can be determined.

Soft set theory was first introduced by D. A. Molodtsov [D. Molodtsov, Soft set theory first results, Computers & Mathematics with Applications, 37(4–5) (1999) 19–31] in 1999 as an effective approach to deal with uncertainty. Over time, fuzzy soft set theory has evolved significantly, integrating concepts from both fuzzy set theory and soft set theory to enhance decision-making processes. Acharjee and Medhi [S. Acharjee and S. Medhi, Logical connectives of fuzzy soft set theory, New Mathematics and Natural Computation, 21(1) (2025) 339–352] introduced several advanced concepts related to logical connectives in fuzzy soft set theory. Building on Molodtsov’s foundational work, this paper explores new developments in fuzzy soft logic, extending the findings of Acharjee and Medhi. Specifically, we introduce the concepts of [Formula: see text]-fuzzy soft implication and [Formula: see text]-fuzzy soft bi-implication, along with their related results within the framework of fuzzy soft logic.

Fuzzy logic (FL) is a mathematical technique for dealing with imprecise data and problems that have many solutions rather than one. Although it is implemented in digital computers which ultimately make only yesno decisions, FL works with ranges of values, solving problems in a way that more resembles human logic. FL is a multi-valued (as opposed to binary) logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 and 1, black and white, yes or no; in terms of Boolean algebra, everything is in one set or another but not in both. FL allows for partial membership in asset values between 0 and 1, shades of gray, and introduces the concept of the “fuzzy set.” When the approximate reasoning of FL (Zadeh, 1965) is used with an expert system, logical inferences can be drawn from imprecise relationships. FL theory was developed by Lofti A. Zadeh at the University of California in the mid 1960s. However, it was not applied commercially until 1987 when the Matsushita Industrial Electric Co. used it to automatically optimize the wash cycle of a washing machine by sensing the load size, fabric mix, and quantity of detergent and has applications in the control of passenger elevators, household applications, and so forth.

The ensemble Kalman filter (EnKF) developed at the Meteorological Research Branch of Canada is used in the context of synthetic radial wind data assimilation at the mesoscale. A dry Boussinesq model with periodic boundary conditions is employed to provide a control run, as well as two ensembles of first guesses. Synthetic data, which are interpolated from the control run, are assimilated and simulate Doppler radar wind measurements. Nine “radars” with a range of 120 km are placed evenly on the horizontal 1000 km × 1000 km domain. These radars measure the radial wind with assumed Gaussian error statistics at each grid point within their range provided that there is sufficient upward motion (a proxy for precipitation). These data of radial winds are assimilated every 30 min and the assimilation period extends over 4 days. Results show that the EnKF technique with 2 × 50 members performed well in terms of reducing the analysis error for horizontal winds and temperature (even though temperature is not an observed variable) over a period of 4 days. However the analyzed vertical velocity shows an initial degradation. During the first 2 days of the assimilation period, the analysis error of the vertical velocity is greater when assimilating radar observations than when scoring forecasts initialized at t = 0 without assimilating any data. The type of assimilated data as well as the localization of the impact of the observations is thought to be the cause of this degradation of the analyzed vertical velocity. External gravity modes are present in the increments when localization is performed. This degradation can be eliminated by filtering the external gravity modes of the analysis increments. A similar set of experiments is realized in which the model dissipation coefficient is reduced by a factor of 10. This shows the level of sensitivity of the results to the kinetic energy power spectrum, and that the quality of the analyzed vertical wind is worse when dissipation is small.

In a recent article in Cancer, Cho and colleagues1 suggested that the Cancer of the Liver Italian Program (CLIP) system was the best prognostic system for predicting the survival of patients with hepatocellular carcinoma (HCC) who were undergoing transcatheter arterial chemoembolization, and further validation studies will be performed. We agree with the authors. Predicting mortality is an important component of the decision-making process, and scoring systems (Child-Pugh, CLIP, Model for Endstage Liver Disease, Barcelona Clinic Liver Cancer, etc) long have been accepted for establishing patient outcomes in HCC. Although these models perform well in predicting the mortality or morbidity in the original cohorts on which the models were developed, they may underestimate or overestimate mortality or morbidity in different populations.1 However, in the midst of such uncertainty, fuzzy logic may play an important role in the decision-making process. Fuzzy logic is the science of reasoning, thinking, and inference that recognizes and uses the real-world phenomenon that everything is a matter of degree. In the simplest terms, fuzzy-logic theory is an extension of binary theory that does not use crisp definitions and distinctions. Instead of assuming that everything must be defined in to black and white (binary view), fuzzy logic is a method that captures and uses the concept of fuzziness in a computationally effective manner. Neural networks and fuzzy logic are 2 complementary technologies. Neural networks can learn (ie, adapt) from data and feedback, but understanding the pattern learned by neural networks is difficult. Conversely, fuzzy rule-based models are easy to comprehend, because they use linguistic terms and the structure of if/then rules. Unlike neural networks, fuzzy logic does not come with a learning algorithm (ie, the system cannot learn from feedback). The combination of neural networks and fuzzy logic has created a new term, a neurofuzzy system. Neural networks, fuzzy systems, and the combination of both already have been applied successfully to computer-aided diagnosis—eg, detection of microcalcification, automatic detection of distorted plethysmogram pulses in neonates and pediatric patients, detection of erythematosquamous diseases, and lung-nodule detection—and have been useful for predicting the presence of prostate cancer.2 In conclusion, slight variations in population characteristics can change predictive values of the scoring systems. However, neurofuzzy systems can incorporate data from many clinical, biologic, and genetic variables and can provide better risk estimations for hepatocellular carcinoma. Mesut Tez MD*, Baris Zulfikaroglu MD , * Gazi University School of Medicine, Department of Surgery, Ankara, Turkey, Ankara Numune Hospital, Department of Fifth Surgery, Ankara, Turkey.

This paper is about control of Automated Guided Vehicle for path following using fuzzy logic controller. The Automated Guided Vehicle is a tricycle wheeled mobile robot with three wheels, two fixed passive wheels and one steering driving wheel. First, kinematic and dynamic modeling for Automated Guided Vehicle is presented. Second, a controller that integrates two control loops, kinematic control loop and dynamic control loop, is designed for Automated Guided Vehicle to follow an unknown path. The kinematic control loop based on Fuzzy logic framework and the dynamic control loop based on two PID controllers are proposed. Simulation and experimental results are presented to show the effectiveness of the proposed controllers.

PurposeLimited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.Design/methodology/approachA Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.FindingsResults of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.Originality/valueThis paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.