Search Results (55)

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article

Drought is a complex and impactful natural hazard, with sometimes catastrophic impacts on small or subsistence agriculture and water security. In Pacific Island countries, there lacks an agreed approach for monitoring agricultural drought hazard with satellite-derived remote sensing data. This study addresses this gap through a framework for agricultural drought monitoring in the Pacific using freely available space-based observations. Applying World Meteorological Organization’s (WMO) recommendations and a set of objective selection criteria, three remotely sensed drought indicators were chosen and combined using fuzzy logic to form a composite drought hazard index: the Standardised Precipitation Index, Soil Water Index, and Normalised Difference Vegetation Index. Each indicator represents a subsequential flow-on effect of drought on agriculture. The index classes geographic areas as low, medium, high, or very high levels of drought hazard. To test the drought hazard index, two case studies for drought in the western Pacific, Papua New Guinea (PNG), and Vanuatu, are assessed for the 2015–2016 El Niño-related drought. Findings showed that at the height of the drought in October 2015, 58% of PNG and 72% of Vanuatu showed very high drought hazard, compared to 6% and 40%, respectively, at the beginning of the drought. The hazard levels calculated were consistent with conditions observed and events that were reported during the emergency drought period. Application of this framework to operational drought monitoring will promote adaptive capacity and improve resilience to future droughts for Pacific communities. Full article

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

Fuzzy differential subordinations, a notion taken from fuzzy set theory and used in complex analysis, are the subject of this paper. In this work, we provide an operator and examine the characteristics of meromorphic functions in the punctured open unit disk that are related to a class of complex parameter operators. Complex analysis ideas from geometric function theory are used to derive fuzzy differential subordination conclusions. Due to the compositional structure of the operator, some pertinent classes of admissible functions are studied through the application of fuzzy differential subordination. Full article

The bipolar fuzzy model is a rapidly evolving research area that provides a robust framework for addressing real-world problems, with wide-ranging applications in scientific and technical domains. Within this framework, bipolar fuzzy graphs play a significant role in decision-making and problem-solving, particularly through domination theory, which helps tackle practical challenges. This study explores various operations on product bipolar fuzzy graphs, including union (∪), join (+), intersection (∩), Cartesian product (×), composition (∘), and complement, leading to the generation of new graph structures. Several important results related to complete product bipolar fuzzy graphs under these operations are established. Additionally, we introduce key concepts such as dominating sets, minimal dominating sets, and the domination number $⋎\left(\mathcal{H}\right)$, supported by illustrative examples. This study further investigates the properties of domination in the context of these operations. To demonstrate practical applicability, we present a decision-making problem involving the optimization of bus routes and the strategic placement of bus stations using domination principles. This research contributes to the advancement of bipolar fuzzy graph theory and its practical applications in real-world scenarios. Full article

This review examines the integration of advanced ultrasonic techniques and artificial intelligence (AI) for monitoring and analyzing concrete structures, focusing on detecting and classifying internal defects. Concrete structures are subject to damage over time due to environmental factors and dynamic loads, compromising their integrity. Non-destructive techniques, such as ultrasonics, allow for identifying discontinuities and microcracks without altering structural functionality. This review addresses key scientific challenges, such as the complexity of managing the large volumes of data generated by high-resolution inspections and the importance of non-linear models, such as the Hammerstein model, for interpreting ultrasonic signals. Integrating AI with advanced analytical models enhances early defect diagnosis and enables the creation of detailed maps of internal discontinuities. Results reported in the literature show significant improvements in diagnostic sensitivity (up to 30% compared to traditional linear techniques), accuracy in defect localization (improvements of 25%), and reductions in predictive maintenance costs by 20–40%, thanks to advanced systems based on convolutional neural networks and fuzzy logic. These innovative approaches contribute to the sustainability and safety of infrastructure, with significant implications for monitoring and maintaining the built environment. The scientific significance of this review lies in offering a systematic overview of emerging technologies and their application to concrete structures, providing tools to address challenges related to infrastructure degradation and contributing to advancements in composite sciences. Full article

The capability of complex fuzzy sets plays a valuable role in resolving many real-life problems. In this paper, we present the compositions of complex fuzzy relations by using the idea of implication operators and max-product compositions of complex fuzzy relations and illustrate these compositions with concrete examples. The converse of these newly invented triangular compositions in terms of compositions of the converse relations is also defined. We also study the interactions with the union and intersection. The main goal of this article is to present a new technique to enhance medical diagnostic models that can assist in improving the features of healthcare systems. We utilize these compositions to diagnose diseases in patients on the basis of the intensity of symptoms. Full article

This paper’s findings are related to geometric function theory (GFT). We employ one of the most recent methods in this area, the fuzzy admissible functions methodology, which is based on fuzzy differential subordination, to produce them. To do this, the relevant fuzzy admissible function classes must first be defined. This work deals with fuzzy differential subordinations, ideas borrowed from fuzzy set theory and applied to complex analysis. This work examines the characteristics of analytic functions and presents a class of operators in the open unit disk ${\mathcal{J}}_{\eta ,\varsigma }^{\kappa }(\mathrm{a},\mathrm{e},\mathrm{x})$ for $\varsigma >-1,\eta >0,$ such that $\mathrm{a},\mathrm{e}\in \mathbb{R},(\mathrm{e}-\mathrm{a})\ge 0,\mathrm{a}>-\mathrm{x}$. The fuzzy differential subordination results are obtained using (GFT) concepts outside the field of complex analysis because of the operator’s compositional structure, and some relevant classes of admissible functions are studied by utilizing fuzzy differential subordination. Full article

The groundwater of the Luohe Formation in Binchang mining area is the main source of water for industrial and agricultural use and for drinking water for residents in the area. In order to study the hydrochemical characteristics and water-quality status of Luohe Formation groundwater in the mining area, statistical analysis, Piper three-line diagram, ion ratio relationship, and other methods were used to study the hydrochemical characteristics and formation factors of the groundwater. The Nemerow index evaluation method and the fuzzy comprehensive evaluation method based on principal component analysis were used to evaluate the groundwater quality in the mining area. The results show that the groundwater is weakly acidic as a whole, and the content of SO₄²⁻ and Cl⁻ have strong variability in terms of spatial distribution. The groundwater chemical type gradually evolves from SO₄ • HCO₃ • Cl–Na, SO₄–Na and SO₄ • Cl–Na-type water in the north of the mining area to SO₄ • HCO₃ • Cl–Na • Ca, HCO₃ • SO₄–Na • Mg, and SO₄ • Cl–Na • Ca • Mg-type water in the south. The formation of the hydrochemical composition of groundwater in the study area may be related to multiple factors such as cation-alternating adsorption, carbonate and sulfate dissolution, and hydraulic exchange with the groundwater of the upper Huachi Formation. Comparing the evaluation results of the Nemerow index method and the principal component analysis method, the latter’s evaluation results can take into account the contribution of each indicator to the overall groundwater quality, and to a certain extent can weaken the control effect of a certain pollution indicator, exceeding the limit on the entire evaluation result. Therefore, the evaluation results based on the principal component analysis method are more credible. Full article

Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future. Full article

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images. Full article

Many problems of practical interest can be modeled and solved by using interval-valued fuzzy graph (IVFG) algorithms. An IVFG is a very useful and effective tool for studying various calculations, fields of intelligence, and computer science, such as networking, imaging, and other fields, such as biological sciences. In different applications, they present an appropriate construction means. There were limitations in the definition of fuzzy graphs (FGs), which prompted us to propose a new definition for IVFGs. Some interesting properties related to the new IVFGs are investigated, and enough conditions under which the level graph on IVFGs is equivalent are obtained. Therefore, in this study, we present the properties of a level graph (LG) of an IVFG, and four operations, the Cartesian product (CP), composition (CO), union, and join, are investigated on it. Today, in a treatment system, one of the issues that can be very valuable and important to the quality of service to patients is finding qualified and efficient people in each department, which is not an easy task. But the interval-valued fuzzy graph, as an important fuzzy graph, can help us by considering the ability of each person in the form of intervals of numbers and the effectiveness of each one on the other (according to the relationships between them) in order to find the most worthy people. So, an application of IVFG to find the most effective person in a hospital information system has been introduced. Full article

Medical diagnosis is the process of finding out what is the disease a person may be suffering from. From the symptoms and their gradation, the doctor can decide which the dominant disease is. Nevertheless, in the process of medical diagnosis, there is ambiguity, uncertainty, and a lack of medical knowledge that can adversely affect the doctor’s judgment. Thus, a tool of artificial intelligence, fuzzy logic, has come to enhance the decision-making of diagnosis in a medical environment. Fuzzy set theory uses the membership degree to characterize the uncertainty and, therefore, fuzzy sets are integrated into imperfect data in order to make a reliable diagnosis. The patient’s medical status is represented as q-rung orthopair fuzzy values. In this paper, many versions and methodologies were applied such as the composite fuzzy relation, fuzzy sets extensions (q-ROFS) with aggregation operators, and similarity measures, which were proposed as decision-making intelligent methods. The aim of this procedure was to find out which of the diseases (viral fever, malaria fever, typhoid fever, stomach problems, and chest problems), was the most influential for each patient. The work emphasizes the contribution of aggregation operators in medical data in order to contain more than one expert’s aspect. The performance of the methodology was quite good and interesting as most of the results were in agreement with previous works. Full article

This paper explores the concept of Generalized Roughness in LA-Semigroups and its applications in various mathematical disciplines. We highlight the fundamental properties and structures of Generalized Roughness, examining its relationships with Fuzzy Lie Algebras, Order Theory, Lattice Structures, Algebraic Structures, and Categorical Perspectives. Moreover, we investigate the potential of mathematical modeling, optimization techniques, data analysis, and machine learning in the context of Generalized Roughness. Our findings reveal important results in Generalized Roughness, such as the preservation of roughness under the fuzzy equivalence relation and the composition of roughness sets. We demonstrate the significance of Generalized Roughness in the context of order theory and lattice structures, presenting key propositions and a theorem that elucidate its properties and relationships. Furthermore, we explore the applications of Generalized Roughness in mathematical modeling and optimization, highlighting the optimization of roughness measures, parameter estimation, and decision-making processes related to LA-Semigroup operations. We showcase how mathematical techniques can enhance understanding and utilization of LA-Semigroups in practical scenarios. Lastly, we delve into the role of data analysis and machine learning in uncovering patterns, relationships, and predictive models in Generalized Roughness. By leveraging these techniques, we provide examples and insights into how data analysis and machine learning can contribute to enhancing our understanding of LA-Semigroup behavior and supporting decision-making processes. Full article

The paper exposes some of the results obtained in a major research project related to the design, development, and experimental testing of a morphing wing demonstrator, with the main focus on the development of the automatic control of the actuation system, on its integration into the experimental developed morphing wing system, and on the gain related to the extension of the laminar flow over the wing upper surface when it was morphed based on this control system. The project was a multidisciplinary one, being realized in collaboration between several Canadian research teams coming from universities, research institutes, and industrial entities. The project’s general aim was to reduce the operating costs for the new generation of aircraft via fuel economy in flight and also to improve aircraft performance, expand its flight envelope, replace conventional control surfaces, reduce drag to improve range, and reduce vibrations and flutter. In this regard, the research team realized theoretical studies, accompanied by the development and wind tunnel experimental testing of a rectangular wing model equipped with a morphing skin, electrical smart actuators, and pressure sensors. The wing model was designed to be actively controlled so as to change its shape and produce the expansion of laminar flow on its upper surface. The actuation mechanism used to change the wing shape by morphing its flexible upper surface (manufactured from composite materials) is based on Shape Memory Alloys (SMA) actuators. Shown here are the smart mechanism used to actuate the wing’s upper surface, the design of the intelligent actuation control concept, which uses a self–tuning fuzzy logic Proportional–Integral–Derivative plus conventional On–Off controller, and some of the results provided by the wind tunnel experimental testing of the model equipped with the intelligent controlled actuation system. The control mechanism uses two fuzzy logic controllers, one used as the main controller and the other one as the tuning controller, having the role of adjusting (to tune) the coefficients involved in the operation of the main controller. The control system also took into account the physical limitations of the SMA actuators, including a software protection section for the SMA wires, implemented by using a temperature limiter and by saturating the electrical current powering the actuators. The On–Off component of the integrated controller deactivates or activates the heating phase of the SMA wires, a situation when the actuator passes into the cooling phase or is controlled by the Self–Tuning Fuzzy Logic Controller. Full article