Database Model Research Articles

Authentic caring in online professional development for early childhood teachers in South Africa

Background: The professional development (PD) of early childhood teachers is of strategic importance in higher education and the profession given the need to improve responsiveness and progress towards millennium goals.Aim: The purpose was to analyse classroom practices and teachers’ experiences of PD programmes, through the theoretical lens of the UNESCO ICT Competency Framework for Teachers to construct an authentic data-based model, which can be used to understand PD challenges and guide decisions about the improvement of education quality.Setting: The study was conducted in Gauteng with teachers from a selection of early childhood centres.Methods: A phenomenological inquiry explored the settings and practices of 23 selected teachers in Gauteng province. Interview and observation data were analysed for content categories and grounded theory articulations.Results: The main finding is that teachers facing everyday educational challenges benefit from school-based support and available media but need additional learning to advance their professional aspirations. The grounded theory analyses of the data highlighted that early childhood practices and articulated development needs are best explained as authentic caring. This was conceptualised metaphorically as garden spaces where careful tending, deliberate planning and various elements of nurturing knowledge may harmoniously be blended. The model extends prevailing theories of PD and the flipped ICT Competency Framework for Teachers.Conclusion: The model of authentic caring is a grounded theory articulation based upon beliefs of practising EC teachers.Contribution: The article provides a comprehensive online teacher professional development framework for EC teachers, emphasising authenticity, care and alignment with global educational standards.

Local porosity prediction in metal powder bed fusion using in-situ thermography: A comparative study of machine learning techniques

The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data.

Integrating smart manufacturing techniques into undergraduate education: A case study with heat exchanger

The process systems domain is undergoing the fourth industrial revolution, which is helping industries digitize and optimize their production techniques. Concurrently, the field of data-based modeling has been expanding, leading to the proposal of many fault detection models. However, the rapid expansion has created gaps in the field. For instance, Smart Manufacturing (SM) methodologies have yet to be incorporated into undergraduate chemical engineering education. Additionally, only a few developed fault detection models have been deployed for real-time usage and practical applications. This study takes a crucial step toward bridging the two mentioned gaps by enabling undergraduate students to learn SM techniques and developing a safe and controlled academic environment for deploying fault detection models. The demonstration is implemented on a shell and tube heat exchanger, taught in a senior year laboratory course, using the Smart Manufacturing Innovation Platform (SMIP). The implementation provides an easily customizable pipeline for SM applications involving human-in-the-loop decision-making on a real-life hardware system. Actual data from heat exchanger equipment is used to train and compare the performances of several state-of-the-art fault detection models, including fully connected, convolutional, and recurrent neural networks. Current work also presents tutorials on deploying models for practical real-time applications using the SMIP. The overall architecture is a plug-and-play package that will motivate students to learn about SM and catalyze their interest in developing and deploying fault detection models using real-world data.

Implementation and validation of optimal start control strategy for air conditioners and heat pumps

Commercial buildings are responsible for approximately 20 % of the total energy consumption and greenhouse gas emissions in the United States. Over 85 % of these buildings lack building automation systems, and many are small (<50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, several operational deficiencies lead to excess energy consumption. Studies have shown that managing the RTUs’ heating and cooling set points, schedules, setbacks, and optimal start can result in a 20 % to 25 % reduction in electricity consumption in small commercial buildings. These buildings typically use fixed schedules to start the RTUs 60 to 120 min before occupancy begins, which results in excess energy consumption. This paper presents research that demonstrates and evaluates the performance of four optimal start methods, which utilize data-based modeling as a key element in facilitating adaptive control in response to time-varying inputs while requiring minimal sensor inputs. The evaluation found energy savings in two commercial buildings equipped with RTUs by periodically alternating four different optimal start models during the cooling and heating season. The resulting energy savings are positive for all models and range from 2 to 5 kWh/day/unit. The units on the east side of the building showed higher savings, while interior units showed greater variability in savings due to the differences in capacities and room sizes. Savings were considerably greater during the heating season compared to the cooling season. The performance of all four models on Mondays was poor; models suggested a shorter optimal start time, which resulted in relatively larger errors. The future work will look at using a different model for the days after weekends and holidays.

Inflated energy prices and capital structure: firm level evidence from Indian manufacturing sector

Purpose The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period. Design/methodology/approach After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database. Findings The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure. Practical implications This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based. Originality/value To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding.

Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.

Generating interpretable rainfall-runoff models automatically from data

A sudden surge of data has created new challenges in water management, spanning quality control, assimilation, and analysis. Few approaches are available to integrate growing volumes of data into interpretable results. Process-based hydrologic models have not been designed to consume large amounts of data. Alternatively, new machine learning tools can automate data analysis and forecasting, but their lack of interpretability and reliance on very large data sets limits the discovery of insights and may impact trust. To address this gap, we present a new approach, which seeks to strike a middle ground between process-, and data-based modeling. The contribution of this work is an automated and scalable methodology that discovers differential equations and latent state estimations within hydrologic systems using only rainfall and runoff measurements. We show how this enables automated tools to learn interpretable models of 6 to 18 parameters solely from measurements. We apply this approach to nearly 400 stream gaging sites across the US, showing how complex catchment dynamics can be reconstructed solely from rainfall and runoff measurements. We also show how the approach discovers surrogate models that can replicate the dynamics of a much more complex process-based model, but at a fraction of the computational complexity. We discuss how the resulting representation of watershed dynamics provides insight and computational efficiency to enable automated predictions across large sensor networks.

DECENTRALIZED VS. CENTRALIZED DATABASE SOLUTIONS IN BLOCKCHAIN: ADVANTAGES, CHALLENGES, AND USE CASES

his study provides a comprehensive examination of decentralized and centralized database solutions within the realm of blockchain technology, exploring the inherent strengths and weaknesses of each approach and their implications for various industries. Decentralized databases, characterized by distributed ledger technology, offer robust security and transparency by eliminating the need for a central authority, thereby reducing the risk of data tampering and fostering trust among participants. However, these benefits come with significant trade-offs, particularly in terms of scalability and performance, as the consensus mechanisms required in decentralized systems often result in slower transaction processing and higher resource consumption. In contrast, centralized databases, typically used in private or permissioned blockchains, excel in operational efficiency and scalability, enabling faster transaction processing and more streamlined management due to the presence of a central authority. Nevertheless, centralized systems introduce vulnerabilities, such as single points of failure and reduced transparency, which can undermine trust and compromise data integrity. The study also highlights industry-specific preferences, with sectors like healthcare and public governance leaning towards decentralized solutions for their emphasis on data integrity, while industries such as finance and logistics favor centralized systems for their superior performance and low latency. Moreover, the research identifies a critical gap in the literature regarding hybrid database models that could potentially integrate the strengths of both decentralized and centralized systems, offering a balanced approach that mitigates their respective weaknesses. These findings underscore the necessity for a tailored approach to database selection in blockchain implementations, aligned with the specific needs and goals of different industries, and suggest that further exploration of hybrid models could lead to more effective and adaptable blockchain solutions in the future.

Deep learning-assisted research on high-performance electrolyte for zinc-ion capacitors

Zinc-ion capacitors (ZICs) have garnered significant attention due to their ability to balance energy density and power density. The properties of electrolytes greatly influence the electrochemical performance of ZICs, and the oxidation potential (OP) of electrolytes can be used to determine the most suitable electrolyte for ZICs. However, traditional methods for exploring OP are time-consuming and inefficient, making it difficult to effectively determine the most suitable electrolyte for ZICs. Herein, we propose a machine learning-assisted method that combines deep learning with the traditional machine learning algorithm Gradient Boosting Regression (GBR), which we refer to as DeepGBR. This method can accurately predict the OP of electrolytes even when dealing with complex features and small datasets. Furthermore, with the assistance of deep learning, the accuracy of the predictions improves as the dataset size increases. We further validated the model with four common electrolyte molecules, finding that the predicted results are highly consistent with the theoretical values. The results show that acetonitrile has a high OP, which can achieve high energy density and long-cycle stability when used as an electrolyte solvent for ZICs. This work provides a certain reference for the database modeling strategy of electrolytes for electrochemical energy storage devices.

Data-based models to investigate protective piles effects on the scour depth about oblong-shaped bridge pier

The primary objective of this study is to examine the suitability of employing a machine learning models (MLMs) comprising Support Vector Machine (SVM), Gene Expression Programming (GEP), and Artificial Neural Network (ANN) algorithms to emulate the influence of geometric and geotechnical parameters of the piles on the scour depth reduction about the oblong-shaped bridge pier. A dataset consisting of 90 laboratory observations was utilized, allocating 70 % of the data for the training and 30 % for the testing the MLMs. An oblong-shaped bridge pier of diameter D, was positioned and subjected to a rectangular flume featuring an erodible bed comprising sand sediment particles of D50 = 1.7 mm under clear water and subcritical flow conditions. The spacing between the piles, LD, the angle of pile orientation with respect to the flow, θ, and the Froude number, Fr, were identified as independent parameters through the dimensional analysis and Buckingham-Π theory. The MLMs’ performance were assessed employing root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The outcomes, affirming the predictive capacity of all MLMs included, revealed that the GEP model employing a three-gene configuration exhibited superior accuracy compared to the other two MLMs. Specifically, the performance indicators (RMSE, MAE, R2, DDR) during the training and testing phases were (0.0433, 0.033, 0.981, 6.8) and (0.054, 0.045, 0.958, 4.81) respectively. The sensitivity analysis elucidated the hierarchy of influential variables as follows: Fr, LD and θ.

Data-based modeling of the Pharmacodynamics for the effect of Propofol and Remifentanil during General Anesthesia

Predicting Bispectral Index (BIS) and Mean Arterial Pressure (MAP) during anesthesia is critical for patient safety and effective anesthesia management. Traditional pharmacodynamic response surface models have limitations in accuracy and adaptability. This paper presents a novel approach for predicting BIS and MAP using machine learning techniques. Rather than using standard pharmacodynamic response surface models, a machine learning-based approach is proposed to model the pharmacodynamic. The proposed method considers the state of the standard propofol and remifentanil pharmacokinetic model and the patient information as features to predict the BIS and MAP values. Training and testing are done on a selected subset of the VitalDB dataset (Lee and Jung, 2018) containing 191 different patients. Results demonstrate that the machine learning-based approach outperforms standard pharmacodynamic models in terms of accuracy. Specifically, the Support Vector Regression (SVR) model achieves a Mean Absolute Prediction Error (MDAPE) 32% smaller than the Eleveld model for BIS prediction. For MAP prediction, the SVR model also demonstrates superior performance with a reduction of 66% of MDAPE. The proposed method provides similar performance as the deep-learning method (Lee et al., 2018) while keeping a simple structure that may be used in other applications.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

EuroPED-NN: uncertainty aware surrogate model

This work successfully generates an uncertainty-aware surrogate model of the EuroPED plasma pedestal model using the Bayesian neural network with noise contrastive prior (BNN-NCP) technique. This model is trained using data from the JET-ILW pedestal database and subsequent model evaluations, conforming to EuroPED-NN. The BNN-NCP technique has been proven to be a suitable method for generating uncertainty-aware surrogate models. It matches the output results of a regular neural network while providing confidence estimates for predictions as uncertainties. Additionally, it highlights out-of-distribution regions using surrogate model uncertainties. This provides critical insights into model robustness and reliability. EuroPED-NN has been physically validated, first, analyzing electron density ne(ψpol=0.94) with respect to increasing plasma current, Ip , and second, validating the Δ−βp,ped relation associated with the EuroPED model. This affirms the robustness of the underlying physics learned by the surrogate model. On top of that, the method was used to develop a EuroPED-like model fed with experimental data, i.e. an uncertainty aware experimental model, which is functional in JET database. Both models have been also tested in ∼50 AUG shots.

A Visual Environment for Data Driven Protein Modeling and Validation.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Research on the integrated evaluation model of database based on LDA and entropy weight method

Research on the integrated evaluation model of database based on LDA and entropy weight method

Databases Unbound: Querying All of the World's Bytes with AI

Over the past five decades, the relational database model has proven to be a scaleable and adaptable model for querying a variety of structured data, with use cases in analytics, transactions, graphs, streaming and more. However, most of the world's data is unstructured. Thus, despite their success, the reality is that the vast majority of the world's data has remained beyond the reach of relational systems. The rise of deep learning and generative AI offers an opportunity to change this. These models provide a stunning capability to extract semantic understanding from almost any type of document, including text, images, and video, which can extend the reach of databases to all the world's data. In this paper we explore how these new technologies will transform the way we build database management software, creating new that systems that can ingest, store, process, and query all data. Building such systems presents many opportunities and challenges. In this paper we focus on three: scalability, correctness, and reliability, and argue that the declarative programming paradigm that has served relational systems so well offers a path forward in the new world of AI data systems as well. To illustrate this, we describe several examples of such declarative AI systems we have built in document and video processing, and provide a set of research challenges and opportunities to guide research in this exciting area going forward. And lovely apparitions, -dim at first , Then radiant, as the mind arising bright From the embrace of beauty (whence the forms Of which these are the phantoms) casts on them The gathered rays which are reality- Shall visit us the progeny immortal Of Painting, Sculpture, and rapt Poesy , And arts, though unimagined, yet to be ; Prometheus Unbound, Percy Bysshe Shelley

Receiving Non-Contact Information About the Object through a Mobile Application

The article introduces a mobile application that enables users to retrieve information about objects by simply approaching them with their phone. This eliminates the need for manual searching and allows for automatic retrieval of relevant information on the phone. This non-contact method of retrieving information makes it easier to preserve and utilize objects, saving time and resources. The mobile application that has been developed comprises of various modules such as object identification, object data collection and processing, database modeling, and information retrieval. It has been implemented using modern technologies and equipment using Java language. This application can be used effectively in multiple fields such as museums, parks, medicine, education, among others. In the field of education, this app provides additional resources and a more interactive learning experience that goes beyond traditional textbooks or presentations.

Modeling Spatio-Temporal Rainfall Distribution in Beni–Irumu, Democratic Republic of Congo: Insights from CHIRPS and CMIP6 under the SSP5-8.5 Scenario

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha.

Habitat Prediction of Bigeye Tuna Based on Multi-Feature Fusion of Heterogenous Remote-Sensing Data

Accurate habitat prediction of Bigeye Tuna, the main fishing target of tuna pelagic fishery, is of great significance to the fishing operation. In response to the fact that most of the current studies use single-source data for habitat prediction, and the association between spatiotemporal features and habitat distribution is not fully explored and that this has limited the further improvement of prediction accuracy, this paper analyzes the spatiotemporal distribution of the characteristics of Bigeye Tuna’s highly migratory nature. Additionally, it puts forward a method of habitat prediction that utilizes heterosource remote-sensing data for the four-dimensional time–space–environment–spectrum (TSES) for deep-level feature extraction. First, a multi-source heterogeneous dataset was constructed by combining the spatiotemporal distribution characteristics of the product-level environmental remote-sensing data and the L1B-level original spectral remote-sensing data, and then a multi-branch, dynamic spatiotemporal feature extraction, Long Short-Term Memory Network (LSTM) time-series model was constructed to extract the characteristics of the heterogeneous data. This model was constructed to fully explore and utilize the multidimensional deep-level TSES distribution features affecting the habitat prediction. Finally, the two types of heterogeneous data were subjected to the weighted average-based decision-level fusion to obtain the final prediction results. The experimental results show that compared with other methods, the proposed method in this paper outperforms traditional machine-learning models and other single-source, data-based time-series models, with R2 reaching 0.96278 and RMSE decreasing to 0.031361 in the validation experiments of these models. In contrast, the method in this paper demonstrates good generalization ability and achieves accurate prediction of future fishery distribution.

Neutrosophic Logic - A Better Approach to Execute Imprecise Queries Through the Neutrosophic Database Model

Objectives: The neutrosophic set has a more powerful ability to process imprecise information compared to the vague set. The objective of the authors is to process the uncertain query using vague as well as neutrosophic logic and compare the result. Methods: Authors have designed a new architecture to process uncertain queries using vague as well as neutrosophic sets. In the architecture at first, the imprecise query has been taken as input. From the inputted imprecise query uncertain data and fuzzy attributes are identified. Next, using the truth membership algorithm the truth membership value between uncertain data and the fuzzy attribute value for each tuple is calculated. This truth membership value is used to obtain the false and indeterminacy membership value to represent the domain of fuzzy attribute in vague or neutrosophic form. Then, the tolerance membership value of each tuple corresponding to the imprecise query is determined by using the similarity measures formula for vague as well as neutrosophic set. Findings: Using the truth membership algorithm and similarity measures formula, the tolerance membership value for each tuple of the relation corresponding to an imprecise query is obtained. If an imprecise query consists of more than one fuzzy attribute then the fuzzy intersection is used to obtain the membership value of the tuple. After obtaining the tolerance membership value of each tuples, the decision maker will provide a threshold (α-cut) value based on which an SQL is generated which fetched a set of tuples whose membership value meets the threshold (α-cut) value for vague as well as neutrosophic set. Authors have considered a patient database to process uncertain queries using a neutrosophic set and claimed neutrosophic set gives better results compared to vague. Novelty: The novelty of this work is to train the computer using a soft set to obtain desired outcomes while processing imprecise query in the database. Keywords: Fuzzy data, Vague set, Neutrosophic Set, Similarity Measures, α -Equal neutrosophic data