Knowledge Rules Research Articles

Revealing the pattern of causality between tourist experience and the perception of sacredness at shamanic heritage destinations in Northeast China

For religious heritage tourism, the key to a destination’s reputation lies in enabling visitors to fully experience the sacredness or a unique spiritual experience offered by the site. The authenticity of the tourism experience and perception is the most significant factor influencing visitors’ perception of the sacredness of religious or holy sites. However, there is currently a lack of sufficient references and evidence to explore the relationship between tourists’ authentic experiences and their perception of sacredness in religious heritage tourism. The aim of this study is to elucidate the causal relationship between the characteristics or attributes of tourists’ authentic experiences and their perception of the sacredness of the destination. Furthermore, it seeks to explore resource development and site creation strategies for religious tourism destinations to continuously enhance the sense of sacredness of these places. The Shamanic cultural traditions in the traditional settlements of Northeast China provide a subtly different context for this study. However, the topics discussed have broad applicability in constructing the network of relationships between the characteristics of tourists’ experiences and their perception of sacredness in religious heritage tourism. This study applied the causal decision analysis model based on data mining (RSA-DEMATEL) and established a causal network diagram between core attributes based on clarifying the behavioral rule knowledge (i.e., If…Then… rules) between tourists’ travel experience (conditional attributes) and the perception of place sacredness (decision-making attributes). The study finds that enhancing tourists’ perception of the sacredness of a destination is related to authentic tourism experiences, the ability to observe believers and participate in the religious activities of local residents, landscapes that reflect local traditions and historical culture, as well as the number of tourists and the extent of commercial activities. This research offers further insights into the sustainable development of religious tourism, providing a critical conceptual framework and data analysis model for subsequent related studies, contributing significantly to both theory and practice.

Использование метаграфемных средств для выражения эмоций в интернет-комментариях

Оur research object is online comments to a text about reducing pension expenses and a sharp reduction in the number of pensioners in Russia. The text had a strong conflict potential due to the manipulative techniques used by the author; it caused a large number of aggressive comments from readers – people of retirement and pre-retirement age. The study subject is metagraphematic (singragraphemic and supragraphemic) means used by the authors of Internet comments as negative emotions expressing means along with verbal means of speech aggression. The following text analysis methods were used: formal, logical, semanticand syntactic analysis method. Features of metagraphemic means use in online comments were identified. In singraphemics this is the use of non-regulated by the Russian grammar rules punctuation marks and their homogeneous and heterogeneous combinations,etc. In supragraphemics – unmotivated and grammatically unsound use of uppercase and lowercase letters, as well as the use of specific emoticon signs indicating negative emotions experienced by the author of the comment. The reasons for this metagraphematic means usage in the comments may be the following: communicants’ state of emotional tension caused by the publication content and leading to an increase in the pace of computer typing; their age and the insufficient proficiency level of the typing techniques as well as the liberalization of language norms in Internet communications and poor grammatical rules knowledge by communicants. Our research showed that metagraphematic means are actively used in Internet comments to express the communicants’ emotions.

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation

Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor segmentation, but the segmentation based on DCNN is data-hungry for model training and ignores clinical knowledge. From the clinical view, bladder tumors originate from the mucosal surface of bladder and must rely on the bladder wall to survive and grow. This clinical knowledge of tumor location is helpful to improve the bladder tumor segmentation. To achieve this, we propose a novel bladder tumor segmentation method, which incorporates the clinical logic rules of bladder tumor and bladder wall into DCNNs to harness the tumor segmentation. Clinical logical rules provide a semantic and human-readable knowledge representation and are easy for knowledge acquisition from clinicians. In addition, incorporating logical rules of clinical knowledge helps to reduce the data dependency of the segmentation network, and enables precise segmentation results even with limited number of annotated images. Experiments on bladder MR images collected from the collaborating hospital validate the effectiveness of the proposed bladder tumor segmentation method.

Adequate, but Not Ideal: The U.S. Navy’s Need to Refine Its Administrative Separation Board Procedures

While the Navy is likely not mandated by the Constitution to edit its procedures for Administrative Separation Boards, it should do so. Service members can be subject to a variety of serious consequences through Administrative Separation Boards, so the processes should be as effective as possible to ensure that they are adequately protected. To improve the Administrative Separation Board Procedures for the United States Navy, this Comment suggests two policy changes. First, this Comment suggests that the Navy provide more training to Senior Members to ensure they are implementing the existing evidence rule correctly. Second, this Comment suggests that the Navy should add a version of the personal knowledge rule and lay witness opinion rule to the Administrative Separation proceedings. This Comment argues that these suggestions would benefit the Navy even though the Navy is not likely constitutionally mandated to make the changes.

Promoting (Safe) Young-User Cycling in Russian Cities: Relationships among Riders’ Features, Cycling Behaviors and Safety-Related Incidents

Background: Promoting cycling, walking and other ‘active’ transportation means continues to be a shared guideline in urban planning, closely aligned with the Sustainable Development Goals of the United Nations. Nonetheless, young cyclists’ safety figures and their potential contributors, including behavioral issues, remain an ongoing concern for researchers, practitioners, and policymakers. Aim: This study aimed to analyze both risky and protective riding patterns of young Russian cyclists in relation to cycling safety factors using the Cycling Behavior Questionnaire (CBQ). Methods: This study used the data provided by 374 young Russian cyclists, aged M = 21.6 (SD = 4.8) years, who responded to an electronic survey on cycling behavior and safety outcomes. Results: Beyond demographic characteristics, the key road rule knowledge and risk perception of young cyclists were significantly associated with their self-reported cycling behaviors. Although both traffic violations and riding errors were negatively correlated with safety incidents, predictive analyses show that errors (though not violations) play a predictive role in young cyclists’ safety-related incidents. Conversely, positive cycling behaviors have been shown to be significant reducers of riding crash likelihood. Conclusion: The results of this study support the idea that addressing key issues such as risk perception, road rule knowledge and protective riding habits, while also targeting risky behaviors on the road, could enhance cycling safety outcomes. Additionally, these findings offer valuable insights into understanding the factors contributing to riding risks and crashes among young cyclists, particularly in the context of the increasing need to promote safer and more sustainable urban mobility in a country with a still young cycling culture.

Identification of Learning Resource Access in Improving Student's Knowledge in Linguistic Rules at MIN 3 Tapin

Identification of Learning Resource Access in Improving Student's Knowledge in Linguistic Rules at MIN 3 Tapin

Construction of knowledge constraints: a case study of 3D structural modeling

The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human–machine collaboration and combines structural interpretation to form a new model of complex structural modeling guided by knowledge. Specifically, we focus on utilizing knowledge rule reasoning technology to extract topological semantic knowledge from interpretive data and employ knowledge inference to derive structural constraint information from complex geological structure models, thus effectively constraining the 3D geological structure modeling process. To achieve this, we develop a rule-based knowledge inference system that derives theoretical models consistent with expert cognition from interpretive data and prior knowledge. Additionally, we represent the extracted knowledge as a topological semantic knowledge graph, which facilitates computer recognition and allows estimation of intersection lines during 3D geological modeling, resulting in the creation of accurate models. The applicability of our proposed method to various complex geological structures is validated through application tests using real-world data. Furthermore, our method effectively supports the realization of intelligent structure modeling in real working area.

Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.

A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure

Interval type-2 fuzzy neural networks (IT2FNNs) have gained sustainable attention and wide applications because of their power of adaptive fuzzy modeling. Although the existing models accurately simulate the relationships between inputs and outputs by various adaptive learning algorithms, the parameters and fuzzy rules characterizing the model behaviors no longer have explicit interpretations. To achieve good interpretability and generalization performance, this paper proposes a novel data-knowledge-driven IT2FNN (DKIT2FNN). Unlike accuracy-oriented model fitting, DKIT2FNN pioneers the use of famous association rule mining techniques to automatically explore the interpretable rule knowledge, which intuitively presents conditional relation patterns among features. Using the obtained knowledge, the DKIT2FNN model is accomplished in a single-hidden layer network framework, in which the antecedent structure of each fuzzy rule is determined by the relation patterns provided by rule knowledge. In addition, an adversarial incremental learning algorithm is proposed for the self-organizing modeling of DKIT2FNN. Besides automatically assembling a compact rule base, it integrates the label adversarial training to reinforce the model's capability against label noise. We validate the DKIT2FNN on classification benchmark datasets with label noise and compare it with state-of-the-art non-fuzzy and neuro-fuzzy methods. The results demonstrate that the proposed DKIT2FNN achieves robust classification performance and good interpretability.

Analyzing the Paraphrasing Techniques in Academic Writing Skills and the Paraphrasing Challenges Across Gender

Gender differences is assummed as the general individual’s differences that exists and catches an attention of many aspects in ELT. Writing techniques could have examined and yielded the potential facts in gender differences on the development of writing proficiency in English language learning. The study aims at identifying paraphrase techniques in gender differences and to explore the challenges the students encountered. A Mixed method research design was carried out. The participants of this study were 33 male students and 34 female students for writing test and 5 male students and 5 female students for interview. Data collection invloved the distribution of writing tests to the participants and doing interview. The findings showed that the frequency pertentages for male students NC 14%, Mi.R 27%, Mo.R 28%, SR 31% & for female students NC 31%, Mi.R 21%, Mo.R 29%, SR 37%. The most frequent paraphrase technique employed by male and female students is moderate revision (SR). Then, male students (57,73%) are more dominant than female students (41.32%) in using syntactical paraphrase, male students (37,65%) more frequent than female students (32,62%) in using semantic paraphrase, female students (26.06%) are more frequent than male students (4.62%) in using organizational paraphrase. The paraphrase challenges found on how to organize the ideas, problems of source text comprehension, problems of stylistic styles of writing, grammar or rules of sentences and vocabulary knowledge (Language Proficiency-Based Problems).

Assessing ethical behavior and self-control in elite ultimate championships: a cross-sectional study using the spirit of the game scoring system.

Implementing a self-refereeing system presents a unique challenge in sports education, particularly in academic and training settings where officiated sports prevail. However, Ultimate Frisbee stands out by entrusting players with both athlete and referee roles, introducing distinctive ethical complexities. This manuscript is intended to evaluate ethical behavior and self-control within the Spirit of the Game (SOTG) scoring system in Elite Ultimate. To address these, Ultimate employs the (SOTG) scoring system, integral since the sport's inception in the late 1980s. SOTG aims to enhance and evaluate athletes' ethical conduct. This study evaluates SOTG's effectiveness in elite-level Ultimate, analyzing variations across divisions and age groups in three high-level tournaments. Using a cross-sectional design, data were collected from five international Ultimate tournaments in 2022. Teams spanned diverse age groups (under 17 to over 50) and divisions (women's, mixed, open). Post-match, teams assessed opponents' SOTG in five domains: Rules knowledge, fouls, fairness, attitude/self-control, and communication. Ratings used a 5-point Likert scale ("poor" to "excellent"). An overall SOTG score was calculated by aggregating domain scores. Our study consistently revealed high SOTG scores, reflecting strong sportsmanship. "Positive attitude and self-control" consistently ranked highest, while "Knowledge and use of the rules" scored lowest. Divisional differences in SOTG were statistically insignificant. Notably, WMUCC2022 (participants aged 30+) had significantly higher SOTG scores, possibly indicating age-related self-control improvement or evolving sport culture. Lower rules knowledge scores may stem from linguistic translation challenges. Self-refereeing promotes ethical behavior across divisions and age groups. SOTG underscores sportsmanship's importance and aligns with International Olympic Committee (IOC) and with Sustainable Development Goals (SDGs), particularly SDG 3, 4, 5 and 16 fostering a fairer, healthier, and more peaceful world.

LSVC: A Lifelong Learning Approach for Stream-View Clustering.

Multiview clustering (MVC) can achieve more accurate results by utilizing complementary information from multiple perspectives, compared to traditional single-view methods. However, current multiview techniques require all views to be available upfront, making them inadequate for dealing with prevalent data sources that arrive as streams, such as stem cell analysis and multicamera surveillance. To address this problem, in this article, we propose a method called lifelong stream-view clustering (LSVC), which comprises an embedding anchor knowledge library and three key components, enabling the capability to perform asynchronous clustering on stream views. These three components are specifically: 1) the knowledge extraction module that extracts the abstract knowledge of the newcome view over time and updates the shared knowledge library; 2) the knowledge transfer module that aligns the newcome view with the historical knowledge library, enabling the transfer of structure information to the knowledge library; and 3) the knowledge rule module that constraints the knowledge library to enjoy a fair amount of anchors for each cluster, improving the discrimination of knowledge. The experimental results show that LSVC outperforms traditional single-view clustering (SVC) and MVC methods as it gradually improves with the accumulation of stream views and tends to be stable over time.

Text processing using LLM for automatic creation of agricultural crops knowledge bases

The complexity of subject areas in which intelligent information systems operate is steadily increasing. Tasks assigned to smart agriculture systems are increasingly focused on automating and robotizing areas of human activity. Solving such tasks requires adaptive and flexible methods capable of accommodating dynamic changes in the environment in real-time. The mivar approach to creating intelligent decision-making systems enables working with adaptive discrete structures and provides methods for managerial decision-making based on adaptive active logical inference from the mivar rule knowledge base. The mivar logical inference machine forms the core of expert systems based on the mivar approach. As a result of the development of the mivar approach across various subject areas, different versions of mivar logical inference machines with their algorithms for rule traversal in the knowledge base have been created. Recent advancements in artificial intelligence and machine learning have opened new opportunities for enhancing the mivar approach. The integration of large language models for automating text processing in mivar systems significantly enhances the accuracy and efficiency of decision-making processes based on expert systems for sustainable agriculture. This paper demonstrates the feasibility of using automated text processing, intended for human training, through large language models, and its subsequent application in action planning tasks within technical systems. The proposed methodology is aimed at creating extensive knowledge bases based on textual information for real-time monitoring and decision-making in smart agriculture systems.

What differentiates e-bike riders from conventional cyclists in Denmark? A user-based study

Among the many technology-related advances in two-wheeled mobility, the growing share of e-bikes stands out. Indeed, despite their increased cost and user-related constraints, e-bikes have gained ground in European cities over the last two decades. However, not much is known about the psychosocial and behavioural characteristics of e-bike users compared with conventional bike (c-bike) users. AimsTherefore, the aims of this study were: first, to explore differences between e-bike and c-bike users regarding individual features, cycling habits, and riding behaviour; and second, to identify individual characteristics, cycling habits, and behaviours predicting using an e-bike as the main cycling mode. MethodsThe study used data from 557 Danish cyclists aged over 18, proportionally distributed according to population data (13% e-bike and 87% c-bike users). The cyclists responded to an electronic survey on cycling affairs, addressing trip patterns, risk perception, cycling anger, distractions, and riding behaviours. ResultsOverall, no differences were found regarding personality, distractions, riding errors, or cycling anger. However, e-bike users self-reported significantly lower rates of traffic violations and higher rates of positive behaviours, traffic rule knowledge and road risk perception. Furthermore, structural (SEM) analyses suggest that e-bike riding can be predicted through demographic factors, psychosocial characteristics, and cycling behaviours. ConclusionThe results of this research provide insights into the characteristics and behaviours of e-bike and c-bike users in Denmark, indicating key differences between the two groups in relation to their demographic backgrounds and cycling habits. This underscores the importance of considering demographics, cycling habits, and psychosocial factors when studying and promoting the use of e-bikes.

Domain-specific knowledge and domain-general abilities in children's science problem-solving.

Problem-solving in early and middle childhood is of high relevance for cognitive developmental research and educational support. Previous research on science problem-solving has focussed on the process and strategies of children handling challenging tasks, but less on providing insights into the cognitive network that enables science problem-solving. In this study, we aimed to investigate whether performance in science problem-solving is mainly determined by domain-specific rule knowledge, by domain-general cognitive abilities or both. In our study, 215 6- to 8-year-old children completed a set of three domain-specific rule knowledge tasks and three corresponding problem-solving tasks that were content-coherent, as well as a vocabulary task, and a reasoning task. Correlational and regression analyses revealed a negligible impact of domain-specific rule knowledge on corresponding problem-solving tasks. In contrast, the associations between problem-solving performance in different domains and the associations between problem-solving performance and domain-general abilities (vocabulary and reasoning) were comparably strong. The findings suggest that science problem-solving in primary school children primarily relies on domain-general cognitive abilities. Implications of these findings are discussed with regard to cognitive theories and early science education.

Forward Chaining Method in a Web-Based Bipolar Disorder Expert System

Bipolar disorder is a psychiatric disorder. At Argodadi Pinilih Disability Secretariat, Sedayu District, Yogyakarta Region, around 60% of people with disabilities are affected by psychiatric disorders and one of them is bipolar, one of them is depression, and suicide if initial treatment is not given to the sufferer. In addition, the cost of doing consultation with the medical specialist is not cheap.An expert system is an artificial intelligence system that is useful for diagnosing an error and as a decision-maker with the knowledge rules applied by an intelligent system that can solve problems like an expert. In making an expert system, the forward chaining method is used which aims to be able to diagnose bipolar disorder with accurate results and its utilization can be used by experts and laypeople to make an initial diagnosis of bipolar disorder.The results of this study are in the form of an expert system program that is used to diagnose bipolar disorder which can provide information related to the disease and can provide information on initial treatment of the disease. The information obtained by the consultant from the expert system is in the form of a percentage.

Rough knowledge enhanced dueling deep Q-network for household integrated demand response optimization

Implementing a household integrated demand response (HIDR) can be an effective solution to save energy and reduce carbon emissions in household multi-energy system (HMES). However, the HMES are subject to uncertainties, and HIDR optimization needs to automatically adapt to the uncertainty and make decisions within 15 min. Therefore, we propose an HIDR optimization method combining rough knowledge and a dueling deep Q-network (DDQN). Firstly, we propose an HIDR optimization framework of rough knowledge and DDQN fusion, in which knowledge is utilized to generate good DDQN samples and serves as action guidance value input to DDQN. Secondly, we formulate the household equipment models and knowledge rules. Subsequently, we design the HIDR optimization algorithm that incorporates rough knowledge into DDQN, focusing on dynamic probability of knowledge sample participation in DDQN learning, knowledge serving as DDQN action adviser, knowledge diversification and dynamic adjustment of DDQN’s random exploration probability. Simulation results show that our method saves energy costs by 5.6% and 0.9% compared to rule-based methods and DDQN, respectively. Additionally, based on an i5-10300H CPU, our method’s convergence time is no more than 13 min even for scheduling interval of 15 min. The average convergence time of our method is 29% of DDQN.

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

Definition of the Presumption Rule of "Knowledge" in the Crimes of Assisting in Information Network-Related Criminal Activities

The continuous development of network technology has profoundly transformed the daily production and lives of human beings. However, it has also given rise to numerous new forms of cybercrime, leading to the proliferation of certain cybercrimes. Assisting information network criminal activities constitute a crucial aspect of cybercrime. In 2015, the promulgated Criminal Law Amendment (IX) listed it as an independent offense. However, due to the problem of unclear judicial interpretation, the crime has been in a state of cold storage for a long time until 2019, when the Supreme Peoples Court and the Supreme Peoples Procuratorate promulgated the Interpretation of a Number of Issues Concerning the Application of Law to the Handling of Criminal Cases of Illegal Utilization of Information Networks and Helping Criminal Activities on Information Networks and Other Criminal Cases the crime has gradually begun to be applied in large quantities. In recent years, the crimes of assisting in information network-related criminal activities has been on the rise, but there have been numerous objections and lack of consensus regarding the determination of subjective knowledge in judicial practice and the theoretical community. Therefore, it is crucial to clarify the presumption rule of knowledge to provide guidance for judicial practice.

Internal versus external knowledge sourcing of organizational rules: an exploratory study of CPGs in a healthcare organization

Abstract In this study, we examine how organizational rules source knowledge. By knowledge sourcing of a rule, we mean the formation of reference ties from the rule to knowledge sources located outside of the focal rule. Rules can source knowledge from sources within the organization (e.g., other rules) and outside (e.g., research publications, policies, standards, etc.). Our theoretical model proposes that knowledge sourcing of rules is driven by inherent incompleteness of rules as a result of bounded rationality of rule makers and rule making process. Incomplete rules can lead to experiences of insufficient rule knowledge, termed “knowledge gaps,” which are shaped by rule dynamics at the levels of individual rules, the rule system, and rule networks. Our theoretical model leads to several hypotheses that we test with longitudinal archival data of clinical practice guideline (CPG) changes in a Canadian healthcare organization. The findings support our theoretical model of incomplete organizational rules which encounter knowledge gaps and close them through internal and external knowledge sourcing. The theoretical and practical implications of the findings are discussed.