Database Approach Research Articles

Spatial stratification and socio-spatial inequalities: the case of Seoul and Busan in South Korea

This study approaches the spatial stratification phenomenon through a data-based social stratification approach. In addition, by applying a dissimilarity-based clustering algorithm, this study analyzes how regions cluster as well as their disparities, thereby analyzing socio-spatial inequalities. Ultimately, through map visualization, this study seeks to visually identify spatial forms of social inequality and gain insight into the social structure for policy implications. The results determine how the regions are socioeconomically structured and identify the social inequalities between the spaces.

Quantitative Metaproteomics and Activity-based Protein Profiling of Patient Fecal Microbiome Identifies Host and Microbial Serine-type Endopeptidase Activity Associated With Ulcerative Colitis

The gut microbiota plays an important yet incompletely understood role in the induction and propagation of ulcerative colitis (UC). Organism-level efforts to identify UC-associated microbes have revealed the importance of community structure, but less is known about the molecular effectors of disease. We performed 16S rRNA gene sequencing in parallel with label-free data-dependent LC-MS/MS proteomics to characterize the stool microbiomes of healthy (n = 8) and UC (n = 10) patients. Comparisons of taxonomic composition between techniques revealed major differences in community structure partially attributable to the additional detection of host, fungal, viral, and food peptides by metaproteomics. Differential expression analysis of metaproteomic data identified 176 significantly enriched protein groups between healthy and UC patients. Gene ontology analysis revealed several enriched functions with serine-type endopeptidase activity overrepresented in UC patients. Using a biotinylated fluorophosphonate probe and streptavidin-based enrichment, we show that serine endopeptidases are active in patient fecal samples and that additional putative serine hydrolases are detectable by this approach compared with unenriched profiling. Finally, as metaproteomic databases expand, they are expected to asymptotically approach completeness. Using ComPIL and de novo peptide sequencing, we estimate the size of the probable peptide space unidentified (“dark peptidome”) by our large database approach to establish a rough benchmark for database sufficiency. Despite high variability inherent in patient samples, our analysis yielded a catalog of differentially enriched proteins between healthy and UC fecal proteomes. This catalog provides a clinically relevant jumping-off point for further molecular-level studies aimed at identifying the microbial underpinnings of UC.

Geographic and taxonomic trends of rising biological invasion costs

Invasive alien species (IAS) are a growing global ecological problem. Reports on the socio-economic impacts of biological invasions are accumulating, but our understanding of temporal trends across regions and taxa remains scarce. Accordingly, we investigated temporal trends in the economic cost of IAS and cost-reporting literature using the InvaCost database and meta-regression modelling approaches. Overall, we found that both the cost reporting literature and monetary costs increased significantly over time at the global scale, but costs increased faster than reports. Differences in global trends suggest that cost literature has accumulated most rapidly in North America and Oceania, while monetary costs have exhibited the steepest increase in Oceania, followed by Europe, Africa and North America. Moreover, the costs for certain taxonomic groups were more prominent than others and the distribution also differed spatially, reflecting a potential lack of generality in cost-causing taxa and disparate patterns of cost reporting. With regard to global trends within the Animalia and Plantae kingdoms, costs for flatworms, mammals, flowering and vascular plants significantly increased. Our results highlight significantly increasing research interest and monetary impacts of biological invasions globally, but uncover key regional differences driven by variability in reporting of costs across countries and taxa. Our findings also suggest that regions which previously had lower research effort (e.g., Africa) exhibit rapidly increasing costs, comparable to regions historically at the forefront of invasion research. While these increases may be driven by specific countries within regions, we illustrate that even after accounting for research effort (cost reporting), costs of biological invasions are rising.

Optimized selection of axial pile bearing capacity predictive methods based on multi-criteria decision-making (MCDM) models and database approach

Optimized selection of axial pile bearing capacity predictive methods based on multi-criteria decision-making (MCDM) models and database approach

Agricultural Advisory Diagnostics Using a Data-Based Approach: Test Case in an Intensively Managed Rural Landscape in the Ganga River Basin, India

Low technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.

Robust Fault Diagnosis using a Data-based Approach and Structural Analysis

This paper presents a fault diagnosis approach that combines structural and data-driven techniques. The proposed method involves two phases. As a first step, the residuals structure is obtained from the structural model of the system by using structural analysis without considering mathematical models (only the component description of the system). Secondly, the analytical expressions for residuals are derived from available historical data using a robust identification approach. Through adaptive nets, residuals are adjusted by determining an interval model that takes into account the uncertainties and noises affecting the system. In the diagnosis part, residuals are tracked and evaluated. The presence of inconsistent residuals can be regarded as a fault, therefore thresholds for each residual are introduced. In addition to detecting faulty scenarios, it is also possible to determine which is the most likely fault that occurred in the system. To accomplish such classification, the proposed approach implements a Bayesian reasoning that uses the FSM (Fault Signature Matrix) that is obtained from the structural analysis of the system and residual activation signals. A brushless DC motor (BLDC) is used as a case study to illustrate the proposed approach. Simulation experiments illustrate the overall performance.

Reinforcing Hurst Exponent with Oscillation Detection for Control Performance Analysis: An Industrial Application

Control performance assessment is one of the most important components of control assets monitoring. In the past decades, many different data-based approaches have been presented to assess control performance. Due to the high number of control loops for production industries, the methods requiring less parameters and less priori information became popular in industrial use. Detrended Fluctuation Analysis (DFA) is one of these methods, requiring only closed loop data and a pair of segment length to estimate Hurst exponent based control performance. However, the selection of segment lengths plays an important role in estimation, especially for oscillating control loops. In this paper, we have proposed an additional step (i.e. a step zero) to DFA to estimate control performance. This step uses an autocorrelation based and robust oscillation detection and characterization method to identify the oscillation period. Then, the calculated period is used to select the minimum segment length which leads more robust and precise estimation of control performance based on DFA. The proposed method has been applied and tested on several industrial control loops. The comparisons of the results between two approaches are given.

On the identification and assessment of underlying acoustic dimensions of soundscapes

The concept of soundscapes according to ISO 12913-1/-2/-3 proposes a descriptive framework based on a triangulation between the entities acoustic environment, person and context. While research on the person-related dimensions is well established, there is not yet complete agreement on the relevant indicators and dimensions for the pure description of acoustic environments. Therefore, this work attempts to identify acoustic dimensions that actually vary between different acoustic environments and thus can be used to characterize them. To this end, an exploratory, data-based approach was taken. A database of Ambisonics soundscape recordings (approx. 12.5 h) was first analyzed using a variety of signal-based acoustic indicators (Ni = 326) within the categories loudness, quality, spaciousness and time. Multivariate statistical methods were then applied to identify compound and interpretable acoustic dimensions. The interpretation of the results reveals 8 independent dimensions “Loudness”, “Directivity”, “Timbre”, “High-Frequency Timbre”, “Dynamic Range”, “High-Frequency Amplitude Modulation”, “Loudness Progression” and “Mid-High-Frequency Amplitude Modulation” to be statistically relevant. These derived latent acoustic dimensions explain 48.76% of the observed total variance and form a physical basis for the description of acoustic environments. Although all baseline indicators were selected for perceptual reasons, validation must be done through appropriate listening tests in future.

Deglobalization of Manufacturing Value Chains? A data-based approach

In manufacturing value chains (MVC), an increasing globalization used to be the persistent direction for decades. Recent crises massively disturbed global MVCs and lead to the question, if a trend reversal, a “deglobalization”, is about to happen. Some authors already see this trend reversal as a given, but did not provide any supporting data so far. This paper intends to shed light into the topic by reviewing underlying reasons, motives and key drivers for companies to operate in global MVCs, identifying corresponding indicators, and gathering data for these. The subsequent analysis of the data reveals a distinct slowdown of globalization growth rates, but no trend reversal, yet.

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing.

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

MPC Design for an Auditorium Building Using Data Driven Modeling

This paper describes the design of a model predictive controller (MPC) for preheating/-cooling of an auditorium building with a central air handling unit (AHU). The data-based approach uses system identification to estimate a low order nonlinear MPC model. The data used for identification is generated by simulation of a high order process model that was developed in Modelica using the open source Buildings library. This model is further used for validation of the predictive controller within Matlab/Simulink using Functional Mock-up Interface (FMI). Advantages of MPC compared to a conventional control strategy are simulatively proven using co-simulation, where an overall cost reduction of approx. 25% was achieved.

Study of aspects and factors for qualitative-descriptive assessment for multi-base classes I elementary school Perspective of aims and approaches

Background and Purpose: In response to the negative consequences of the traditional evaluation system and its reform, the Supreme Council of Education has replaced older methods with descriptive evaluation. Despite the good reception of this method, like any other innovation, it has faced challenges and criticism. Therefore, the purpose of the present study, is to study aspects and factors of qualitative -descriptive evaluation for multi-base elementary course from perspective of aims and approaches. Methods: Study method is qualitative approach and theoretical method for new risen data base. Participants were educational development assessment specialists, educational science specialists, curriculum and educational psychologist from all universities of the country and also staff managers of ministry of education in elementary education (whom had at least three indicated works in area of assessment, teaching in multi-base classes). considering the nature of this subject, theoretical targeted sampling was used. For analyzing data base of new risen approach in this study, open, theoretical and selective coding were used in this order. Results: results show that qualitative-descriptive evaluation must be process oriented with structuralism approach and be spread on multi-bases of student’s life as in all aspects, areas and dimensions. Conclusion: reporting structure to be qualitative and cause mental health growth and evade competitive environment and the multi-level final exams should be changed to multi-participatory exams.

A Reliable Model to Predict the Methane-Hydrate Equilibrium: An Updated Database and Machine Learning Approach

A Reliable Model to Predict the Methane-Hydrate Equilibrium: An Updated Database and Machine Learning Approach

Modeling and Simulation of Abnormal Behavior Detection Through History Trajectory Monitoring in Wireless Sensor Networks

Data security is becoming increasingly important with the growing popularity of smart management systems combining wireless sensor networks (WSNs) and intelligent systems in industrial, agricultural, and construction fields. Traditional security methods for WSN have focused on data integrity and the identification of outliers in sensor data and are therefore vulnerable to attacks such as false positive attacks (FPAs) and false negative attack (FNAs) using massively compromised nodes. these attacks significantly compromise the sensor nodes because the correlation between node verification behaviors is not considered in the communication process until the intercepted sensor data are used as input into the system. This study introduces an FPA and FNA detection method using a spatiotemporal historical data-based knowledge base. The main contribution of the present study is the recognition of abnormal correlations using behavior monitoring through the discrete event system specification model. By recognizing abnormal correlations, the proposed method prevents the inflow of false data generated as a result of widespread damage. Furthermore, a new strategy is proposed to maximize the lifetime of the network by blocking any compromised nodes. The proposed spatiotemporal data-based detection approach is applicable to a wide variety of fields owing to its use of a shared security model. The proposed method was shown to reduce the energy consumption by 46.737% and 41.927% in comparison to statistical en-route filtering and cluster-based false-data filtering, respectively.

Smoother-Based Iterative Learning Control for UAV Trajectory Tracking

This letter presents a data-based control approach to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). We revisit an existing Iterative Learning Control (ILC) algorithm based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions. While we will specifically refer to multirotor platforms for the experimental validation, the formulation can be applied to any dynamic system (including systems with underlying feedback loops). The novelty of this work is the introduction of a smoother to estimate the repetitive disturbance to improve the learning performance. This estimator must rely on an accurate system model that has been obtained through a black-box identification procedure using the Predictor-Based Subspace Identification (PBSID) algorithm. A Monte Carlo analysis has been carried out with the aim of showing the performance improvements and limitations of the proposed algorithm with respect to existing approaches. Finally, the proposed approach has been validated through experimental activities involving a small quadrotor performing an aggressive manoeuver.

Sensor Fault Tolerant Control for Hybrid Systems With Sinusoidal Disturbance

This paper aims to design of an active fault-tolerant control AFTC of switched systems in the presence of sensor fault and periodic disturbances. AFTC requires sensor fault estimation. Thus, a data-based projection approach is proposed based on performance measures that allow fault estimation without relying on mathematical models and fault assumptions. A new switching control strategy is proposed for fault compensation and recovering the desired performances. These techniques integrate a bank of controllers, corresponding to a set of partial models to design a set of switching control laws, compensating for the fault effect and attenuating the disturbance. A new linear matrix inequality based on Lyapunov stability analysis is proposed to compute control gains. This technique allows finding the optimal values of the control gains matrices, which ensure both sensor fault compensation and disturbance attenuation. A comparative study of the proposed strategy with existing work is carried out to show the effectiveness of the designed fault-tolerant control.

Data-driven control-oriented reduced order modeling for open channel flows

Model order reduction can be used for efficient simulation of complex systems. Data-based system identification approaches using neuronal networks or Dynamic Mode Decomposition enable us to extract characteristic properties of the system dynamics in order to reduce them to a low-dimensional space. There the temporal propagation can be described with significantly less computational effort. Both approaches are applied to the boundary actuated St. Venant equations, to obtain control-oriented reduced order models, which try to capture the dynamics of open channel flows for a wide range of input signals. It is investigated whether these models can be used for efficient simulation and how accurately they reconstruct the dynamic behavior of the water depth and velocity of an open channel.

A machine learning framework for LES closure terms

In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive a consistent framework for LES closure models, with special emphasis laid upon the incorporation of implicit discretization-based filters and numerical approximation errors. We investigate implicit filter types that are inspired by the solution representation of discontinuous Galerkin and finite volume schemes and mimic the behavior of the discretization operator, and a global Fourier cutoff filter as a representative of a typical explicit LES filter. Within the perfect LES framework, we compute the exact closure terms for the different LES filter functions from direct numerical simulation results of decaying homogeneous isotropic turbulence. Multiple ANN with a multilayer perceptron (MLP) or a gated recurrent unit (GRU) architecture are trained to predict the computed closure terms solely from coarse-scale input data. For the given application, the GRU architecture clearly outperforms the MLP networks in terms of accuracy, whilst reaching up to 99.9% correlation between the networks' predictions and the exact closure terms for all considered filter functions. The GRU networks are also shown to generalize well across different LES filters and resolutions. The present study can thus be seen as a starting point for the investigation of data-based modeling approaches for LES, which not only include the physical closure terms, but account for the discretization effects in implicitly filtered LES as well.

Toolbox Lean 4.0 - Development and Implementation of a Database Approach for the Management of Digital Methods and Tools

Lean Production Systems (LPS) comprise an enterprise-specific set of goals and principles in combination with methods and tools for the holistic management of production processes. LPS have been established as an industry standard in automotive and mechanical engineering industries. LPS design is a management task often attributed to industrial engineering units. In industrial practice, the management of LPS methods and tools is often realized using partly unsystematic catalogs, collections, wikis, and databases. With Industrie 4.0, the digital transformation of production, the aim is to develop Lean Production Systems 4.0 (LPS 4.0), based on a synergetic approach of LPS and Industrie 4.0.The evolutionary development towards LPS 4.0 brings new challenges for industrial engineering, especially with regard to increased complexity in the management of existing Lean methods, digitally enhanced methods, as well as completely new digital methods and tools. Therefore, this paper introduces a novel database approach for the systematic management of digital methods and tools. Based on requirements validated with industrial practitioners, the developed toolbox Lean 4.0 provides systematic support for the management of complexity in industrial engineering. Involving a broad industrial consortium, the toolbox is co-developed in iterative loops, based on multiple relevant sources, implemented in a prototype application, and continuously evaluated. The general toolbox framework can be instantiated individually to enterprise-specific needs and at the same time helps explore new pathways for applied research on production system design.

Data-based Approach to Predict Feasibility and Computational Requirement for Chemical Production Scheduling

Online scheduling requires frequent re-optimization to generate a schedule repeatedly accounting for updated information. However, if the time between re-optimizations is too short, then finding good, and in some cases even feasible, solutions can become challenging. This work proposed an approach, based on supervised learning techniques, to predict whether a given instance is feasible and, given that it is feasible, what is the computational requirement to solve the instance. Towards this goal, we introduce various types of features related to problem size, scheduling horizon, and processing times and costs that can be derived based on domain knowledge. Logistic regression and random forests models are trained as feasibility classifier and computational time regressor, respectively, using the dataset obtained from a wide variety of instances. Both show good predictive performances: F1 score ∼0.90 and AUC ∼0.98 for the feasibility classification and MSE ∼0.5 for the computational time prediction. Finally, we discuss the features that are shown to be significant in the cases of makespan minimization and cost minimization.