Knowledge Acquisition Methods Research Articles

The Influence of Digital Communication Technology on University Branding: An Understanding of Open and Distance Learning (ODL) from Students’ Perspectives

Open and distance learning (ODL) refers to flexible educational opportunities regarding access and various knowledge acquisition methods. In the era of digital communication technologies, ODL offers flexible, self-directed learning techniques and comprehensive and engaging learning content. Most students at the Professional and Continuing Education (PACE) Universiti Utara Malaysia are working adults, meaning students would have to balance their employment and studies. It is a competitive industry, with each institution endeavouring to provide exceptional services to its stakeholders. The objectives of our study are 1) to understand students’ perceptions of PACE, particularly in ODL, and 2) to explore the motivation for students’ value co-creation, particularly in ODL. This study employed a qualitative method approach, namely, focus group discussions. The informants were current students from various learning centres (e.g., UUM Sintok, KL, JB, etc.). Our findings discovered four main themes for the perception of ODL students: communication engagement, service-dominant mindset, student experience, and knowledge sharing. Meanwhile, the motivation for students’ value co-creation in ODL is mainly because of attitude, active participants, facilities, satisfaction, brand loyalty and brand image. Our study provides some insights into value co-creation and how to position PACE's reputation in the minds of its stakeholders. As such, our findings were significant for PACE to communicate its corporate branding, and students may be more inclined to study at the university. Keywords: Open and Distance Learning (ODL), digital communication technology, university branding, distant learning students, Malaysia.

Effectiveness of educational interventions in minimizing reactance in traffic accident prevention

IntroductionClassical mechanics concepts are vital for understanding traffic safety, encompassing speed, acceleration, stopping distance, impact speed, deformation, kinetic energy, and their effects on vehicle occupants. Statistical physics principles are also employed to study multiple vehicles as a many-particle system. This study examines the effectiveness of using a damaged car and structured instruction in road safety initiatives.MethodsParticipants engaged with a damaged car and discussed deformation caused by excessive speed. They participated in role simulations, envisioning and solving risky traffic scenarios, while considering the potential boomerang effect resulting from excessive fear appeals within the campaign.ResultsCombining emotional appeals with solution-focused, action-oriented, and self-esteem-enhancing interventions is essential to mitigate the boomerang effect and encourage safety-compliant behavior. Educational institutions must implement tailored follow-ups to emotionally charged prevention campaigns. Using Design-Based Research, we developed effective methods for knowledge acquisition and transfer, drawing from cognitive behavioral therapy and established traffic safety programs.DiscussionThis publication explores non-standardized social training within focus groups, focusing on negative emotional states following an emotionalizing campaign similar to “Crash Kurs NRW,” targeting upper-level courses in North Rhine-Westphalian schools. Methods from cognitive behavioral therapy and social learning initiated, observed, and documented changes in participants’ norms, values, and attitudes, discussed herein. Additionally, we investigated whether the presence of police officers in prevention campaigns influences reactive behavior. Comparing pre- and post-surveys confirmed reactance after emotionalizing campaigns, and our structured post-survey effectively influenced stage-event-induced behavior. Surprisingly, police officers’ presence did not significantly impact reactive behavior. Furthermore, our follow-up tool extends its applicability to similar emotionalizing prevention campaigns, broadening its potential reach. Our future research will delve into students’ physics literacy during traffic space analysis of accident hotspots, advancing traffic safety initiatives.

A preliminary epistemological ″human ecology″ framework for understanding the limits of bioethics.

This article tries to set up the epistemological bases of the science of ″human ecology″. This term has started to be used as a synonymous of morality, especially in the Catholic moral social doctrine that used for the first time to justify its marriage prospectives. We look at both terms together (human plus ecology) and we propose that human ecology should be a discipline that in the first time study human behavior and population (objective) using the postulates of the science of ecology (method) and then, once a conceptual framework for social sciences disciplines such as bioethics can be settle, could be used as a way to support or not moral postulates in the name of ecology. We conclude by defining which should be the methods of knowledge acquisition, the limits and the validity of what should be considered ″Human ecology″, that is to say, the ecology of the humans.

Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data

Abstract. Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k-means, and it has been used to detect hidden patterns in climate systems; k-means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.

Understanding Knowledge Acquisition, Adaptive Strategies, Challenges, and Preservation Methods Among Traditional Fishermen in a Digital Age

The Riau Islands (Kepri) province of Indonesia boasts an expansive ocean, covering 96% of its territory and fostering a rich heritage of knowledge and maritime culture among its citizens. Traditional fishing communities on Riau Island have thrived for generations, drawing on ancestral wisdom and local expertise. However, the swift rise of digitalization and modernization has triggered a transformative shift in their learning methods and adaptation strategies. This study focuses on: identifying methods of knowledge acquisition, investigating adaptive strategies, unveiling challenges, and exploring knowledge preservation techniques. Through in-depth interviews with more than two decades of experienced fishermen, qualitative data was gathered and meticulously analyzed using NVivo 12 software. The result found that traditional fishermen employ diverse strategies, integrating technology, continuous learning, and data-driven decision-making. They face challenges like limited internet access, cultural traditions, and safety concerns. Yet they preserve wisdom through oral traditions, apprenticeships, and modern tools, ensuring a vibrant fishing legacy. Their resilience highlights the urgent need for digital adaptation and intergenerational dialogue to shape sustainable and innovative fishing communities. Hence, this study recommend to develop sustainable training initiatives, establish community platforms, formulate policies to enhance digital accessibility, and encourage meaningful conversations across generations within traditional fishing communities.

Study and analysis of various link predictions in knowledge graph: A challenging overview

Knowledge Graph (KG) is the network which contains some topic-based entities, called nodes, and the associated information among the entities. Here, the concept in the knowledge graph is denoted by the tuple relationship, such that the ⟨entity1, predicate, entity2⟩. The entity in the knowledge graph is the abstract concepts based on the particular object, namely the organization, dataset, people, and some associated documentation. The big issue in the KG is that it consists of some incomplete information. The missing details can be identified by employing the knowledge graph completion (KGC) solution. KGC is the same as the link prediction concepts in the knowledge graphs. However, this concept is more complex that it does not predict the link relationship among the nodes but also the diversified information from the link relations. Hence this survey analyzes different methods of link prediction techniques, and this review provides a detailed review of 50 research papers concentrating on various methods, like embedding-based methods, deep learning methods, knowledge acquisition methods, ranking methods, and representation learning methods. The analysis is carried out with respect to the survey based on the publication year, research techniques, performance measures, dataset, toolset and achievement of the research methodologies. Also, the problems in the methods are explained in the research gaps and issues. Furthermore, the future extent of these research works is done based on the limitations identified from the existing research methods.

INFORMATION CULTURE IN PROFESSIONAL TRAINING OF FUTURE TEACHERS

The article provides an overview on the problem of the introduction of information and telecommunication technologies in the modern educational system, which demonstrates new opportunities for optimizing the learning process, particularly language learning, at all levels. The choice of the topic, its relevance, the theoretical significance and practical value of the investigated problem are substantiated. A special attention is paid to the Internet, web technologies, possibilities of mass media, active use of the didactic potential of the virtual network. The authors consider the possibilities of training new generation teachers who possess not only traditional, but also innovative learning technologies, who are able not only to process certain information, but also to produce it, creatively using the time to solve educational tasks. The proposed work is devoted to the problem of forming the information culture of future teachers, their readiness to use the latest information technologies in their professional activities; organization of the educational process using modern information and communication technologies. It was found that a modern lesson today is impossible without involving the powerful capabilities of Internet resources, the usage of which contributes to the formation of a truly modern student, armed with the latest technologies for searching, receiving and using cognitive information. It was determined that the formation of readiness to work with information technologies in professional activity is a long process, which is an important component of the professional pedagogical training of future specialists, therefore this process takes place through purposeful activity, the future teacher's own activity and special conditions. The following conditions are considered as the most important for the formation of future teachers’ information culture: student’s involvement in active information activities; professionalism of the teacher; technical equipment; active learning methods; arming students with rational methods of knowledge acquisition. Using of technical tools in education allows solving the following tasks: the usage of innovative information technologies and their compliance with the conditions of modern educational technology; increasing visibility of methodical materials in education; improving the organization of students’ independent cognitive activities; the usage of global and regional systems; application of interactive learning methods.

Ten-year mixed-method evaluation of prelicensure health professional student self-reported learning in an interfaculty pain curriculum.

Introduction:Student perspectives on interprofessional pain education are lacking.Objectives:The purpose of this study was to evaluate ratings of knowledge acquisition and effective presentation methods for prelicensure health professional students attending the University of Toronto Centre for the Study of Pain Interfaculty Pain Curriculum (Canada).Methods:A 10-year (2009–2019) retrospective longitudinal mixed-methods approach comprising analysis and integration of quantitative and qualitative data sets was used to evaluate 5 core University of Toronto Centre for the Study of Pain Interfaculty Pain Curriculum learning sessions.Results:A total of 10, 693 students were enrolled (2009–2019) with a mean annual attendance of 972 students (±SD:102). The mean proportion of students rating “agree/strongly agree” for knowledge acquisition and effective presentation methods across sessions was 79.3% (±SD:3.4) and 76.7% (±SD:6.0), respectively. Knowledge acquisition or presentation effectiveness scores increased, respectively, over time for 4 core sessions: online self-study pain mechanisms module (P = 0.03/P < 0.001), online self-study opioids module (P = 0.04/P = 0.019), individually selected in-person topical pain sessions (P = 0.03/P < 0.001), and in-person patient or interprofessional panel session (P = 0.03). Qualitative data corroborated rating scores and expanded insight into student expectations for knowledge acquisition to inform real-world clinical practice and interprofessional collaboration; presentation effectiveness corresponded with smaller session size, individually selected sessions, case-based scenarios, embedded knowledge appraisal, and opportunities to meaningfully interact with presenters and peers.Conclusion:This study demonstrated positive and increasing prelicensure student ratings of knowledge acquisition and effective presentation methods across multifaceted learning sessions in an interfaculty pain curriculum. This study has implications for pain curriculum design aimed at promoting students' collaborative, patient-centered working skills.See commentary: Trouvin A-P. “Ten-year mixed method evaluation of prelicensure health professional student self-reported learning in an interfaculty pain curriculum”: a view on pain education. PAIN Rep 2022;7:e1031.Students attending learning sessions at the University of Toronto Interfaculty Pain Curriculum (2009–2019) in Toronto, Canada, self-report high ratings of knowledge acquisition and effective presentation methods.

Attribute reduction in intuitionistic fuzzy formal concepts

Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life.

Python-assisted biological knowledge acquisition method to trigger design inspiration

Design inspiration comes from the continuous stimulation of external information and the continuous accumulation of knowledge. In order to obtain an ideal design inspiration from nature, researchers have proposed a large number of biological information retrieval and knowledge acquisition methods. But how to purposefully acquire valuable biological knowledge in order to effectively stimulate design inspiration and produce the novel and feasible designs idea is still an urgent problem to be solved. This paper proposes a method for acquiring valuable biological knowledge to efficiently stimulate inspiration and quickly conceive solutions in engineering design. First, keywords, such as the functional requirements and key components of design objects, are selected as the engineering terminologies. Next, biological keywords related to the engineering terminologies are searched from the biological dictionary and biology websites. Then in order to retrieve enough biological knowledge, these biological keywords are expanded manually and automatically respectively based on Thesaurus Webpage and WordNet database, and expanded keywords are filtered according to repeated words and different forms of the same words. Finally, in the biological knowledge base, biological keywords that had been filtered are used to obtain biological knowledge with Python web crawler programming. Through an example of application for ship equipment, the effectiveness of the method is verified.

The use of Fuzzy rule-based systems in the design process of the metallic products on example of microstructure evolution prediction

The challenge on the contemporary market of consumer goods is a quick response to customer needs. It entails time restrictions, which a semi-finished products’ (including metal products) manufacturer must meet. This issue must be addressed during a design phase, which for the most of semi-finished products suppliers, takes part during a quotation preparation process. Our research is aimed at investigating possibility of application of Fuzzy Reasoning methods for shortening of a design process, being a part of this process. We present a study on application of simplified models for solving technological tasks, allowing obtaining expected properties of designed products. The core of our concept is replacing numerical models and classical metamodels with a rule-based reasoning. A quotation preparation process can be supported by solving a technological problem without numerical experiments. Our goal was to validate the thesis basing not only on the presentation of some potential solutions but also on the results of simulation studies. The problem is illustrated with an example of thermal treatment of aluminum alloys, aimed at evaluation of a summary fraction of precipitations as a function of time and technological parameters. We assumed that it is possible to use both unstructured and point numerical experiments for knowledge acquisition. Implementation of this concept required the use of hybrid knowledge acquisition methods that combine the results of point experiments with expert knowledge. A comparison of obtained results to the ones obtained with metamodels shows a similar efficiency of both approaches, while our method is less time and laborious.

Linguistic and Cultural Knowledge Acquisition in Terms of the Multimodal Approach To EIL Studies

The problem of linguistic and cultural knowledge correlation in terms of English as an International Language learning deserves special attention as one of the topical issues in reference to the new methods of knowledge and skills acquisition, required in the present-day global society. Language learning determined by particular cultural and conceptual bases faces new challenges in the case of EIL, as culture should be discussed within different terms in this connection and serve to achieve different educational goals. Within this context, culture is referred to as both the subject of studies and the means of learning. It is used as the source of information for EIL learners and helps to create a multicultural environment to facilitate the process of intercultural professional communication. Culture can be learned both from the point of view of similarity and its difference, which correlates with the basic tendencies in semiosic systems development. The use of modern information technologies as well as the lifetime learning mode in which modern citizens live and work seeks new approaches and methods of knowledge acquisition. The multimodal approach to language and culture deserves special attention from the viewpoint of EIL learning as it implicates elaboration of certain methodology that covers the whole complex of modes based on the use of the human senses abilities as well as different semiotic systems including language. The approach in question seems to be used for the purpose of educational process optimization, especially with reference to the purposes of EIL studies and the role of culture in this concern. To analyze the problem in detail and to demonstrate the validity of cultural knowledge application within EIL learning and teaching, the survey that included 3 questionnaires has been carried out on the basis of the materials presented by 62 linguistic (English philology) and 60 non-linguistic (Social sciences) students. The answers contained in the questionnaire enabled the authors to come to the conclusions as referred to the validity of multimodal approach application with respect to cultural and linguistic skills and knowledge acquisition within the scope of EIL learning and teaching.

Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm

In order to overcome the problems of poor clustering effect and large error of text knowledge acquisition in traditional text knowledge acquisition methods, a new text knowledge acquisition method of collaborative product design based on genetic algorithm is proposed in this paper. The definition of collaborative product design text knowledge clustering is given. According to the operation process of the genetic algorithm, the chromosomes of clustered text are constructed and encoded and the initial population is obtained. The fitness function of clustering is constructed by the DB index evaluation method; the selection, crossover, and mutation operators in the genetic algorithm are determined; and the objective function of collaborative product design text knowledge clustering is constructed. After the text knowledge clustering is completed, the text knowledge data of collaborative product design are obtained in an all-around way by using the method of rough set and neural network. The experimental results show that compared with the traditional text knowledge acquisition methods, the clustering effect of the proposed method is better and the text knowledge error is reduced up to 0.02.

A Knowledge Acquisition Method of Ship Coating Defects Based on IHQGA-RS

Coating defects are caused by a series of factors such as the improper operation of workers and the quality of the coating itself. At present, the coating process of all shipyards is inspected and recorded at a specific time after construction, which cannot prevent and control defects scientifically. As a result, coating quality decreases, and production costs increase. Therefore, this paper proposes a knowledge acquisition method based on a rough set (RS) optimized by an improved hybrid quantum genetic algorithm (IHQGA) to guide the ship-coating construction process. Firstly, the probability amplitude is determined according to the individual position of the population, and the adaptive value k is proposed to determine the rotation angle of the quantum gate. On this basis, the simulated annealing algorithm is combined to enhance the local search ability of the algorithm. Finally, the algorithm is applied to rough set attribute reduction to improve the efficiency and accuracy of rough set attribute reduction. The data of 600 painted examples of 210-KBC bulk carriers from a shipyard between 2015 and 2020 are randomly selected to test the knowledge acquisition method proposed in the paper and other knowledge acquisition methods. The results show that the IHQGA attribute approximate reduction algorithm proposed in this paper is the first to reach the optimal adaptation degree of 0.847, the average adaptation degree is better than other algorithms, and the average consumption time is about 10% less than different algorithms, so the IHQGA has more vital and more efficient seeking ability. The knowledge acquisition result based on the IHQGA optimization rough set has 20–50% fewer rules and 5–10% higher accuracy than other methods, and the industry experts have high recognition. The knowledge acquisition method of this paper is validated on a hull segment. The obtained results are consistent with the expert diagnosis results, indicating that the method proposed in this paper has certain practicability.

Fault Tolerant Control Based SFO-Intelligent PID: Application on SynRM for Flywheel Energy Storage Systems -Part I

Reliability, safety, and fault-tolerant control (FTC) for power systems are substantially raised in several industrial applications. It is critical to correctly diagnosis the faults occurring in a drive system to avoid harmful accidents and to ensure continuity of operation. It is necessary to design proper control techniques to recover the faulty system’s performance/functionality to its nominal level. This research work divided into two parts a design scheme of intelligent fault-tolerant control based artificial intelligence techniques (AI) is described and implemented integrates common knowledge acquisition methods with techniques developed in the fields of model-based diagnosis (MBD) to reach the so-called towards intelligent fault tolerant control to provide reliability and maintain the stability of the system in desired performance automatically; This paper presents the first part of this scheme proposes stator-flux oriented (SFO) control with GA-tuned proportional-integral-derivative (PID) controller. The principle is to set the q axis component of stator current invariant during healthy and faulty operating mode. The overall development scheme is summarized and an example illustrates features of the methods performed on a 1.0kW SynRM drive. Results presented have shown that the FT controller scheme developed is potentially capable of dealing with a larger number of faults and can successfully keep a good dynamic performance during faulty mode.

Training needs of N-Power Agro-beneficiaries in Kwara State, Nigeria

&#x0D; &#x0D; &#x0D; This study assessed the training needs of N-Power Agro beneficiaries in Kwara State. Simple random sampling was used to select 216 respondents using a questionnaire to collect data while percentages, mean, standard deviation and Pearson Product Moment Correlation (PPMC) were used to analyze the data. Training of respondents was assessed using job/task and skill gap analysis. Results showed that rules and regulation governing the job (x̅ = 5.69), knowledge acquisition (x̅ =1.99), field practices (x̅ =1.70), record keeping (x̅ =1.69), and extension service delivery methods (x̅ =1.59) were the areas where training were needed. Extension service delivery methods (x̅ =3.17) was identified as the only gap which training could address. It was found that age (r=0.341), sex (r=0.095) and educational status (r=0.180) were favorable socioeconomic factors determining training needs of respondents at p≤0.05 level of significance. A significant gap exists in what the beneficiaries already know and what they now need to know as they have not been undergoing adequate capacity building training; hence there is a dire need for intensive training to cover the existing gaps for effective productivity of the N-agro beneficiaries in Kwara State.&#x0D; &#x0D; &#x0D;

Does the Preferred Study Source Impact Orthopedic In-Training Examination Performance?

This study examines the role of electronic learning platforms for medical knowledge acquisition in orthopedic surgery residency training. This study hypothesizes that all methods of medical knowledge acquisition will achieve similar levels of improvement in medical knowledge as measured by change in orthopedic in-training examination (OITE) percentile scores. Our secondary hypothesis is that residents will equally value all study resources for usefulness in acquisition of medical knowledge, preparation for the OITE, and preparation for surgical practice. 9 ACGME accredited orthopedic surgery programs participated with 95% survey completion rate. Survey ranked sources of medical knowledge acquisition and study habits for OITE preparation. Survey results were compared to OITE percentile rank scores. 386 orthopedic surgery residents SETTING: 9 ACGME accredited orthopaedic surgery residency programs RESULTS: 82% of participants were utilizing online learning resources (Orthobullets, ResStudy, or JBJS Clinical Classroom) as primary sources of learning. All primary resources showed a primary positive change in OITE score from 2018 to 2019. No specific primary source improved performance more than any other sources. JBJS clinical classroom rated highest for improved medical knowledge and becoming a better surgeon while journal reading was rated highest for OITE preparation. Orthopedic surgery residents' expectation for OITE performance on the 2019 examination was a statistically significant predictor of their change (decrease, stay the same, improve) in OITE percentile scores (p<0.001). Our results showed that no specific preferred study source outperformed other sources. Significantly 82% of residents listed an online learning platform as their primary source which is a significant shift over the last decade. Further investigation into effectiveness of methodologies for electronic learning platforms in medical knowledge acquisition and in improving surgical competency is warranted.

Decision-making technology based on knowledge engineering and experiment on the intelligent water-fertilizer irrigation system

Although the irrigation technologies based on the Decision-making System (DMS) began in the late 1990s, while being merely embryonic from laboratory research into application in the agricultural irrigation areas, DMS based on intelligent algorithms have drawn much attention from the academia over the recent years. In this study, we have provided an overview of the decision-making technology based on knowledge engineering for intelligent irrigation system referred to as Knowledge-based Engineering (KBE). As the modern technical research and scientific theory on agricultural water saving is further developed, the water-fertilizer irrigation is becoming increasingly intelligent. We have put forward the concept of KBE intelligent irrigation system and its support to decision-making in the study, while adopting the techniques and methods of knowledge engineering. In addition, we have combined our research findings with the expert knowledge on the water-fertilizer irrigation in a system integrated with computer network, intelligent reasoning and artificial intelligence (AI), among other modern high-techs. We have set up the decision-making models and analytical methods of irrigation and fertilization for KBE by referring to the expert experience and data of fertilization. Moreover, we have taken into account the web crawler technology in irrigation and fertilization, and we have put forward novel methods of knowledge acquisition based on the web crawler. Correspondingly, we have established the knowledge base for the decision-making support system tailored to irrigation and fertilization. The experiment result shows that the recommended irrigation quota is compared with local cultivation technology experience to obtain a decision accuracy of 81.7%. And the water and fertilizer management plan obtained by the intelligent decision-making system has a thicker stem and higher plant height during the growth period than the crops obtained by local cultivation experience. The output of the decision-making system is 620 kg, which a relative increase of 5.08% is compared with the 590 kg obtained from local cultivation experience.

Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

A Study on Influence of Psychological Capital of Chinese University Teachers Upon Job Thriving: Based on Motivational Work Behavior as an Intermediary Variable

With the continuous development of the information process, a series of revolutionary changes are occurring in universities such as knowledge acquisition methods, teaching methods, as well as the relation between teaching and learning, which lead to new and higher requirements for teaching staff. Combining the development requirements of China’s new era, starting from the perspective of capital development in universities, we should discuss the influence of universities teachers’ psychological capital on job thriving. Through the literature, we found that the psychological capital of universities teachers cannot affect the job thriving directly. Therefore, we would try to find an intermediary variable-motivational work behavior and establish an intermediary model to study the relationship between variables. Through the establishment of models, the use of empirical analysis methods, analysis the data from electronic questionnaires, use software analysis, and we reveal the role of university teachers’ psychological capital on the job thriving, and how to improve teachers’ job thriving, the organization brings greater benefits to provide countermeasures and suggestions and provides more new enlightenment for the management of universities.