RESEARCH ON STRATEGIES OF HOME CARE AND INTERVENTION FOR STROKE BASED ON KNOWLEDGE RULES

For patients with stroke at home, strategies have been formulated for emotional nursing, sports rehabilitation nursing, and interventions for poor lifestyle habits such as smoking, drinking, and picky eating. Data were obtained through tracking investigation, effect evaluation indexes were developed according to Hamilton depression scale (HAMD), activities of daily living (ADL) and other rating scale; C4.5 decision tree algorithm was used to analyze the effect of nursing intervention strategy, then we derived the corresponding knowledge rules. We come to conclusions: ① Effective emotional care and bad living habits interventions are contributed to reduce the risk of stroke. ② Smoking, drinking, picky eating, exercising and other factors are associated, so we should combine and intervene them as to better perfect the risk of stroke to provide decision-making reference for home nursing and rehabilitation intervention of stroke patients.

Creating Regulatory Harmony: The Participatory Politics of OECD Chemical Testing Standards in the Making

In recent decades, the Organisation for Economic Co-operation and Development (OECD) has become a powerful forum for trade liberalization and regulatory harmonization. OECD members have worked to reconcile divergent national regulatory approaches, applying a single framework across sovereign states, in effect determining whose knowledge-making practices would guide regulatory action throughout the industrialized world. Focusing on US regulators, industry associations, and environmental groups, this article explores the participatory politics of OECD chemical regulation harmonization in the late 1970s to early 1980s. These efforts were conditioned by differential institutional access and resources among stakeholders who sought to shape regulatory knowledge rules. Facing competing European and US approaches to chemical data—a minimum “base set” of test data versus case-by-case determinations—OECD members chose the European approach in 1980. However, US regulatory politics shifted with the election of President Reagan, prompting industry associations to lobby the US government to block the agreement. Examining the micropolitics of these standards in the making, I demonstrate that while long-term structures advantaged industrial actors, ideological alignment with the US government precipitated their decisive influence. The case illustrates the importance of attending to the distinctive politics of international harmonization and the effects on transnational knowledge-making and regulatory intervention.

Complexity Computer Simulation in the Study of the Overall Playing Method of Campus Football

With the mutual exchange and integration of world football, modern football is in an increasingly comprehensive direction. This research mainly discusses complexity computer simulation in the study of the overall play of campus football. Complexity computer simulation is used to design the background of the simulated football field, and the area is divided according to the size ratio of the actual football field. Then, it uses drawing software to draw the football and player controls. The construction of the knowledge base of this paper is mainly combined with the functional modules of rapid formation and response tactics. In the fast formation function, the required formation can be quickly given through football experience and knowledge rules. In the applied tactics function, for the responsibilities of forwards, midfielders, defenders, and other roles, the tactics implemented are given, including partially coordinated offensive and defensive tactics, personal offensive and defensive tactics, and set-ball tactics. The “holistic play” football tactics studied in this paper use XML files as recording and playback data, which not only greatly reduce the amount of file data but also make the operation of XML files intuitive and simple. XML can not only realize the recording and playback of player and football track but also be used in the function of rapid formation. The coach uses the “holistic play” football tactics simulation to demonstrate the movement route through the image, guide the players in each position to perceive the game scene by observing the movement route, and analyze and judge the tactical coordination of their respective positions. The computer simulation tactical analysis of the precision of the passing and running and the path coefficient of the passing factor is 0.606 and 0.59, respectively. This research helps to provide guidance on the overall playing tactics of football.

Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference

In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time.

An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

Mass innovation and entrepreneurship (I&E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&E activities, and this calls for accurate and comprehensive evaluation of their I&E thinking ability. Therefore, this paper proposes an evaluation model for the I&E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.

Optimasi Decision Tree menggunakan Particle Swarm Optimization untuk klasifikasi sel Pap Smear

Penelitian ini menyajikan klasifikasi sel Pap Smear. Data yang digunakan adalah dataset Herlev yang berjumlah 917 data. Penelitian ini bertujuan untuk mengetahui apakah penggunaan algoritma optimasi particle swarm optimization dapat meningkatkan kinerja dari algoritma Decision tree dalam mengklasifikasikan data Sel Pap Smear. Tahapan dari penelitian ini adalah preprocessing, feature optimization, knowledge rules, evaluation dan performance report. Hasil dari penelitian ini menunjukkan bahwa algoritma Decision tree menghasilkan akurasi sebesar 91.39 % dengan AUC 0.858, sedangkan penerapan algoritma particle swarm optimization pada Decision tree menghasilkan akurasi yang lebih baik yaitu sebesar 96.76 % dengan AUC 0.926.

Meso Trajectories in the National Innovation System and Their Regulation

The mesotrajectory is presented as a three-phase process of the development of mesopopulations: emergence (origination), diffusion (acceptance, assimilation and adaptation) and retention of a new rule (innovation). The central category of the NIS, i.e. knowledge, is considered from two positions: as a set of specific rules and as the most critical innovation resource. The proposed methodology also describes the three phases of mesostructure dividing each of them into two series–parallel sub-phases and incorporating them in the design of niches, technological and market ones. The methodology allows specifying the effect of the evolutionary selection and intermittent development of meso-units in the first two phases, as well as the mechanisms of changing the socio-technological regime in the third phase. The study set and analyse policy for creating motivation for innovative behaviour at different phases of the mesotrajectory. The actors’ mesopopulation are represented as carriers of the properties of knowledge-rules-resources. The knowledge of the actor is taking into account not only as a rule but a factor breaking the mesotrajectory. Among other characteristics of mesotrajectory discontinuity, intermittent equilibrium is taken into consideration in the study. The problem of regulating trajectory continuity is analysed in the framework of public policy.

Teori Kognitif Sosial Albert Bandura (Studi Kritis Dalam Menjawab Problem Pembelajaran di Mi)

Discussion on the theory of conditioning (behaviorism) which succeeded in the field of learning during the first half of the twentieth century. Beginning in the late 1950s and early 1960s these theories were questioned in many fields. The influence of these theories is decreasing, and currently the more prominent theoretical perspectives are cognitive perspectives. One of the major challenges to behaviorism comes from observational learning studies conducted by Albert Bandura and his colleagues. The most important finding of this research is that people can learn new actions just by watching others do it. The observer does not have to carry out these actions when he learns them. Strengthening is not necessary so learning can occur. These findings refute the central assumptions of conditioning theories. Social cognitive theories that highlight the idea that most human learning takes place in a social environment. By observing others, people acquire knowledge, rules, skills, strategies, beliefs and attitudes. In learning in social cognitive MI, it is important to apply considering that children of this age do a lot of learning from observing the surrounding environment, both observing teachers, parents, and the community as a model, so it is important to create a good environment in learning MI age.Keywords: Social Cognitive Theory, Learning

Process Simulation Framework Design and Validation with Grinding Systems

Process Industrial & their complex control operations require comprehensive simulation software systems for modeling plant dynamics and analyzing gaps and to achieve optimal control efficiency. These models support in training plant engineers on various process scenarios in controlled pseudo real time environment. Higher degree of model designing customization, flexibility, scalability, cost efficiency and domain agnostic solution features, are the desired characteristics of any process simulation framework. This paper formulates prototype design of an integrated generic process simulator platform and its components, enabling intuitive and interactive representation of intelligent model formats, facts, knowledge, rules & behaviors. The benefits range from safer process training, analysis / synthesis of controller models; control optimization and theoretical learning. The simulation performance of proposed framework is verified through material fineness control modeling of rotary vertical grinding mill. The adaptive leaning features, with hybrid prediction model validations results in the simulation accuracy and results are compared with prevalent systems.

Bounded Interval Fuzzy Control for Half Vehicle’s Active Suspension System

A novel efficient control scheme for an active vehicle suspension system is to be designed and simulated in this paper. A half car model has been designed and controlled using two different schemes of standard fuzzy control and bounded interval fuzzy control using MATLAB/SIMULINK. The bounded interval fuzzy control is designed to reduce the uncertainties in the fuzzy sets system and solve the non-linear control problem that the standard fuzzy control cannot handle it. It should be noted that fuzzy logic system is capable of dealing with imprecise concepts and numerous vague but the design of membership functions is nontrivial task. This is because of uncertainty degree that is caused due to road inputs profiles, fuzzy knowledge rules and immeasurable disturbance. The proposed method is expected to be able to mimic the heuristic knowledge of design the membership functions which depends on degree of uncertainty. The membership functions will be generated online during the process in order to deal with uncertainties. The simulation results have demonstrated that the proposed control exhibits better performance and stability as compared to standard fuzzy logic. In addition, the proposed scheme presents a preferable solution and balancing achievement between ride comfort and handling performance. These results demonstrated that the body accelerations and tire dynamic loads will be reduced for the vehicle suspension system in either automobiles or robotics suspension systems.