Knowledge

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

Undergraduate courses in electrical machines often include an introduction to the air gap magnetic field as a basic element in the energy conversion process. The students must learn the main properties of the field produced by basic winding configurations and how they relate to the winding current and frequency. This paper describes a new test equipment design aimed at helping students achieve these learning goals. The test equipment is designed based on four main elements: a modified slip ring induction machine, a winding current driver board, the DAQ boards, and a PC-based virtual instrument. The virtual instrument provides the winding current drivers with suitable current references depending on the user selected machine operational status (single- or three-phase/winding with DC or AC current) and measures and displays the air gap magnetic field for that operational status. Students’ laboratory work is organized into a series of experiments that guide their achievement of these air gap field-related abilities. Student learning, assessed based on pre- and post-lab exams and end-of-semester exams, has increased significantly. The students’ opinions of the relevance, usefulness, and motivational effects of the laboratory were also positive. Full article

In response to the recent formalization of Filipino Sign Language (FSL) and the lack of comprehensive studies, this paper introduces a real-time FSL gesture recognition system. Unlike existing systems, which are often limited to static signs and asynchronous recognition, it offers dynamic gesture capturing and recognition of 10 common expressions and five transactional inquiries. To this end, the system sequentially employs cropping, contrast adjustment, grayscale conversion, resizing, and normalization of input image streams. These steps serve to extract the region of interest, reduce the computational load, ensure uniform input size, and maintain consistent pixel value distribution. Subsequently, a Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model was employed to recognize nuances of real-time FSL gestures. The results demonstrate the superiority of the proposed technique over existing FSL recognition systems, achieving an impressive average accuracy, recall, and precision rate of 98%, marking an 11.3% improvement in accuracy. Furthermore, this article also explores lightweight conversion methods, including post-quantization and quantization-aware training, to facilitate the deployment of the model on resource-constrained platforms. The lightweight models show a significant reduction in model size and memory utilization with respect to the base model when executed in a Raspberry Pi minicomputer. Lastly, the lightweight model trained with the quantization-aware technique (99%) outperforms the post-quantization approach (97%), showing a notable 2% improvement in accuracy. Full article

We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as teammates, artificial intelligence (AI) machines must be able to determine what constitutes the usable knowledge that contributes to a team’s success when facing uncertainty in the field (e.g., testing “knowledge” in the field with debate; identifying new knowledge; using knowledge to innovate), its failure (e.g., troubleshooting; identifying weaknesses; discovering vulnerabilities; exploitation using deception), and feeding the results back to users and society. It matters not whether a debate is public, private, or unexpressed by an individual human or machine agent acting alone; regardless, in this exploration, we speculate that only a transparent process advances the science of autonomous human–machine teams, assists in interpretable machine learning, and allows a free people and their machines to co-evolve. The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric. We conclude that the structure of “knowledge”, once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its useful applications are multifaceted (akin to maximum entropy production). Our novel finding is the existence of Shannon holes that are gaps in knowledge, a surprising “discovery” to only find Shannon there first. Full article

Despite increased attention being given to Indigenous rights, decolonization, and reconciliation in a broader business setting, the engagement of business, marketing, and consumer studies with Indigenous cultures and peoples is negligible. Although Indigenous and First Nations peoples have a significant position in the social sciences, there is no specific body of marketing or consumer knowledge that is dedicated to Indigenous knowledge and practices, even though there is a growing interest in more inclusive and transformative marketing. This paper reports on current research on Indigenous worldviews and marketing, with a continuum of Indigenous research being presented which is particularly informed by Māori experiences in Aotearoa New Zealand. Several appropriate research methods for advancing Indigenous knowledge are presented. The paper concludes by noting the potential contributions that Indigenous knowledge may provide and some of the challenges faced. Full article

Commercial construction projects (CCPs) in New Zealand contribute more to the economy than other project types. However, many face cost and time increases due to inadequate planning. Procurement pathways that involve contractors during design development provide more time to plan, collaboratively. Nevertheless, most projects are procured through traditional tender where contractors are only involved after detailed design. Through two-stage early contractor involvement (2S-ECI), contractors can provide design buildability advice for complex projects, contribute value management, carry out exploratory works, and order materials. The role of subcontractors in 2S-ECI can be significant. Six semi-structured interviews were conducted with clients, consultants, main contractors, and a subcontractor involved in large complex commercial construction projects. The findings build on the emerging body of knowledge about 2S-ECI by providing insight into subcontractor early involvement. Project complexity and market conditions were the main reasons for early subcontractor involvement. Common challenges include a lack of information sharing among the parties, non-competitive selection, and a lack of standard contract documentation. Opportunities for improvement include clarifying client expectations, educating stakeholders, and providing more equitable compensation for pre-construction services. Key drivers for subcontractor involvement include project complexity, market conditions, ordering long-lead-time systems, and performance specifications. Specialist early sub-trades include electrical, mechanical, structural steel, and façades. Subcontractors should typically be engaged as early as possible, often concurrently via main contractors to share performance risk. Pre-construction services provided by subcontractors include planning and sequencing; design buildability analysis; risk mitigation; value management; budget advice; systems procurement; design solutions; and document control systems. Advantages include obtaining specialist project knowledge and improving completion certainty. Producing a pre-construction services agreement (PCSA) for subcontractors may address challenges, as has been carried out for main contractors, but there is still a gap in the contractual framework for 2S-ECI for subcontractors. Full article

The Program for International Student Assessment highlights the persistent lack of commitment and motivation among students worldwide in their school activities, which are resulting in decreased proficiency levels in reading, mathematics, and science. The magnitude of this phenomenon, with its clear social implications, suggests that we are facing a concerning quest for immediate answers and results. This research focuses on the impact of the relationships between self-regulated learning processes and the planning of time management that is dedicated to school activities on student performance, specifically in the subjects of the Mother Tongue and Mathematics. The instruments used for analysis included the Inventory of Self-Regulated Learning Processes, the Inventory of Time Management Planning, a personal data sheet, and a school data sheet. The sample in this study consisted of 688 students from primary schools in northern Portugal. The results reveal that self-regulated learning has a positive influence on how students plan time management, both in the short and long term. Additionally, a positive and statistically significant relationship is observed between short-term and long-term time management planning and students’ academic performance. This study provides an in-depth perspective on the dynamics between these elements, shedding light on the crucial nuances that shape students’ academic journeys. Full article

Background: In an era of dramatic technological progress, the consequent economic transformations, and an increasing need for an adaptable workforce, the importance of education has risen to the forefront of the social discourse. The concurrent increase in the awareness of issues pertaining to social justice and the debate over what this justice entails and how it ought to be effected, feed into the education policy more than ever before. From the nexus of the aforementioned considerations, concern about the so-called education gap has emerged, with worldwide efforts to close it. Methods: I analyze the premises behind such efforts and demonstrate that they are founded upon fundamentally flawed ideas. Results: I show that in a society in which education is delivered equitably, education gaps emerge naturally as a consequence of differentiation due to talents, the tendency for matched mate selection, and the heritability of intellectual traits. Conclusion: I issue a call for a redirection of efforts away from the ill-founded idea of closing the education gap to the understanding of the magnitude of its unfair contributions, as well as to those social aspects that can modulate it in accordance with what a society deems fair according to its values. Full article

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region. Full article

Healthcare organizations have a contractual obligation to the public to address population-level inequities to health services access and shed light on them. Various studies have focused on achieving equitable access to healthcare services for vulnerable patients. However, these studies do not provide a nuanced perspective based on the local reality across the full continuum of care. In previous work, graph topology was used to provide visual depictions of the dynamics of patients’ movement across a complex healthcare system. Using patients’ encounters data represented as a graph, this study expands on previous work and proposes a methodology to identify and quantify cohort-specific disparities in accessing healthcare services across the continuum of care. The result has demonstrated that a more nuanced approach to assessing access-to-care disparity is doable using patients’ patterns of service utilization from a longitudinal cross-continuum healthcare dataset. The proposed method can be used as part of a toolkit to support healthcare organizations that wish to structure their services to provide better care to their vulnerable populations based on the local realities. This provides a first step in addressing inequities for vulnerable patients in accessing healthcare services. However, additional steps need to be considered to fully address these inequities. Full article

Science has been an incredibly powerful and revolutionary force. However, it is not clear whether science is suited to performance under pressure; generally, science achieves best in its usual comfort zone of patience, caution, and slowness. But, if science is organized knowledge and acts as a guiding force for making informed decisions, it is important to understand how science and scientists perform as a reliable and valuable institution in a global crisis. This paper provides insights and reflections based on the experience of the COVID-19 pandemic and from an analytical perspective. In particular, we analyze aspects such as speed, transparency, trust, data sharing, scientists in the political arena, and the psychology of scientists—all of which are areas inviting more detailed investigations by future studies conducting systematic empirical studies. Full article