Database Model Research Articles

Assessing impacts of observations on ocean circulation models with examples from coastal, shelf, and marginal seas

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

Digital technology for the national sport development: Designing a database model to analyse elite sports data

Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

Data-based distributed model predictive control for large-scale systems

Data-based distributed model predictive control for large-scale systems

On the Way to a Data-Based Governance Model for Swiss Public Institutions – Focus Fields for Empowerment in Innovation and Identification

On the Way to a Data-Based Governance Model for Swiss Public Institutions – Focus Fields for Empowerment in Innovation and Identification

COSMIC Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers.

Single nucleotide polymorphisms (SNPs) in cytochrome P450 (CYP450) enzymes alter the metabolism of a variety of drugs. Numerous medications, including chemotherapies, are metabolized by CYP450 enzymes, making the expression of this suite of enzymes in tumor cells relevant to prescription regimens for cancer patients. We analyzed the characteristics of mutations of the CYP2D6 enzymes in cancer patients obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC), including mutation type, age of the patient, tissue type, and histology. Mutations were analyzed through the Cancer-Related Analysis of Variants Toolkit (CRAVAT) software along with CHASM and VEST4 algorithms to determine the likelihood of being a driver and/or pathogenic mutation. For mutations with significant CHASM and VEST4 scores, structural analysis of each corresponding mutant protein was performed. The effect of each mutation was evaluated for its impact on the overall protein stability and ligand binding using Foldit Standalone and SwissDock, respectively. Structural analysis revealed that several missense mutations in CYP2D6 resulted in altered stability after energy minimization. Three missense mutations of CYP2D6 significantly altered docking stability and those located on alpha-helices near the docking site had a more significant impact than those not found in secondary protein structures. In conclusion, we have identified a series of mutations to CYP2D6 enzymes with possible relevance to cancer pathologies. Significance Statement CYP2D6 is responsible for the metabolism of many anti-cancer drugs. This study identified and characterized a series of mutations in the CYP2D6 enzyme that occurred in tumors. We found it likely that many of these mutations would alter enzyme function, leading to changes in drug metabolism in the tumor. We provide a basis for predicting the likelihood of a patient carrying these mutations to identify patients who may benefit from a precision medicine approach to drug selection and dosing.

Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

"Projekt TyreRoadNoise" -- Data-based study of effects on controlled and real drive noise emission

Road traffic noise affects the quality of life of many citizens. In particular, the noise emitted by electric vehicles is mainly caused by tyre-road contact. There are many conflicts of interest between the performance characteristics of tyres and road surfaces. Therefore, more accurate prediction models are needed to exploit the potential for reducing tyre-road noise emissions during development. To this end, the TyreRoadNoise project team is developing a physics- and data-based tyre-road noise prediction model over a three-year period. A multi-manufacturer fleet of vehicles will collect extensive data on tyre-road noise emissions and their influencing parameters using simple measurement methods. AI methods will be used to estimate relevant parameters and correlations, which will be validated by measurements in controlled environments. The model should make it possible to extrapolate emissions on other routes from appropriate measurements on a specific road and to derive mitigation measures. Data and models will be made publicly available for future use. This article identifies the research needs, gives a brief overview of the project and describes the tyre-road noise measurement techniques used. Interested parties are invited to discuss and participate in the project.

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients’ disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Participation and clarity of budget targets, management accounting information system on managerial performance with budgetary slack as a mediation variable

The aim of this research is to analyze "budget participation, clarity of budget targets and management accounting information systems on managerial performance with the budgetary slack as a mediating variable," which has been carried out by 5 service companies affected by COVID-19. This research uses quantitative methods, and the data source used is primary data. The population in this research is 5 service companies spread across Jakarta, including PT. Sukses Andalan Bangsa, PT. Anak Bangsa Cakrawala Dunia, PT. Impact Power Mandiri, PT. Kitamuda Andalan Indonesia and PT. Triputra International. The sample selection for this research included directors, managers, and supervisors in each division of the company, with a total of 50 respondents. In this research, data analysis uses SmartPLS 4.0 with a data-based analysis method structural equation modeling based on partial least. Based on the hypothesis testing carried out, the research results show that budget participation has no positive and insignificant effect on managerial performance. For clarity of budget targets and management, accounting information systems have a positive and significant effect on managerial performance. Then, the budget gap has a positive and significant mediating effect on all variables of budget participation, clarity of budget targets, and management accounting information systems on managerial performance.

An Assessment Model for Agricultural Databases: The Arthropod Pesticide Resistance Database as a Case Study.

This paper presents a multi-method approach for evaluating the utility and impact of agricultural databases in the context of the rapidly expanding digital economy. Focusing on the Arthropod Pesticide Resistance Database, one of the most comprehensive global resources on arthropod pesticide resistance, we offer a framework for assessing the effectiveness of agricultural databases. Our approach provides practical guidance for developers, users, evaluators, and funders on how to measure the impact of these digital tools, using relevant metrics and data to validate their contributions. Additionally, we introduce an index-based method that evaluates impact across multiple dimensions, including data usage, accessibility, inclusivity, knowledge generation, innovation, research and policy development, and collaboration. The detailed methodology serves as both a reference and a model for evaluating the impact of other agricultural databases, ensuring they effectively support decision-making and foster innovation in the agricultural sector.

On Gamma-Gompertz-Makeham Assurances and Life Annuities

We focus on the gamma-Gompertz-Makeham model, and derive useful structural properties for this mortality model. We provide the basic properties like moments, remaining life expectancy, single life annuity, among many others, in closed form, and so it eliminates the need of evaluating them through numerical integration directly. The estimation of the gamma-Gompertz-Makeham model parameters is performed by using the maximum likelihood method under the traditional discrete Poisson distribution, as well as under the recently introduced discrete Bell distribution, which is an interesting alternative to the usual Poisson distribution, mainly in the presence of overdispersion. We illustrate the performance of the gamma-Gompertz-Makeham model in a human mortality database, and compute, based on the Poisson and Bell distributions, the remaining life expectancy, single life annuity and single assurance at ages 30, 55, and 80 in France, Italy, Japan, and Sweden, from 1947 to 2020 males and females separately.

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.

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.