Knowledge Model Research Articles

Knowledge and handling practices for raw hen's eggs during purchase, preparation, storage, and consumption: a cross sectional study

The study aimed to assess the hen’s eggs knowledge, handling practices, and consumption among Palestinian consumers using the cross-sectional survey design. Online questionnaire was sent via social media platforms including well-known Facebook and WhatsApp groups in West bank, Gaza and Jerusalem, along with an invitation explaining the research objectives and the voluntary participation. The online questionnaire consisted of three main sections: socio demographic, knowledge and practices for hen’s eggs during purchase, preparation, storage, and consumption. The items were developed based on previous literature and international guidelines for hen's eggs purchase and handling, and subjected to content validity process, followed by a pilot study to determine the reliability of the questionnaire using Cronbach’s alpha test. The final data were analysed based on the dichotomous Rach model for knowledge and polytomous Rasch model for practices using Winsteps version 5.1.3. The Rasch SPSS output interval data files were used in the inferential analyses tests (one-way ANOVA test and independent samples t-test). The Rasch analysis showed that on average the participants had quite low level of knowledge of hen's eggs consumption safety, (person ability Mean 0.10 logit). The item difficulty measures ranged between + 1.65 (most difficult) and − 2.24 logit (easiest). It was not easy for the participants to endorse the items pertaining their practices in handling the hen’s eggs during purchase, preparation, storage and consumption, (person ability Mean − 0.11 logit). The item difficulty measures ranged between + 2.68 logits (most difficult) to − 2.45 logit (easiest). In addition, female participants significantly outperformed males in knowledge scores; and the level of hen's eggs hygiene practices and storage during purchase depended on participants’ knowledge level. The research recommended interventional programs to enhance Palestinians’ awareness and knowledge about hen's eggs knowledge, handling practices, and consumption. Further quantitative and qualitative research studies were also recommended.

A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling

Abstract Accurate streamflow prediction is essential for optimal water management and disaster preparedness. While data-driven methods’ performance often surpasses process-based models, concerns regarding their ‘black-box’ nature persist. Hybrid models, integrating domain knowledge and process modeling into a data-driven framework, offer enhanced streamflow prediction capabilities. This study investigated watershed memory and process modeling-based hybridizing approaches across diverse hydrological regimes – Korean and Ethiopian watersheds. Following watershed memory analysis, the Soil and Water Assessment Tool (SWAT) was calibrated using the recession constant and other relevant parameters. Three hybrid models, incorporating watershed memory and residual error, were developed and evaluated against standalone long short-term memory (LSTM) models. Hybrids outperformed the standalone LSTM across all watersheds. The memory-based approach exhibited superior and consistent performance across training, evaluation periods, and regions, achieving 17–66% Nash–Sutcliffe efficiency coefficient improvement. The residual error-based technique showed varying performance across regions. While hybrids improved extreme event predictions, particularly peak flows, all models struggled at low flow. Korean watersheds’ significant prediction improvements highlight the hybrid models’ effectiveness in regions with pronounced temporal hydrological variability. This study underscores the importance of selecting a specific hybrid approach based on the desired objectives rather than solely relying on statistical metrics that often reflect average performance.

History Matters: Temporal Knowledge Editing in Large Language Model

The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated. Prevailing efforts in model editing conflate these two distinct categories of edits arising from distinct reasons and directly modify the original knowledge in models into new knowledge. However, we argue that preserving the model's original knowledge remains pertinent. Specifically, if a model's knowledge becomes outdated due to evolving worldly dynamics, it should retain recollection of the historical knowledge while integrating the newfound knowledge. In this work, we introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe (Assessment of TempOral Knowledge Editing) to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models, which edits both historical and new knowledge concurrently and optimizes the model's prediction for the time of each fact. Our assessments demonstrate that while AToKe is still difficult, METO maintains the effectiveness of learning new knowledge and meanwhile substantially improves the performance of edited models on utilizing historical knowledge.

What Are the Rules? Discovering Constraints from Data

Constraint programming and AI planning are powerful tools for solving assignment, optimization, and scheduling problems. They require, however, the rarely available combination of domain knowledge and mathematical modeling expertise. Learning constraints from exemplary solutions can close this gap and alleviate the effort of modeling. Existing approaches either require extensive user interaction, need exemplary invalid solutions that must be generated by experts at great expense, or show high noise-sensitivity. We aim to find constraints from potentially noisy solutions, without the need of user interaction. To this end, we formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we select the model with the best lossless compression of the data. Solving the problem involves model counting, which is #P-hard to approximate. We therefore propose the greedy URPILS algorithm to find high-quality constraints in practice. Extensive experiments on constraint programming and AI planning benchmark data show URPILS not only finds more accurate and succinct constraints, but also is more robust to noise, and has lower sample complexity than the state of the art.

Augmented Commonsense Knowledge for Remote Object Grounding

The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as “Bring me the blue cushion in the master bedroom”. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. The source code is available at https://github.com/Bahram-Mohammadi/ACK.

Community Awareness Model Based on Integrity, Attitude, Knowledge and Perception Towards Solid Waste Management and Environmental Care

Globally, the solid waste management (SWM) and environmental care (EC) are critical issues that require significant attention. Increasing community Awareness is one solution to address these challenges. Community awareness on SWM and EC refers to a community's knowledge and understanding of the importance of proper waste management and environmental care. The objectives in this study are: i) to identify the strength of relationships between variables and ii) to develop a multiple linear regression model for community Awareness, Integrity, Attitude, Knowledge, and Perception. A total of 254 respondents from UiTM Perak Branch, Tapah Campus participated by completing an online questionnaire, with 70.87% female and 29.13% male respondents. The study used a simple random sampling technique and applied correlation analysis and multiple linear regression. The results showed moderate associations (0.4 < r < 0.7) between community Awareness and Integrity, Attitude, Knowledge, and Perception regarding SWM and EC. The multiple linear regression model was found to be significant with a p-value of less than 0.05 where all variables were significantly contributed on community Awareness.

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling.

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

A participatory learning management model of indigenous knowledge for creative economy of Karen in Thai-Myanmar Border

ABSTRACT The Karen is the largest hill tribe in Thailand, living in densely forested areas on the Thai-Myanmar border for centuries. This study aims to develop a participatory learning management model of indigenous knowledge for creative economy. The qualitative research purposively selected participants based on a village complex of four Karen major subgroups including Pga K’nyau (White Karen), Pwo (Red Karen), Pa-O (Black Karen), and Kayah (Long Neck Karen). This participatory action research applied three techniques including Participatory Rural Appraisal (PRA), Appreciation Influence Control (AIC), and Future Search Conference (FSC) to provide opportunities for participants to take part in this study. The findings found that a participatory learning management model of indigenous knowledge for creative economy requires knowledge integration and holistic learning processes. The knowledge integration and holistic learning processes must be sustainable in several areas including: being environmentally friendly, building pride, adding values, as well as maintaining identity. This paper offers policy implications on indigenous education.

A Knowledge and Evaluation Model to Support the Conservation of Abandoned Historical Centres in Inner Areas

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity ITI and degradation ID of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge.

The effect of augmented reality applied to learning process with different learning styles in structural engineering education

PurposeThe purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.Design/methodology/approachUsing Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.FindingsThe findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.Originality/valueThese results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. 1

How to maximize the impact of workplace training: a mixed-method analysis of social support, training transfer and knowledge sharing

ABSTRACT Training is crucial for developing employee competences and improving organizational performance. However, to ensure the effectiveness of training, it is essential that the newly acquired knowledge is applied (training transfer) and shared within the organization (knowledge sharing). To explore this further, the first study involved a survey of 541 employees who had undergone workplace training within the past two years. By using a joint model for training transfer and knowledge sharing, it was discovered that both factors had a significant impact on individual organizational results and identified motivation and volition as mediators. Furthermore, we found that both peer and supervisor support functioned as antecedents for training transfer and knowledge sharing. In the second study, the role of social support was further investigated by exploring how knowledge networks change through training and how knowledge is shared and acquired through social connections after training. Qualitative interviews were conducted with 51 participants and recorded their knowledge networks before and after training. Our findings indicate that participants primarily gain new contacts within their own organization, and that transfer of training predicts the change in the number of contacts in the network.

Public health surveillance and the data, information, knowledge, intelligence and wisdom paradigm.

This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is undesirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors' enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training - from data to knowledge to wisdom - into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.

A Survey on the Knowledge and Awareness of Testicular Cancer and Testicular Self-Examination Among Men in Saudi Arabia.

Objectives The aim of the study was to assess the knowledge and awareness of testicular cancer (TC) and testicular self-examination (TSE) and to identify the associated factors in men in Saudi Arabia. Methods An online questionnaire-based study was conducted in Saudi Arabia with a representative sample of 794 participants. The questionnaire comprised knowledge, awareness and attitude questions regarding TC and TSE in addition to signs/symptoms and risk factors.Multivariate logistic regression analysis was conducted to identify the significant variables associated with knowledge of TC and TSE. Results Around 43% (n=340) of the participants had inadequate knowledge of TC, whereas 26% (n=205) had heard about TSEand only 65 (8.2%) performed TSE. The first model for knowledge found that participants with a low level of education [2.75 (1.18, 6.42)]; no past history of a testicular problem [2.20 (1.22, 3.95)] and those who had not heard about TSE [1.79 (1.24, 2.57)] were at higher odds for inadequate knowledge, whereas those whose mothers had received college-level education [0.39 (0.19, 0.79)] and those who received information from school/college [0.61 (0.37, 0.97)] were more likely to have adequate knowledge about TC. The secondmodel for TSE found that a low level of education 5.24 (1.34, 20.52) was associated with not performing TSE. Receiving information from social media [0.08 (0.03, 0.17)], school/college [0.06 (0.02, 0.13)], family and friends [0.17 (0.05, 0.57)] and medical staff [0.08 (0.03, 0.17)] were associated with higher odds of performing TSE. Conclusion The majority of Saudi males have knowledge about TC. On the contrary, only a small percentage of the respondents have heard of or performed TSE as a screening technique. Educated sources of information can be a reliable way of giving correct knowledge on sensitive topicslike TSE.

Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that BERTweet outperforms the standard BERT model and exhibits similar performance to RoBERTa in crisis tweets. Furthermore, we employ natural language processing techniques to extract key subject entities (e.g., police, victims) and leverage the publicly available commonsense knowledge model, COMET-ATOMIC 2020, to identify their intentions in given crisis scenarios. Evaluation of COMET-ATOMIC 2020 on subject-based intent prediction in crisis tweets reveals that BART was superior to GPT2-XL model, providing crisis responders with vital information for better decision making. Notably, the integration of sentiment and emotion classification, identification of attention words and subject-based intent prediction represents a novel methodology, not previously applied in the context of crisis scenarios.

BIM-based automated fault detection and diagnostics of HVAC systems in commercial buildings

In order to meet the growing demand for effective Automated Fault Detection and Diagnostics (AFDD) for HVAC systems, innovative approaches are needed to address limitations in data diversity and access to contextual information. This study introduces a methodology that leverages Building Information Modeling (BIM) to enhance the development of the AFDD model. Feature engineering techniques are utilized to generate dynamic BIM features, compensating for the lack of sensory and contextual data in Building Management Systems (BMS). By integrating AFDD analytics with BIM, a comprehensive digital twin of the facility is created, which enables facility managers to compare, reuse, and develop AFDD models for HVAC systems. The proposed methodology demonstrates the potential of leveraging BIM-based knowledge models to overcome the challenges associated with the limited sensor and contextual information availability by utilizing BIM for feature generation and, conversely, updating the BIM model with AFDD analytics.

MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation

As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry.

PEMBANGUNAN APLIKASI JAWLAH TIKTOQ BAGI MENINGKATKAN PENGUASAAN SEBUTAN HURUF ARAB DALAM KALANGAN MURID SEKOLAH RENDAH

'Jawlah Tiktoq' is an application developed as a teaching aid for learning and facilitating Arabic for non-native students. The Jawlah Tiktoq application was developed based on the concept of fun learning to help students master the pronunciation of Arabic 'mutamathilah' letters. The application focuses on four 'mutamathilah' letters- ta', kaf, to' and qaf (ت ك ط ق) and it was named after these letters. The data shows that students face challenges in mastering the pronunciation of ‘mutamathilah' letters as they have the same voice articulation and letter properties. Therefore, the application was developed as an effective alternative medium in the teaching of 'mutamathilah' letters. In this study, the Jawlah Tiktoq application was developed as an instructional material by adapting the ADDIE Instructional Model. Questionnaire were used as the instrument in the needs analysis phase which involved Arabic teachers from primary schools in Federal Territory. The purposive sampling method was used in the needs analysis phase which involved 100 teachers. Data from the questionnaire and literature review were used in the development and design phases. In addition, the Technological Pedagogical Content Knowledge (TPACK) model has been used to ensure that the application developed can attract students’ interest and motivation and make the classroom atmosphere more conducive and interactive. The implementation phase involves piloting the application with selected students while the usability evaluation involves a group of field experts. The use and development of the Jawlah Tiktoq application have helped improve students' mastery of the pronunciation of the Arabic 'mutamathilah' letters as well as their motivation and interest in learning Arabic.

A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven spatial-temporal adaptive model

In alumina production, the evaporation as the key process uses recyclable resources and reduces environmental pollution. In fact, the quality of export product with offline and delayed, results in low precision of process control and high energy consumption. To ensure green and efficient production, in this paper, a new prediction method integrating process knowledge and data-driven spatial-temporal adaptive model is put forward. First, to preprocessed production data for ensuring modeling accuracy, data reconciliation technology is adopted. Then, based on material and heat transfer mechanism, for equipment and industrial process, the mechanism models are established. Furthermore, with time difference and moving window model, an error compensation method is utilized in terms of double locally weighted kernel PLS for estimation error in hypothesis-based mechanism modeling. Finally, the data-driven spatial-temporal adaptive model and the process knowledge-based mechanism model are integrated. To illustrate the model feasibility, an industrial sodium aluminate solution evaporation is used. It demonstrates that, for the developed model, the prediction accuracy can reach more than 90% within the ±2% error range, and effectively estimate the actual product quality and ensure the prediction effect.

Exploring the capabilities and limitations of large language models in nuclear medicine knowledge with primary focus on GPT-3.5, GPT-4 and Google Bard

Exploring the capabilities and limitations of large language models in nuclear medicine knowledge with primary focus on GPT-3.5, GPT-4 and Google Bard