Concepts Of Theory Research Articles

Assessment of Overflow Risk in Agricultural Reservoir Based on Water Balance Theory

The unexpected climate changes are causing rainfalls to exceed the design frequency, which complicates the management and operation of agricultural reservoirs and increases the risk of overflow. According to the “Planning and Design Standards for Agricultural Prediction Infrastructure Maintenance Project (Fill Dam Section),” agricultural reservoirs were originally designed without flood control functions, with the standard being to directly discharge incoming flood. Based on this foundation, this study assesses the overflow risk of agricultural reservoirs using the concepts of inflow and outflow theories as well as storage capacity. The balance when inflow and outflow are at their maximum was analyzed for prioritized areas, focusing on the flood management area (the area between the full water level and crest elevation) relative to the basin area, to examine the feasibility of balance through flood routing. The proposed method is easily accessible to facility managers, enabling them to assess the risk of reservoirs for efficient management and operation during floods.

The Application of Information Theory to Interpret Shore Platform Erosion Rates

Advancements in information physics have recently introduced the application of information theory to investigate physical systems. The behaviour of erosion at the granular scale is to date still a complex system to unpack, and therefore geomorphology research requires novel approaches to better inform the interpretation of temporal and spatial erosion patterns at different scales. This paper applies information theory concepts to re-evaluate erosional data that were measured on limestone surfaces of two shore platforms in Malta with a traversing micro-erosion meter (TMEM). By representing erosion rates through their information content using a Box-Cox style transformation of the raw data (application of an inverse normal distribution function to fractionally ranked data), it is possible to identify points and measurement periods that contribute to a disproportionately large share of unexpected erosion rates that could provide more insight into the causes of erosion rates. Despite the variations in the information content from erosion rates at individual measurement points, most points consistently contribute to a similar amount of information. These findings illuminate the importance of considering the informational value of erosion data to further understand the underlying physical processes and potentially improve predictive models.

Cognitive Analysis of Euphemisms in The Big Bang Theory under CBT

This research delves into the pragmatic function of euphemisms in the context of the American sitcom The Big Bang Theory, utilizing Conceptual Blending Theory (CBT) as the analytical framework. The purpose is to understand how euphemisms, as a form of polite language, are constructed to avoid direct and potentially offensive expressions, thus promoting social harmony. The methodology involves a detailed analysis of euphemistic language within the sitcom, focusing on the integration of mental spaces as proposed by CBT. The study identifies key euphemisms and constructs a conceptual integration network for each, revealing the input spaces, generic space, and the blended space that emerges, offering new meanings. The main results demonstrate that euphemisms are cognitively processed through the blending of spaces, where elements from different domains combine to form a less confrontational meaning. The conclusion underscores the efficacy of CBT in explaining the complexity of euphemistic language, showcasing its role in navigating social norms and cultural sensitivities. This research provides insights into American cultural nuances and the strategic use of language in social interactions, enhancing the understanding of euphemisms as a cross-cultural communication tool.

Modeling collaborative memory with SAM.

While humans often encode and retrieve memories in groups, the bulk of our knowledge of human memory comes from paradigms with individuals in isolation. The primary phenomenon of interest within the relatively new field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared with noncollaborative groups of the same size. This effect has been found in a variety of materials and group compositions. However, most research in this field is led by verbal conceptual theories without guidance from formal computational models. We present a framework to scale the Search of Associative Memory model (SAM) to collaborative free recall paradigms with multiple models working together. Multiple SAM models recalling together naturally produce collaborative inhibition when the group members use recalls by the group as cues to retrieve from memory, strongly supporting the "retrieval disruption" hypothesis. This work shows that SAM can act as a unified theory to explain both individual and collaborative memory effects and offers a framework for future predictions of scaling to increased group sizes, shared knowledge, and factors facilitating the spread of false memories in groups.

Visualization of commuting areas and examination of jobs-housing balance with the adoption of excess commuting

Rapid urban growth extends the boundaries of urbanized areas and makes people spend extra travel cost on the journey to work. Scholars have examined commuting behaviors between residence and work places within a region, and suggested the conceptual theory of jobs-housing balance to evaluate if there are sufficient job opportunities for residents in the region. A quantitative measurement of excess commuting is proposed to reflect the degree of jobs-house balance. However, current studies focus on the evaluation of excess commuting and the causality of spatial restructuring between residence and workplace, the spatial extend of excess commuting in an urban region is not visually delineated. In addition, conventional studies acquire static data such as statistic data or other survey data to analyze impacts of commuting patterns on urban formation. Today the emergence of spatial big data sheds light on the causality of urban commuting from higher spatial and temporal resolutions. This study aims to examine the degree of jobs-housing balance through the calculation of excess commuting and represent the spatial extent of excess commuting of Taichung metropolitan area in Taiwan. Electronic Toll Collection (ETC) data, the spatial big data which is publicly accessible in Taiwan, is introduced as the commuting data and to calculate three key parameters of excess commuting, Tmin, Tobs, and Tmax. The three parameters are used to visualize the extent of commuting zones and examine commuting patterns of Taichung metropolitan area and observe the spatial relationship between those patterns and land-use types. The evaluation of excess commuting indicates that the degree of jobs-housing imbalance in Taichung metropolitan area is similar to Los Angeles metropolis in US. The analytical results explain how ETC data can be successfully used for the calculation of excess commuting, and visualization of the extent of commuting regions. In addition, after the cluster analysis of residential areas, it is intuitive to observe spatial correlation between hot zones of residential areas and the spatial extent of theoretical maximum commuting, and shows that the scenarios of commuting can be considered as an indicator to explain the possibility of urban expansion in the future.

Analisis Pengaruh Implementasi Teori Vygotsky Terhadap Pembelajaran Di Kelas

Vygostky's learning theory discussed in this article was pioneered by a figure named Lev Vygotsky who argued that learning is a social process. According to him, children have expertise in developing knowledge starting from social interactions with their social environment. One of the applications is cooperative learning conducted in a group, which contains a group of children who have different abilities to work together and help each other. Vygotsky's theory has the advantage that students are able to think together in solving problems so that the problem can be solved, but it also has the disadvantage that the time needed is longer because of the different abilities of students. The writing of this article aims to find out the history of the Vygotsky theorist, the concept of Vygotsky theory, the implementation of Vygotsky theory in education and its influence in learning. The author uses literature review and questionnaire methods in writing this article. The results obtained from this study were that some of the respondents felt helped by the implementation of Vygotsky's theory in learning, while others did not feel helped or neutral.

Pressure-induced band gap enhancement and temperature-dependent thermoelectric characterization of semiconducting Transition Metal Chalcogenides LiMS (M = Cu, Ag)

The first principles study of Transition Metal Chalcogenides LiMS (M = Cu, Ag) has been conducted utilizing the concepts of Density Functional Theory (DFT). Both the compounds possess a cubic structure with space group F-43m. The mechanical stability of LiCuS and LiAgS has been predicted based on the values of elastic tensor. These Chalcogenides possess an interesting electronic band structure which reveals their semiconducting nature with a direct band gap. The electronic structure is further subjected to high pressure and the changes in band gap are deeply observed. For these materials, the changes in band gap in response to applied pressure suggest the possibility of their use in band gap engineering. The phonon curves are investigated over the Brillouin zone's high symmetry directions which reveal the lattice dynamical stability of the presently studied compounds. Moreover, the atoms present at different positions inside a crystal lattice exhibit vibrations of different intensities and in different directions which have been studied through the Eigenvector representations. The thermoelectric properties of LiCuS and LiAgS have been characterized using Boltzmann's theory over the range of temperature between 100 K and 1600 K. The calculated thermoelectric parameters reveal the high thermoelectric efficiency of these materials with excellent values of the figure of merit. The interesting properties of ternary Chalcogenides LiMS (M = Cu, Ag) make them promising candidates for thermoelectric applications.

Inferential and Macrostructural Ability in Multimodal Texts

Objective: To evaluate inferential and macrostructural ability through three types of multimodal artifacts: verbal, graphic-mathematical, and complete text (which integrates all semiotic modes). Theoretical Framework: This section explores the key concepts of multimodality theory, as well as the notions of inferences and multimodal ability, which are fundamental to addressing the research problem. The main theories and concepts supporting the research are presented, providing a solid foundation for understanding the study's context. Method: A mixed methodology was employed, combining quantitative and qualitative approaches. The quantitative phase adopted a correlational ex post facto design, while the qualitative phase was carried out through a focus group. The quantitative study followed a factorial experimental between-subjects design, considering two dimensions: the participants' professional program and the semiotic systems present in the comprehension test. Results and Discussion: It was concluded that the type of multimodal artifact does not significantly affect the efficient identification of inferences or macrostructural reduction. This suggests that students exhibit a low level of multimodal ability. Research Implications: The practical and theoretical implications of the research are analyzed, providing insights into how the results can influence or be applied to practices related to multimodal comprehension. Originality/Value: The research highlights that universities, in general, do not pay sufficient attention to multimodality, which negatively impacts students' multimodal competencies.

Introducing chaos theory in the classroom: A life sciences students’ perception.

This article details a concise in-class workshop to introduce life science students to basic chaos theory concepts, which is a modern subject and has high potential for applications in life sciences and other fields, justifying its knowledge. The teaching approach is based on a pragmatic strategy that uses only the essential mathematical and computing concepts required to reach the learning outcome, which this article explains step-by-step and, therefore, at least in expectation, even students or professors with no previous software knowledge could understand them. The proposed educational approach uses a meaningful learning approach, and population growth models as an educational anchor subject, which is a common subject for life science students and may be intuitive for students from other areas. Feedback from 70 students surveyed after the workshop yielded positive results, and 72.9% of the students expressed confidence in explaining basic chaos theory concepts following the session, and 94.7% stated they would recommend it to their peers, which underscores the present proposal feasibility.

“She” power: role transition and self-empowerment of women in rural tourism

It is widely believed that participation in tourism empowers women, particularly by providing direct employment and income generation. However, this perspective frequently overlooks women’s agency and self-actualization. This study examines the Tingsong Cultural Community (TCC) in northeast China, integrating self-empowerment with role theory to focus on female participation in tourism. Utilizing multiple data collection strategies, both primary and secondary data were gathered between July 2019 and December 2022. Through inductive thematic analysis, the research unpacks the role transitions of Chinese rural women within tourism practices and the dynamics of self-empowerment. The findings reveal that: (1) role transition is a dynamic process involving exploration, identification, and obligation, marked by an enhanced sense of self-empowerment; (2) self-empowerment arises from awareness and interaction, benefiting from economic, cultural, and subjective dimensions; and (3) external support is essential for empowerment, ideally starting with vulnerable individuals to allow self-empowerment to develop organically. This study refines the concepts of role theory and self-empowerment while establishing the relationship between rural women’s role transitions and self-empowerment, contributing to existing knowledge. The practical implications related to external investment, endogenous development, and digital engagement are insightful for the sustainability of similar destinations.

Pedagogical strategies for teaching nursing theories in undergraduate students: A scoping review protocol.

From a pedagogical point of view, there is a great deal of creativity and practice in teaching conceptual models and nursing theories. However, there seems to be no synthesis of knowledge regarding the pedagogical methods used to teach them. The purpose of this scoping review is to map the available literature on the teaching of nursing conceptual models and theories in undergraduate nursing education. The scoping review will be developed in accordance with the JBI scoping review methodology and the PRISMA scoping review checklist. The aim of the results is to map the available literature data that can serve as inspiration and a foundation for the development of specific courses on conceptual models and theories in nursing sciences. This scoping review will be served as the basis for a study that will be described and explored the integration of nursing knowledge into bachelor of nursing curricula.

Intermediality of Modern Art and Technologies of its Perception

The purpose of the article is to reveal the peculiarities of the manifestation of termediality in modern art in their influence on perceptual processes and to propose appropriate technologies for the perception of intermedial artistic phenomena. The research methodology is based on the concepts of media theory, communication theory, modern art theory and uses general scientific principles of interconnection, systematisation, and synergy. The purpose and objectives of the article necessitated the application of the method of extrapolation of heuristic algorithms of exact sciences to the heuristics of artistic phenomena with a view to practical methods of organising the perception of the intermediality of modern art by students of higher education. The use of the analytical method made it possible to identify the content and design resources of intermediality in its current development. The scientific novelty consists in projecting the properties of intermediality of modern art onto the technology of its perception in the process of university training. In the article, for the first time in domestic art history, the peculiarities of the manifestations of intermediality of modern art in connection with the communication of its recipients are understood. Conclusions. Intermediality is one of the main trends in the development of art, which consists in a kind of dialogue. From the point of view of perceptual processes, intermediality actualises the integrity of perception, which involves different sensory modalities. Intermediality in the modern artistic space outgrows the boundaries of the interaction of arts, connecting other spheres of culture and ensuring the inseparability of synthesised elements in creative practices different from traditional ones. Perception of intermedia modern art requires preparation, which should be carried out in the educational process. Effective technologies for perceiving the intermediality of modern art are interactive and intermedial.

Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model

Abstract Understanding the relationship between structural connectivity (SC) and functional connectivity (FC) of the human brain is an important goal of neuroscience. Highly detailed mathematical models of neural masses exist that can simulate the interactions between functional activity and structural wiring. These models are often complex and require intensive computation. Most importantly, they do not provide a direct or intuitive interpretation of this structure–function relationship. In this study, we employ the emerging concepts of spectral graph theory to obtain this mapping in terms of graph harmonics, which are eigenvectors of the structural graph’s Laplacian matrix. In order to imbue these harmonics with biophysical underpinnings, we leverage recent advances in parsimonious spectral graph modeling (SGM) of brain activity. Here, we show that such a model can indeed be cast in terms of graph harmonics, and can provide a closed-form prediction of FC in an arbitrary frequency band. The model requires only three global, spatially invariant parameters, yet is capable of generating rich FC patterns in different frequency bands. Only a few harmonics are sufficient to reproduce realistic FC patterns. We applied the method to predict FC obtained from pairwise magnitude coherence of source-reconstructed resting-state magnetoencephalography (MEG) recordings of 36 healthy subjects. To enable efficient model inference, we adopted a deep neural network-based Bayesian procedure called simulation-based inference. Using this tool, we were able to speedily infer not only the single most likely model parameters, but also their full posterior distributions. We also implemented several other benchmark methods relating SC to FC, including graph diffusion and coupled neural mass models. The present method was shown to give the best performance overall. Notably, we discovered that a single biophysical parameterization is capable of fitting FCs from all relevant frequency bands simultaneously, an aspect that did not receive adequate attention in prior computational studies.

Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection

Background and Objective: Epilepsy is one of the common diseases of the nervous system, which affects almost 1% of the world’s population. The unpredictable nature of epileptic seizures has caused people suffering from it to be deprived of their normal life, and making a device that can detect a seizure onset can help them. Methods:Game theory is a branch of mathematical science that, after proving its ability in economics, has entered into the field of signal processing and is trying to solve complex problems with a new approach. In this paper, we use game theory and coalitional game concepts to provide a novel way of feature selection in the analysis of Electroencephalography (EEG) signals. We have used a variety of statistical, morphological, and frequency features, and with the proposed feature selection algorithm we have attempted to select the smallest coalition which discriminates between two brain states while appropriately allocating resources to satisfy our desired sensitivity, delay, and false alarm. A modified change point detection algorithm has also been introduced to be used to detect epileptic seizures. Results:The results of the test on the CHB-MIT dataset show that using the proposed algorithm, we can achieve improved performance in terms of sensitivity, delay, and false alarm with a few number of features. Our framework also provides a means to focus on desired criteria for different applications. In scenario which the focus was on sensitivity, the proposed framework achieved 99% average sensitivity, 0.4/h average false alarm, and 2.4 s average delay with only using about 2 features for each patient. Conclusion:By applying the proposed framework on the CHB-MIT dataset we observe that we can achieve better performance compared to recent studies with fewer features in terms of sensitivity, delay, and false alarm metrics. Such an algorithm can be used for the purpose of real-time detection and in wearable devices.

Failure mechanism and crashworthiness optimization of variable stiffness nested origami crash box

In recent years, the study of the crashworthiness of lightweight and efficient thin-walled energy-absorbing structures has received increasing attention. In this work, 3D printing technology was adopted to create a nested origami crash box with stiffness variation, which was made of short carbon fiber reinforced nylon material. The structure effectively inhibits the development of failure behavior of short carbon fiber-reinforced nylon origami crash box due to material brittleness. The quasi-static compression experiment results indicate that compared to origami crash box, nested origami crash box improves by 40% in specific energy absorption and 50% in crash force efficiency, while the initial peak crushing force remains unchanged. The effects of three key geometrical parameters, namely dihedral angle, distance between tubes, and height difference on the damage mechanism of the nested origami crash box are discussed in detail using the finite element method, and the response surface model combined with the non-dominated sorting genetic algorithm II is used for multi-objective optimization. In this work, the concepts of origami theory and nested systems are integrated for the first time, aiming to achieve a stable and efficient energy-absorbing deformation mode by precisely modulating the stress transfer path of the structure and its stiffness. This work provides new ideas for the design and fabrication of novel lightweight energy-absorption boxes.

Divided by policy, united by resilience: Using transformative pedagogy to impact prospective teachers in all contexts

As American teacher educators and teachers are being roiled by restrictive legislation around critical race theory and divisive concepts in some states, our investigation explored the impact of critical communities of practice (CoP) on redesigning and teaching introduction to education courses with a transformative lens. This manuscript details the collaboration of four equity-minded teacher educators, each representing unique institutional and political contexts. While two authors taught in states which promote culturally sustaining pedagogies, the other two authors taught in states that had passed laws restricting the professional autonomy of educators and honest discussions of race and racism. Participation in this critical CoP as a support group enabled us to better understand and navigate our dichotomous policy contexts and renewed our commitment to teaching prospective teachers to be politically conscious and empowered to teach in anti-racist ways. Our CoP was instrumental in helping us recognize that transformative pedagogies and collaboration are effective tools that can provide much needed support to teacher educators across policy contexts, while we simultaneously strive to prepare teacher candidates who will teach in culturally sustaining ways.

Creativity reputation allocation in open and distributed innovation

Much of today's creative work crosses organizational borders. This limits the possibilities for directly compensating creative workers. Thus, other forms of reward, such as reputation-building, are necessary. This study builds on the concept of creative reputation (reputation for creativity) and signaling theory. It asks how reputation should be assigned to creative workers to reflect their creative abilities most accurately. We illustrate reputation allocation using the example of the academic system and develop a simple computational model to compare how different reputation allocation mechanisms—varying in how they utilize observable information about creative workers' outcomes—produce reputations for individual creative workers. The insights derived from the model contribute to our understanding of open and distributed innovation systems. They also challenge current practices in evaluating and recruiting creative workers and motivate future research on creative reputation and reputation allocation.

Novel decision making approach for sustainable renewable energy resources with cloud fuzzy numbers

Decision making approaches depending on the assessments of individual decision makers produce inaccurate results due to the existence of multiple uncertainties. To model intrapersonal uncertainty, interpersonal uncertainty and randomness in decision making assessments, this research study proposes a novel approach by integrating linear and non-linear type of fuzzy numbers with cloud model theory using novel technique of computing entropy of these fuzzy numbers. A novel mathematical model known as cloud fuzzy numbers is introduced using the concepts of fuzzy numbers and cloud theory. The self-evaluated relative weights of experts are computed using a non-linear optimization method which is based on maximum deviation method and Lagrange multipliers of cloud fuzzy numbers. The new cloud fuzzy numbers are then combined with CODAS (combinative distance based assessment) approach that is based on the largest Euclidean and Taxicab distances for the selection of suitable criteria. Firstly, the linguistics evaluations are converted into the fuzzy numbers and then cloud fuzzy numbers using formulae of expectation and entropy ensuring that the obtained interval cloud values follows a normal distribution. Secondly, the cloud fuzzy weighted arithmetic averaging operator is used to aggregate cloud fuzzy numbers using the self-evaluated fuzzy weights Thirdly, the assessment score is determined to rank the alternatives by computing the distance between normalized weighted matrix and the negative ideal solution. Finally, a case study is discussed for the selection of best renewable energy resource in Turkey to elaborate the significance of the proposed research. The convergence and accuracy of the proposed model is proved with certain mathematical and theoretical results.

Evaluation and Improvement of College Dance Course Based on Multiple Intelligences Theory

Based on the theory of multiple intelligences, this paper discusses the methods and strategies for the evaluation and improvement of college dance courses. Firstly, this paper introduces the concept and development of multiple intelligence theory and emphasizes its complement and perfection to traditional intelligence theory. Then, it analyzes the problems and shortcomings of current college dance courses, including overemphasis on skill training and single evaluation criteria. Then, it puts forward the curriculum evaluation index based on the theory of multiple intelligences, including the evaluation of students’ multiple intelligences development and the evaluation of curriculum design and teaching methods. At the same time, curriculum evaluation tools and methods are discussed, such as questionnaire survey, observation record, learning achievement display and student self-rating. In the aspect of curriculum improvement and perfection strategy, it emphasizes the application of multiple intelligence theory, interdisciplinary integration and the introduction of scientific and technological means. In terms of curriculum implementation and effect evaluation, the importance of students’ comprehensive intelligent evaluation and students’ feedback is emphasized. Finally, the paper summarizes the practical value of the evaluation and improvement of college dance courses based on the theory of multiple intelligences and looks forward to the future research direction and prospects.

Shortest path counting in complex networks based on powers of the adjacency matrix.

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.