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Found 6423 publications

An improved index of interactivity for fuzzy numbers

József Mezei Peter Varlaki Robert Fuller

Publication Name: Fuzzy Sets and Systems

Publication Date: 2011-02-16

Volume: 165

Issue: 1

Page Range: 50-60

Description:

In this paper we will introduce a new index of interactivity between marginal possibility distributions A and B of a joint possibility distribution C. The starting point of our approach is to equip each γ-level set of C with a uniform probability distribution, then the probabilistic correlation coefficient between its marginal probability distributions is interpreted as an index of interactivity between the γ-level sets of A and B. Then we define the index of interactivity between A and B as the weighted average of these indexes over the set of all membership grades. This new index of interactivity is meaningful for the whole family of joint possibility distributions. © 2010 Elsevier B.V. All rights reserved.

Open Access: Yes

DOI: 10.1016/j.fss.2010.06.001

Comparative Analysis of Machine Learning Algorithms in Traffic Mainstream Control on Freeway Networks

Mehran Amini Laszlo T. Koczy

Publication Name: Ines 2024 28th IEEE International Conference on Intelligent Engineering Systems 2024 Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 37-41

Description:

Efficient management of mainstream traffic flow on freeway networks is a critical challenge in urban transportation, with significant implications for congestion mitigation and environmental sustainability. The purpose of this study is to address the problem of predicting traffic volumes and maintaining flow rates below critical densities, thereby preventing the onset of congestion on interconnected freeway systems. Motivated by the need for real-Time traffic control strategies, this research employs machine learning algorithms to forecast traffic volumes, leveraging a comprehensive dataset of traffic patterns on freeways. In our approach, we conducted a comparative analysis of two advanced machine learning algorithms: Long Short-Term Memory (LSTM) networks, which are adept at modeling time-series data with long-range temporal dependencies, and Random Forest regression, known for its robust performance across diverse datasets. We enriched the traffic data through feature engineering, incorporating temporal variables, vehicular counts, and a calculated measure of proximity to critical density for the targeted freeway. Our findings indicate a markedly disparate performance between the algorithms. The LSTM model showed a moderate ability to capture the variance in traffic flow, with an R2 score of 0.619. In contrast, the Random Forest model demonstrated exceptional predictive accuracy, achieving an R2 of 0.998, and substantially outperforming the LSTM model in terms of both Mean Squared Error and Root Mean Squared Error.

Open Access: Yes

DOI: 10.1109/INES63318.2024.10629114

Risk Management for Cold Supply Chain: Case of a Developing Country

Laszlo Vasa Dragan Pamucar Yousaf Ali Amin Ullah Khan

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2022-01-01

Volume: 19

Issue: 8

Page Range: 161-185

Description:

Cold Supply Chain (CSC) involves temperature-controlled activities in the overall process, ranging from the raw material storage to the final supply of the products to the consumers. The activities involved are easily exposed to risks such as temperature and humidity, equipment failure and quality risk to name a few. Such sensitive processes need proper risk mitigation strategies, to ensure the effective functioning of the overall CSC. For this purpose, the current research conducted a vigorous literature review and identified 40 relevant risks related to CSC in a developing country. The risks were analyzed using Failure Mode and Effect Analysis (FMEA)-Risk Priority Number (RPN) technique to shortlist the significant risks. The significant risks were then subjected to the Full Consistency Method (FUCOM) for prioritization. The results concluded, contamination of food, temperature and humidity and quality as the top-three risks that can be dangerous for the overall cold supply chain. To overcome these risks, the study recommends the proper implementation of traceability systems and Radio Frequency Identification (RFID) systems. Furthermore, employing the latest technologies and efficient personnel training can also help overcome these risks. Such an application of the study in the case of a developing country, Pakistan's CSC forms to be the first of its kind. Furthermore, the application of FMEA-RPN along with the FUCOM technique in the scenario of CSC risk management forms the novelty of this research study.

Open Access: Yes

DOI: DOI not available

Constrained ordered weighted averaging aggregation with multiple comonotone constraints

Lucian Coroianu Marek Gagolewski Robert Fuller Simon James

Publication Name: Fuzzy Sets and Systems

Publication Date: 2020-09-15

Volume: 395

Issue: Unknown

Page Range: 21-39

Description:

The constrained ordered weighted averaging (OWA) aggregation problem arises when we aim to maximize or minimize a convex combination of order statistics under linear inequality constraints that act on the variables with respect to their original sources. The standalone approach to optimizing the OWA under constraints is to consider all permutations of the inputs, which becomes quickly infeasible when there are more than a few variables, however in certain cases we can take advantage of the relationships amongst the constraints and the corresponding solution structures. For example, we can consider a land-use allocation satisfaction problem with an auxiliary aim of balancing land-types, whereby the response curves for each species are non-decreasing with respect to the land-types. This results in comonotone constraints, which allow us to drastically reduce the complexity of the problem. In this paper, we show that if we have an arbitrary number of constraints that are comonotone (i.e., they share the same ordering permutation of the coefficients), then the optimal solution occurs for decreasing components of the solution. After investigating the form of the solution in some special cases and providing theoretical results that shed light on the form of the solution, we detail practical approaches to solving and give real-world examples.

Open Access: Yes

DOI: 10.1016/j.fss.2019.09.006

CT-Based Defect Analysis in Aluminium Rotor End Rings †

Ibolya Zsoldos István Kozma

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

The advantages of using cast aluminium rotors have been proven recently. However, discontinuities and porosities created during the casting of aluminium can cause problems during motor operation, such as eccentricity, losses, unwanted sounds, and false rotor fault indications. During the casting technology, care must be taken to ensure that there are as few cracks and porosities as possible in the volume and that their distribution is homogeneous. In this article, we present in detail the application of the modern CT-based methodology that has been spreading recently for the detection and analysis of discontinuities, voids, and porosities created during the casting of rotor end rings.

Open Access: Yes

DOI: 10.3390/engproc2024079093

Reliability incorporated optimal process pathway selection for sustainable microalgae-based biorefinery system: P-graph approach

Ákos Orosz Bing Shen How Juin Yau Lim Ferenc Friedler Chang Kyoo Yoo

Publication Name: Computer Aided Chemical Engineering

Publication Date: 2022-01-01

Volume: 49

Issue: Unknown

Page Range: 217-222

Description:

Biofuel from microalgae is one of the promising solutions on addressing climate change by its possibility of reducing the fossil fuel dependency. Till-date, the overall competitiveness of microalgae based biorefinery is the major concern due to its unique operational mechanism, especially the biological growth of microalgae that fluctuates towards the surrounding. Therefore, a novel graph-theoretic approach has been proposed to provide an optimization approach for identifying optimal process design with the consideration of three aspects that includes: economic, environmental, and reliability. The optimization is conducted using P-graph (a powerful graph-theoretic tool) which is capable to determine optimal and near-optimal solutions based on three objective functions: (i) minimizing annual operating cost, (ii) minimizing potential environmental impact, and (iii) maximizing reliability of process. The pool of feasible solutions (optimal and near-optimal) is obtained by satisfying the constraints on both greenhouse gas emissions and its respective reliability along. Thereupon, a further analysis was carried out with the aid of TOPSIS considering three of the assessment aspects to identify the optimal microalgae biorefinery configuration

Open Access: Yes

DOI: 10.1016/B978-0-323-85159-6.50036-1

Maize yield prediction based on artificial intelligence using spatio-temporal data

G. Milics Bálint Daróczy C. Kerepesi A. J. Kovács A. Nyéki M. Neményi András Benczúr

Publication Name: Precision Agriculture 2019 Papers Presented at the 12th European Conference on Precision Agriculture Ecpa 2019

Publication Date: 2019-01-01

Volume: Unknown

Issue: Unknown

Page Range: 1011-1017

Description:

The aim of this study was to predict maize yield by artificial intelligence using spatio-temporal training data. Counter-propagation artificial neural networks (CP-ANNs), XY-fused networks (XY-Fs), supervised Kohonen networks (SKNs), extreme gradient boosting (XGBoost) and support-vector machine (SVM) were used for predicting maize yield in 5 vegetation periods. Input variables for modelling were: soil parameters (pH, P2O5, K2O, Zn, Clay content, ECa, draught force, Cone index), and micro-relief averages and meteorological parameters for the 63 treatment units. The best performing method (XGBoost) attained 92.1 and 95.3% of accuracy on the training and the test set.

Open Access: Yes

DOI: 10.3920/978-90-8686-888-9_124

Estimation of optimum moisture content and maximum dry unit weight of fine-grained soils using numerical methods

Elizabeth Theron Armand Augustin Fondjo Richard P. Ray

Publication Name: Walailak Journal of Science and Technology

Publication Date: 2021-08-15

Volume: 18

Issue: 16

Page Range: Unknown

Description:

Soil compaction is one of the basic engineering techniques, which is carried out to guarantee the stability of soils dependent on specified strength. Nonetheless, in large-scale construction projects, the estimation of compaction features required tremendous effort and time that can be saved utilizing empirical relationships at the initial phases. It becomes critical to develop models to predict the compaction features, namely the maximum dry unit weight (γdmax) and optimum water content (WOP). This article attempts to develop models to predict the γdmax and WOP of fine-grained clay soils. Geotechnical tests such as grain size distribution, Atterberg limits, specific gravity, and proctor compaction tests are performed to assess soil samples' physical and hyro-mechanical characteristics. Multivariate analysis is conducted using MINITAB 18 software to develop the predictive models. The validation process of developed models includes the determination coefficient, probability value (p-value), comparison of the predicted values with experimental values, comparison of the models proposed in this study with other existing models found in the recent literature, and employing a different soil data set. The predicted values obtained from the models proposed in this research project are more accurate than other models developed recently. The proposed models estimate the compaction features of fine-grained clay soils with acceptable precision.

Open Access: Yes

DOI: 10.48048/wjst.2021.22792

Trematurid mites (Mesostigmata: Uropodina) associated with bark beetles (Coleoptera: Scolytinae) in Mexico

Jenő Kontschán Edith Guadalupe Estrada-Venegas Stacy R. Blomquist Alejandro Rodríguez-Ortega John C. Moser Martha Patricia Chaires-Grijalva Armando Equihua-Martínez Víctor Javier Arriola-Padilla Mauricio Pérez-Silva

Publication Name: Acarologia

Publication Date: 2026-01-01

Volume: 66

Issue: 2

Page Range: 364-376

Description:

Phoretic mites associated with bark beetles (Curculionidae: Scolytinae) play crucial roles in forest ecosystems, yet their diversity and distribution in Mexico remain understudied. This study aims to identify the species of the family Trematuridae (genera Trichouropoda and Oodinychus) associated with bark beetles across major coniferous forests in Mexico and to describe their host specificity and attachment patterns. A total of 1,713 bark beetles belonging to nine species were examined from 24 Mexican states. Mites were collected from the host bodies, galleries, and collection vial sediments. Eleven trematurid species were identified. Mites were recorded in ten states, with Trichouropoda polytricha being the most widespread and generalist species, associated with seven host species. A significant disparity in mite prevalence was observed among hosts, ranging from 1.57% in Dendroctonus mexicanus to 25.58% in D. rhizophagus. Phoretic deutonymphs exhibited a clear preference for specific attachment sites, primarily the ventral surface (29%) and gular area (22%). Furthermore, the presence of mites in both new and old galleries confirms these structures as essential micro-ecosystems for their biological cycle. Our findings highlight a high degree of host plasticity in certain species and a complex symbiotic relationship within the gallery environment. This research provides a fundamental baseline for future studies on the ecological impact of mite-beetle associations in Mexican forest health.

Open Access: Yes

DOI: 10.24349/qznl-uogs