Reasonable Method Research Articles

Expert System for Assessing Anxiety Levels in Toxic Relationships Using the Case-Based Reasoning Method Based On The Web

Experiences in toxic relationships often trigger significant emotional stress and impact mental health disorders. This study aims to develop a web-based expert system using the Case-Based Reasoning (CBR) method to evaluate anxiety levels caused by toxic relationships. The system is designed to provide more specific treatment by accurately analysing patterns of disorders resulting from toxic relationships. The system's development follows the waterfall model. System testing was conducted using the black-box testing method, demonstrating that the system performs as expected. The results of manual calculations were compared with the system outputs and showed consistency. Testing using 300 cases—80% as training data (240 cases) and 20% as testing data (60 cases)—achieved an accuracy of 91.67%. The recommendations provided include initial steps to manage anxiety. This indicates that the CBR method effectively distinguishes anxiety levels based on similar cases. These findings contribute to clinical psychology by providing a technological tool for quickly identifying anxiety levels. For practitioners, this system can serve as an initial reference before further treatment, while for users, it offers easy access to understanding their mental condition. This system is expected to be an innovative solution supporting accessible mental health care.

Research on the Reform of Assessment under the Blended Teaching Mode: Taking the Teaching Practice of English Stylistics in Guangdong Technology College as an Example

English Stylistics is provided as an elective course by the Business English major at Guangdong Technology College, whose evaluation mechanism significantly impacts how well students learn the language. A novel assessment model is put forth in this study with the intention of transforming from a single summative assessment to a combination of formative and summative assessment in light of the shortcomings of the current assessment methodology in the course. In order to take advantage of blended teaching and integrate the online and offline learning resources of English Stylistics, all-round reforms have been carried out at multiple levels, including teaching content, teaching methods, and assessment approaches. Therefore, a more reasonable and diversified assessment method has been adopted to more comprehensively reflect students’ learning objectives and proficiency in foreign languages.

Armed boundary sabotage: A case study of human malicious behaviors identification with computer vision and explainable reasoning methods

Nowadays, the technologies in computer vision (CV) are labor-saving and convenient to identify human malicious behaviors. However, they usually fail to consider the robustness, generalization and interpretability of calculation frameworks. In this paper, a very common but sometimes difficult-to-detect case research called armed boundary sabotage is conducted, which is achieved by computer vision module (CVM) and reasoning module (RM). Among them, CVM is used for extracting the key information from raw videos, while RM is applied to obtain the final reasoning results. Considering the transient and confusing properties in such scenarios, a specific human-object interaction analysis process with soft constraint is proposed in CVM. In addition, two reasoning methods which are data-based reasoning method and language-based reasoning methods are implemented in RM. The results show that the human-object interaction analysis process with soft constraint prove to be effective and practical, while the optimal testing accuracy achieves 0.7871. Furthermore, the two proposed reasoning methods are promising for identification of human malicious behaviors. Among them, the advanced language-based reasoning method outperforms others, with highest precision value of 0.8750 and perfect recall value of 1.0000. Besides, these proposals are also verified to be high-performance in other external intrusion scenarios of our previous work. Finally, our research also obtain state-of-the-art results by comparing with other related works.

Quantitative Classification and Prediction of Starkrimson Pear Maturity by Near-Infrared Spectroscopy

Scientific evaluation of pear maturity is important for commercial reasons. Near-infrared spectroscopy is a non-destructive method that could be used for rapid assessment of pear maturity. The aim of this study was to develop a reasonable and effective method for the assessment of Starkrimson pear maturity using near-infrared technology. Partial least squares regression and five classification methods were used for analysis of the data. Among the indices used with the competitive adaptive reweighting–partial least squares regression method for quantitation, the visual ripeness index had the best modeling effect (Rp2: 0.87; root mean square error of prediction: 0.39). The classification model constructed with the visual ripeness index and post-ripeness score gave a cross-validation neural network model with the best classification effect and the highest accuracy (classification accuracy: 88.7%). The results showed that combination of quality indices with near-infrared spectroscopy was effective for rapidly evaluating the maturity of Starkrimson pears.

Laboratory Biochemical and Hematological Parameters: Early Predictive Biomarkers for Diagnosing Hepatitis C Virus Infection.

Hepatitis C virus (HCV) infection is a worldwide concern, causing liver damage and necessitating early detection to prevent its spread. Studies indicate that evaluating changes in biochemical and hematological parameters, which serve as suitable predictors of inflammation, can be a reasonable method for diagnosing hepatitis C infection. This study analyzed 100 samples from high-risk patients positively identified via quantitative real-time PCR (qPCR). Anti-HCV titers, biochemical and inflammatory tests, and complete blood cell counts (CBCs) were performed for these individuals. Additionally, 100 HCV-negative individuals with normal laboratory results were selected as the control group. Receiver operating characteristic (ROC) curves were plotted to determine the cutoff values of the laboratory parameters. According to the findings, the age, average white blood cell (WBC) count, platelet-to-lymphocyte ratio (PLR), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), lactate dehydrogenase (LDH), total bilirubin (TBIL), direct bilirubin (DBIL), alkaline phosphatase (ALP), serum glutamic-pyruvic transaminase (SGPT), and ferritin levels were significantly higher in HCV patients. On the other hand, red blood cell (RBC) counts, neutrophils, lymphocytes, hemoglobin-to-platelet ratio (HPR), and iron (Fe) levels were significantly lower in the case group compared to those in the control group (p < 0.05). Furthermore, the ROC curve analysis revealed that lymphocyte count, neutrophil count, and PLR were very strong predictors for hepatitis C infection (p < 0.0001, AUC = 1). The study highlights significant biochemical and hematological differences between HCV patients and healthy subjects. These biomarkers are crucial for early diagnosis, potentially preventing liver damage and reducing HCV transmission.

Investigation on the mechanical uncertainty of resistance spot weldings and its effect on structure crashworthiness of automotive B-pillars under side impacts

Resistance spot welding (RSW) is the main connection method for the white body structure of automobiles. Understanding the mechanical uncertainty of RSWs and its effect on structure crashworthiness design is of great significance for vehicle safety and lightweight design. In this work, the mechanical uncertainty of RSW was experimentally investigated and its effect on automotive B-pillar crashworthiness under side impacts was numerically analyzed based on a hybrid response surface model (HRSM). Firstly, the mechanical uncertainty of RSWs and associated failure mechanism were investigated and the fluctuation range of RSW strength under different loading conditions were quantified according to experimental results. Then, a reasonable RSW modeling method and subsystem collision simulation approach were demonstrated and the univariate sensitivity analysis was carried out to reveal the effect of RSWs’ mechanical property on B-pillar crashworthiness. Finally, a HRSM integrating Kriging polynomial and deep neural network (DNN) were proposed as surrogate model, based on which the multi-factor analysis was conducted to explore effects of RSW strength uncertainty on structure crashworthiness design.

A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Abstract To obtain innovative concepts in the design, designers often need to retrieve and use interdisciplinary knowledge. Concept–knowledge (C–K) theory emphasizes the role of knowledge and introduces the knowledge (K) space and concept (C) space, employing operators to transform the contents between these spaces. Some studies, based on this theory, have successfully provided designers with different forms of knowledge to stimulate concept generation. However, the amount of knowledge provided in these studies is limited, and they fail to offer convenient methods for knowledge retrieval and reasoning, making it challenging to meet the needs of conceptual design across different fields. This paper proposes an enhanced C–K method leveraging large language models (LLMs) to help designers retrieve knowledge and uncover potentially new concepts. Our method redefines the C space and K space within the context of LLMs, dividing the properties of concept into function, appearance, and technology, and requiring the knowledge to correspond to these properties, thereby facilitating a structured connection between concepts and knowledge. Based on this definition, we achieved flexible knowledge retrieval and concept ideation leveraging LLMs. We also conducted a case study on wearable devices to validate our method. The results showed that our method helped designers to retrieve professional knowledge and inspired them to create feasible and innovative concepts.

Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey

Neural symbolic knowledge graph (KG) reasoning offers a promising approach that combines the expressive power of symbolic reasoning with the learning capabilities inherent in neural networks. This survey provides a comprehensive overview of advancements, techniques, and challenges in the field of neural symbolic KG reasoning. The survey introduces the fundamental concepts of KGs and symbolic logic, followed by an exploration of three significant KG reasoning tasks: KG completion, complex query answering, and logical rule learning. For each task, we thoroughly discuss three distinct categories of methods: pure symbolic methods, pure neural approaches, and the integration of neural networks and symbolic reasoning methods known as neural-symbolic. We carefully analyze and compare the strengths and limitations of each category of methods to provide a comprehensive understanding. By synthesizing recent research contributions and identifying open research directions, this survey aims to equip researchers and practitioners with a comprehensive understanding of the state-of-the-art in neural symbolic KG reasoning, fostering future advancements in this interdisciplinary domain.

Proposed Multi-ST Model for Collaborating Multiple Robots in Dynamic Environments

Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement.

Characteristics of Supercritical CO2 Non-Mixed Phase Replacement in Intraformational Inhomogeneous Low-Permeability Reservoirs

Under the influence of the sedimentation process, the phenomenon of intraformational non-homogeneity is widely observed in low-permeability reservoirs. In the development process of water and gas replacement (WAG), the transport law of water and gas and the distribution of residual oil are seriously affected by the non-homogeneity of reservoir properties. In this paper, a study on two types of reservoirs with certain lengths and thicknesses is carried out, and a reasonable development method is proposed according to the characteristics of each reservoir. Firstly, through indoor physical simulation experiments combined with low-field nuclear magnetic resonance scanning (NMR), this study investigates the influence of injection rate and core length on the double-layer low-permeability inhomogeneous core replacement and pore throat mobilization characteristics. Then, a two-layer inhomogeneous low-permeability microscopic model is designed to investigate the model’s replacement and pore throat mobilization characteristics under the combined influence of rhythmites, gravity, the injection rate, etc. Finally, based on the results of the core replacement and numerical simulation, a more reasonable development method is proposed for each type of reservoir. The results show that for inhomogeneous cores of a certain length, the WAG process can significantly increase the injection pressure and effectively seal the high-permeability layer through the Jamin effect to improve the degree of recovery. Moreover, for positive and reverse rhythm reservoirs of a certain thickness, the injection rate can be reduced according to the physical properties of the reservoir, and the gravity overburden phenomenon of the gas is used to achieve the effective development of the upper layers. The effect of the development of a positive rhythm reservoir therefore improved significantly. These findings provide data support for improving the development effectiveness of CO2 in low-permeability inhomogeneous reservoirs and emphasize the importance of the influence of multiple factors, such as injection flow rate, gravity, and rhythm, in CO2 replacement.

Sequential merging and construction of rankings as cognitive logic

We introduce and evaluate a cognitively inspired formal reasoning approach that sequentially applies a combination of a belief merging operator and a ranking construction operator. The approach is inspired by human propositional reasoning, which is understood here as a sequential process in which the agent constructs a new epistemic state in each task step according to newly acquired information. Formally, we model epistemic states by Spohn's ranking functions. The posterior representation of the epistemic state is obtained by merging the prior ranking function and a ranking function constructed from the new piece of information. We denote this setup as the sequential merging approach. The approach abstracts from the concrete merging operation and abstracts from the concrete way of constructing a ranking function according to new information. We formally show that sequential merging is capable of predicting with theoretical maximum achievable accuracy. Various instantiations of our approach are benchmarked on data from a psychological experiment, demonstrating that sequential merging provides formal reasoning methods that are cognitively more adequate than classical logic.

Approximate reasoning based on the preference implication

In fuzzy logic, most of the implication operators are based on generalizations of the classical, material implication. That is, these implications are defined as the disjunction of the negated value of the first argument and the value of the second argument, while the underlying disjunction operators are associative triangular conorms. In our study, we concentrate on how a class of implication operators, called the preference implication operators, can be used in approximate reasoning. Using this implication operator family, we present a novel, Modus Ponens-like approximate reasoning method, in which we have two premises: (1) a statement and (2) a preference implication with an antecedent of this statement. Here, we show how the continuous logical value of the consequent of the preference implication can be derived from the continuous logical values of the premises. We point out that this novel approximate reasoning method is strongly connected with the so-called aggregative operator, which is a representable uninorm. Next, we also present a threshold value-based generalization of the Modus Ponens syllogism and demonstrate that the Modus Tollens syllogism can be generalized in the same way. Lastly, we provide an illustrative example.

Analysis on the Influencing Factors of the Performance of Jindi Real Estate Market

Market performance refers to the price, output, profit, product quality, technical progress and other final economic results of enterprises in a certain market structure through certain market behavior, as well as their impact on the overall social welfare, reflecting the efficiency of the real estate market operation and the advantages and disadvantages of resource allocation. The quality of economic benefits directly reflects the size of the core competitiveness of an industry, and is also the foundation of the existence of the industry. Therefore, it is very important to evaluate the performance of the real estate market. This paper studies the development and influencing factors of Jindi real estate industry, constructs the evaluation index of market performance of Jindi real estate industry, and makes an empirical analysis on the development of Jindi real estate industry by using reasonable evaluation methods. Find out the advantages and disadvantages of Jindi real estate industry development, and formulate appropriate policies and real estate development strategies for Jindi according to the specific development situation.

SecKG2vec: A novel security knowledge graph relational reasoning method based on semantic and structural fusion embedding

Knowledge graph technology is widely used in network security design, analysis, and detection. By collecting, organizing, and mining various security knowledge, it provides scientific support for security decisions. Some public Security Knowledge Repositories (SKRs) are frequently used to construct security knowledge graphs. The quality of SKRs affects the efficiency and effectiveness of security analysis. However, the current situation is that the identification of relational information among security knowledge elements is not sufficient and timely, and a large number of key relational information is missing. In view of this, we propose a security knowledge graph relational reasoning method, based on the fusion embedding of semantic correlation and structure correlation, named SecKG2vec. By SecKG2vec, the embedded vector simultaneously presents both semantic and structural characteristics, and it can exhibit better relational reasoning performance. In qualitative evaluation and quantitative experiments with baseline methods, SecKG2vec has better performance in relationship reasoning task and entity reasoning task, and potential capability of 0-shot scenario prediction.

Ethical reasoning methods for ICT: What they are and when to use them

Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.

Rough sets, modal logic and approximate reasoning

This paper introduces an approximate reasoning method based on rough sets and modal logic. Various Approximate Modus Ponens rules are investigated and defined in Modal Logic systems interpreted in the rough set language. Although this is primarily theoretical work, we expect natural applications of the technique in real-life scenarios. An attempt in this direction is made in a real case analysis to logically model some issues of legal interest.

Elements of Strategic Planning of Patent Protection for Pharmaceutical Inventions

INTRODUCTION. The commercial success of innovations in medicine and pharmaceutics depends on the strategic planning of patent protection, with the main elements for consideration being the type of patent protection, the scope of the invention, and the order of obtaining protection documents.AIM. This study aimed to analyse specific aspects of the strategic planning of patent protection for pharmaceutical inventions.MATERIALS AND METHODS. The study applied analysis and comparison methods to patent information. Statistical data were prepared using patent databases, including the Rospatent search platform and Questel.RESULTS. In the Russian Federation, invention patents for methods of treatment and diagnosis are more abundant than pharmaceutical product patents. The most reasonable method for protecting intellectual property rights in the pharmaceutical industry is the stepwise obtaining of different types of protection documents for different objects of patent protection, including inventions, utility models, and industrial designs, as part of one project. The strategy of patent protection for a pharmaceutical invention should consider the type of patent (protection combining the use of patents for inventions, utility models, and industrial designs), the correct determination of the object of patent protection (obtaining a product (substance) patent), and stepwise obtaining and strategic accumulation of different protection documents.CONCLUSION. The described strategy helps to cover the fullest possible range of rights protection options, prevent patent infringement, and extend the patent protection period for an invention.

VOCABULARY GAMES FOR ELEMENTARY SCHOOL STUDENTS IN ENGLISH CLASSES

Today, learning foreign languages is one of the subjects that our children need to be taught. Teaching English can be a bit more difficult, especially for elementary school students. For this reason, various games and similar pedagogical methods are used in language teaching to young children. This article describes in detail about games that increase the vocabulary of elementary school students in English classes.

Deposition behavior of PbTe doped LBE aerosol and Te valence prediction: platform test and first-principles calculation

In fast reactor investigation with lead-bismuth eutectic(LBE) coolant, understanding the source term within the reactor and its environmental migration is crucial for managing radiation hazards from 210Po aerosols. The numerical simulations using empirical parameters have proffered insights into the theoretical migration and settling rates of 210Po aerosols. However, the scarcity of platform tests has impeded the acquisition of particle size distributions and settling velocity, thus weakening the mutual confirmation between experimental and theoretical validation. In this study, an LBE aerosol testing platform (LATP) was designed and established to obtain the particle concentration data to predict 210Po migration, where Te was employed as an experimental surrogate. The particle concentration and size distribution function of PbTe-doped LBE aerosol were measured by an aerosol spectrometer and a universal scanning mobility particle sizer, revealing the particle size distribution spanning from 0 to 800nm. Under normal operating conditions (873K), the pinnacle particle size of the aerosol concentration is 47nm, which shifted to 41nm under accident conditions (1223K). Notably, the highest mass concentration of particles under both circumstances falls within the 200-300nm range. The settling velocity of PbTe-doped LBE aerosol increase with the particle size, and ranging from 5.0×10-7 to 7.1×10-5m/s. First-principles calculations and X-ray photoelectron spectroscopy results indicate that PbTe-doped LBE aerosols should preferentially generate TeO2 during the interaction with oxygen. This work provide a reasonable prediction method for the migration characteristics of polonium under severe accident.

Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.