Knowledge

This research article proposes the use of an uncertainty–certainty matrix (UCM) for licensing decision making in the human services, which is the decision to issue a license to operate. It is a proposed study protocol and conceptual framework; it is not an empirical study. It shows how the matrix can be used in rule decision making and how it clearly shows when decision making has gone awry when bias is introduced into the decision making. It is also proposed to be used to make decisions in differential monitoring and in validation and reliability studies. This proposal presents a potential blueprint on how the UCM can be used within human services licensing as a decision-making tool. Full article

An integral indicator of the development of society is the amount of knowledge, which can be measured by the number of accumulated publications in the form of patents, articles, and books. Knowledge production is examined on a global scale. We analyze existing econometric models and develop a generalized model that expresses the per capita knowledge production rate (called productivity) as a function of the amount of accumulated knowledge. The function interpolates two extreme cases, the first of which describes an underdeveloped society with very little knowledge and non-zero productivity, and the second, a highly developed society with a large amount of knowledge and productivity that grows according to a power law as knowledge accumulates. The model is calibrated using literature data on the number of patents, articles, and books. For comparison, we also consider the rapid growth in the global information storage capacity that has been observed since the 1980s. Based on the model developed, we can distinguish between two states of society: (1) a pre-information society, in which the knowledge amount is below a certain threshold and productivity is quite low, and (2) an information society with a super-threshold amount of knowledge and its rapid accumulation due to advanced computer technologies. An analysis shows that the transition to an information society occurred in the 1980s. Full article

Intrusion detection is a critical component of cybersecurity, enabling timely identification and mitigation of network threats. This study proposes a novel two-stage intrusion detection framework using the CSE-CIC-IDS2018 dataset, a comprehensive and realistic benchmark for network traffic analysis. The research explores two distinct approaches: the stacked autoencoder (SAE) approach and the Apache Spark-based (ASpark) approach. Each of these approaches employs a unique feature representation technique. The SAE approach leverages an autoencoder to learn non-linear, data-driven feature representations. In contrast, the ASpark approach uses principal component analysis (PCA) to reduce dimensionality and retain 95% of the data variance. In both approaches, a binary classifier first identifies benign and attack traffic, generating probability scores that are subsequently used as features alongside the reduced feature set to train a multi-class classifier for predicting specific attack types. The results demonstrate that the SAE approach achieves superior accuracy and robustness, particularly for complex attack types such as DoS attacks, including SlowHTTPTest, FTP-BruteForce, and Infilteration. The SAE approach consistently outperforms ASpark in terms of precision, recall, and F1-scores, highlighting its ability to handle overlapping feature spaces effectively. However, the ASpark approach excels in computational efficiency, completing classification tasks significantly faster than SAE, making it suitable for real-time or large-scale applications. Both methods show strong performance for distinct and well-separated attack types, such as DDOS attack-HOIC and SSH-Bruteforce. This research contributes to the field by introducing a balanced and effective two-stage framework, leveraging modern machine learning models and addressing class imbalance through a hybrid resampling strategy. The findings emphasize the complementary nature of the two approaches, suggesting that a combined model could achieve a balance between accuracy and computational efficiency. This work provides valuable insights for designing scalable, high-performance intrusion detection systems in modern network environments. Full article

Sustainable knowledge sharing among mathematics and science teachers is imperative to improve the ability of such teachers to enhance the way information is transferred to learners. South Africa ranked 37th out of 42 countries in an assessment to determine the ability of high school learners to conduct mathematics and science. There is, therefore, an urgent need to investigate how teachers can be empowered to enhance their ability to transfer knowledge of mathematics and science to improve the ability of learners to engage in these subjects. A post-positivist paradigm and quantitative survey design were employed to identify ways of knowledge sharing that will enhance the ability of teachers to transfer knowledge of mathematics and science to learners. The findings identified key barriers to knowledge sharing, including the role of school management in fostering a culture of knowledge exchange, time management, and limited opportunities for professional development. Based on the findings of the research, a framework is proposed to encourage knowledge sharing, which may ultimately improve teaching practices and learner outcomes in mathematics and science. Full article

ChatGPT, or Chat Generative Pre-trained Transformer, developed by OpenAI, is a versatile chatbot known for generating human-like text responses. Since its launch in November 2022, it has sparked interest and debate. This bibliometric study aimed to explore ChatGPT-related publications using the Web of Science database from 2023 to June 2024. Original articles in English were retrieved on 24 June 2024, using the topic field “ChatGPT”. Citation records were analyzed using bibliometrix 4.1 and VOSviewer 1.6.20. Between January 2023 and 24 June 2024, 3231 original articles on ChatGPT were published in 1404 journals, with an average citation rate of 5.6 per article. The United States led with 877 articles, followed by China and India. The University of California System, Harvard University, and the State University System of Florida were the most prolific institutions. Keyword co-occurrence network analysis revealed the interdisciplinary nature of ChatGPT research, particularly contributions in healthcare, education, and technology. In conclusion, this bibliometric analysis identified critical areas of ChatGPT research focus, such as applications in educational settings and its ethical implications. These findings are crucial for fostering further advancements that leverage ChatGPT’s capabilities while mitigating its risks. Full article

Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field. Full article

The significance of knowledge management in the tourism accommodation sector is increasingly vital due to rapid market changes and intense competition. Although the value of identifying and implementing critical success factors (CSFs) for knowledge management is widely recognized in the sector, there is still a lack of comprehensive understanding and practical application of these factors. This study employs the decision-making trial and evaluation laboratory (DEMATEL) methodology to systematically identify and analyze the interrelationships among these CSFs. The findings reveal a complex web of dependencies within this network. Specifically, leadership commitment and support is identified as the most influential CSF, acting as a fundamental element that enables the successful adoption and integration of knowledge management initiatives. Additionally, strategic alignment and a supportive organizational culture are crucial, working synergistically to ensure that knowledge management initiatives are aligned with overarching organizational goals and create an environment that encourages change and collaboration. Furthermore, the study highlights a mutually reinforcing relationship between knowledge processes, governance, and employee training. This relationship suggests that strong governance structures and clearly defined knowledge processes facilitate and improve the effectiveness of employee training programs while also creating a continuous improvement cycle where improved training further refines governance and knowledge processes. Moreover, the study highlights the integration of the ISO 30401:2018 standard as a systematic framework to support these CSFs, providing a structured approach to improve knowledge management systems. By mapping the cause-and-effect relationships among the identified CSFs, this research offers practical insights for industry professionals to effectively prioritize and address these factors. Full article

Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur in the ecosystem due to the differential diffusion dispersal that happen if one of the constituent species results in the activator or the prey, showing a tendency to undergo autocatalytic growth. The diffusion of the constituent species activator is a random mobility function called passive diffusion. If the other species in the system (the predator/inhibitor) disperses sufficiently faster than the activator, then the spatially uniform distribution of species becomes unstable, and the system will settle into a stationary state. This paper introduced Turing’s mechanism in our reaction–taxis–diffusion model to simulate the harmful algal bloom (HAB) pattern. A numerical approach, the Runge–Kutta method, was used to deal with this system of reaction–taxis–diffusion equations, and the findings were qualitatively compared to the aerial patterns obtained by a drone flying over Torment Lake in Nova Scotia (Canada) during the bloom season of September 2023. Full article

Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. Conventional CRMD detection and treatment methods are time-consuming, expensive, and ineffective. To address these challenges, we propose a real-time deep-learning model that provides disease detection and pesticide dosage recommendations. In the model development process, we collected 5000 maize leaf images experimentally, with permission from Haramaya University, and increased the size of the dataset to 8000 through augmentation. We applied image preprocessing techniques such as image equalization, noise removal, and enhancement to improve model performance. Additionally, during training, we utilized batch normalization, dropout, and early stopping to reduce overfitting, improve accuracy, and improve execution time. The optimal model recognizes CRMD and classifies it according to scientifically established severity levels. For pesticide recommendations, the model was integrated with the Gradio interface, which provides real-time recommendations based on the detected disease type and severity. We used a convolutional neural network (CNN), specifically the ResNet50 model, for this purpose. To evaluate its performance, ResNet50 was compared with other state-of-the-art algorithms, including VGG19, VGG16, and AlexNet, using similar parameters. ResNet50 outperformed the other CNN models in terms of accuracy, precision, recall, and F-score, achieving over 97% accuracy in CRMD classification—surpassing the other algorithms by more than 2.5% in both experimental and existing datasets. The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. Additionally, ResNet50 exhibited lower time complexity during model development. This study demonstrates the potential of ResNet50 models for improving maize disease management. Full article

Education 4.0 arises to provide citizens with the technical/digital competencies and cognitive/interpersonal skills demanded by Industry 4.0. New technologies drive this change, though time-independent learning remains a challenge, because students might face a lack of support, advice and surveillance when teachers are unavailable. This study proposes complementing presential lessons with online learning driven by ChatGPT, applied as an educational tool able to mentor K-12 students learning science at home. First, ChatGPT’s performance in the field of K-12 science is evaluated, scoring A (9.3/10 in 2023, and 9.7/10 in 2024) and providing detailed, analytic, meaningful, and human-like answers. Then, an empirical interventional study is performed to assess the impact of using ChatGPT as a virtual mentor on real K-12 students. After the intervention, the grades of students in the experimental group improved by 30%, and 70% of students stated a positive perception of the AI, suggesting a positive impact of the proposed educational approach. After discussion, the study concludes ChatGPT might be a useful educational tool able to provide K-12 students learning science with the functional and social/emotional support they might require, democratizing a higher level of knowledge acquisition and promoting students’ autonomy, security and self-efficacy. The results probe ChatGPT’s remarkable capacity (and immense potential) to assist teachers in their mentoring tasks, laying the foundations of virtual mentoring and paving the way for future research aimed at extending the study to other areas and levels, obtaining a more realistic view of AI’s impact on education. Full article

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy is above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a Zener diode, biased on the avalanche region. Results showed that, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms. Full article

Differentiated thyroid cancer (DTC), comprising papillary and follicular thyroid cancers, is the most prevalent type of thyroid malignancy. Accurate prediction of DTC is crucial for improving patient outcomes. Machine learning (ML) offers a promising approach to analyze risk factors and predict cancer recurrence. In this study, we aimed to develop predictive models to identify patients at an elevated risk of DTC recurrence based on 16 risk factors. We developed six ML models and applied them to a DTC dataset. We evaluated the ML models using Synthetic Minority Over-Sampling Technique (SMOTE) and with hyperparameter tuning. We measured the models’ performance using precision, recall, F1 score, and accuracy. Results showed that Random Forest consistently outperformed the other investigated models (KNN, SVM, Decision Tree, AdaBoost, and XGBoost) across all scenarios, demonstrating high accuracy and balanced precision and recall. The application of SMOTE improved model performance, and hyperparameter tuning enhanced overall model effectiveness. Full article

Student performance prediction is a critical research challenge in the field of educational data mining. To address this issue, various machine learning methods have been employed with significant success, including instance-based algorithms, decision trees, neural networks, and ensemble methods, among others. In this study, we introduce an innovative approach that leverages the Regularized Greedy Forest (RGF) algorithm within an active learning framework to enhance student performance prediction. Active learning is a powerful paradigm that utilizes both labeled and unlabeled data, while RGF serves as an effective decision forest learning algorithm acting as the base learner. This synergy aims to improve the predictive performance of the model while minimizing the labeling effort, making the approach both efficient and scalable. Moreover, applying the active learning framework for predicting student performance focuses on the early and accurate identification of students at risk of failure. This enables targeted interventions and personalized learning strategies to support low-performing students and improve their outcomes. The experimental results demonstrate the potential of our proposed approach as it outperforms well-established supervised methods using a limited pool of labeled examples, achieving an accuracy of 81.60%. Full article

Knowledge comes in various forms: scientific, artistic, legal, and many others. For most non-computer scientists, it is far easier to express their knowledge in text than in programming code. The dynamic decision tree system is a system for supporting the authoring of expertise in text form and navigation via an interface that limits the cognitive load on the reader. Specifically, as the reader answers questions, relevant tree nodes appear and irrelevant ones disappear. Searching by a keyword can help to navigate the tree. Database calls bring in information from external datasets. Links bring in other decision trees as well as websites. This paper describes the reader interface, the authoring interface, the related state-of-the-art work, the implementation, and case studies. Full article

In higher education institutions, there is a strong interaction between research and teaching activities. This paper presents a case study on the research–teaching nexus based on an analysis of academic results related to the course “Instrumentation and Data Analyses in Marine Sciences” within the Marine Sciences and Technologies Bachelor’s Degree at the Universitat Politècnica de Catalunya (UPC), taught at the Vilanova i la Geltrú campus (Barcelona, Spain). The start of this degree in the academic year 2018–2019 allowed the assignment of technological subjects in the degree to a research group with extensive experience in the research and development of marine technologies. The first section of this paper aims to provide a justification for establishing the Marine Sciences and Technologies Bachelor’s Degree. It highlights the necessity of this program and delves into the suitability of the profiles of the professors responsible for teaching marine technology subjects. Their entrepreneurial research trajectory and their competence in electronic instrumentation are strong arguments for their appropriateness. The next section of the paper explores a detailed analysis of academic results based on surveys and student performance indices. Through a thorough examination of these data, this case study demonstrates, within the context of all UPC degrees, that assigning a research group made up of experienced professors and researchers in the field who are accustomed to working as a team produces superior academic results compared to assignments to professors who do not work as a team. Teamwork presents specific skills necessary for operating the infrastructures and equipment associated with an experimental degree. Full article

In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a model region in Germany, our results demonstrate an 18% improvement in the prediction interval width. While traditional Bayesian CNN models may yield broader prediction intervals to adequately capture uncertainties, the 2DCNN-QD method prioritizes quality-driven interval optimization, resulting in narrower prediction intervals without sacrificing coverage probability. Notably, this approach is nonparametric, allowing it to be effectively utilized across a range of real-world scenarios. Full article

Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses. Full article

Variations in the genetic code for proteins are considered to confer traits and underlying disease. Identifying the functional consequences of these genetic variants is a challenging endeavor. There are online databases that contain variant information. Many publications also have described variants in detail. Furthermore, there are tools that allow for the prediction of the pathogenicity of variants. However, navigating these disparate sources is time-consuming and sometimes complex. Finally, text mining and large language models offer promising approaches to understanding the textual form of this knowledge. This review discusses these challenges and the online resources and tools available to facilitate this process. Furthermore, a computational framework is suggested to accelerate and facilitate the process of identifying the phenotype caused by a particular genetic variant. This framework demonstrates a way to gather and understand the knowledge about variants more efficiently and effectively. Full article

This paper introduces a parameter-efficient transformer-based model designed for scientific literature classification. By optimizing the transformer architecture, the proposed model significantly reduces memory usage, training time, inference time, and the carbon footprint associated with large language models. The proposed approach is evaluated against various deep learning models and demonstrates superior performance in classifying scientific literature. Comprehensive experiments conducted on datasets from Web of Science, ArXiv, Nature, Springer, and Wiley reveal that the proposed model’s multi-headed attention mechanism and enhanced embeddings contribute to its high accuracy and efficiency, making it a robust solution for text classification tasks. Full article