Database Approach Research Articles

A reliable model to predict the methane-hydrate equilibrium: An updated database and machine learning approach

Gas hydrates are a type of crystalline compounds that consists of water and small gas molecules. A wide range of applications of gas hydrates in storing natural gas in the form of artificially created solid hydrates, known as solidified natural gas technology, gas separation processes, and seawater desalination technology, has attracted great interest in scientific and practical studies. Gas hydrate formation may also cause deleterious effects such as blockage of gas pipelines. Therefore, accurate prediction of equilibrium conditions for gas hydrates is of great interest. The purpose of this study was to propose machine learning based models to predict methane-hydrate formation temperature for a wide range of brines. In this regard, firstly, a comprehensive database including 987 data samples covering 15 different brines was gathered from the literature. After data cleaning and preparation, three different models of multilayer perceptron, decision tree, and extremely randomized trees were used and tested. The results showed that the extremely randomized trees is capable of predicting methane-hydrate formation temperature with good accuracy. The root mean squared error for this model for the testing dataset was acquired as 0.6248, which shows its great accuracy. The findings of this study can be used as a reliable tool to predict the methane-hydrate formation PT curve in the pure water, single-salt brines, and multi-salt brines.

Data-Based Approach to Educational Programs Quality Management

The purpose of this article is to describe and justify the approach to assessing the quality of educational programs that have been developed and tested at ITMO University since 2019. The article presents the main indicators of assessment and the results of testing this approach at the university. The main unit of quality is an educational program, as it has a long-life cycle and meets the needs of most stakeholders of the educational organization. Taking into account all the features of an educational program, the authors propose a comprehensive set of indicators to assess its quality, which makes it possible to objectively manage it data-based. In conclusion, for completing the task of analyzing the life cycle of an educational program, the authors present an internal rating of educational programs, concise examples of recommendations based on its results. The advantages of the approach to managing educational programs are described, and ways for further development are presented. The article may be of interest to the managers and management teams of institutions of higher education, as well as to the researchers in this field.

Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: Application to planar soft tissues

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, focusing on planar, hyperelastic and incompressible soft tissues. Specifically, the strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress–strain data obtained from biaxial stretching experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser–Ogden–Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. The results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions.

Redesign the model of selection and appointment of managers of the social security organization based on competency models with a data-based approach

Redesign the model of selection and appointment of managers of the social security organization based on competency models with a data-based approach

Impacts of COVID-19 on Ship Behaviours in Port Area: An AIS Data-Based Pattern Recognition Approach

The advent of the COVID-19 pandemic disrupted global commercial activities and the tourism industry heavily. Impacts on maritime transportation were huge, as seaborne trade represents over 80% of global merchandise trade. Investigating how COVID-19 has affected ship behaviours is significant for economic condition evaluation, port management. This paper develops an analysis method to mine knowledge from raw Automation Identification System (AIS) data. First, berths are identified by improved density-based spatial clustering of applications with noise by Pythagoras distance (PD-DBSCAN). Data features, such as ship deadweight, arrival time, dwelling time, ship types, etc., can then be extracted using information matching and statistical analysis. Next, the dynamic time warping method is employed to analyse abnormal ship behaviour patterns and quantify the impacts of COVID-19. After that, a significance test is employed to determine an impact threshold through year-on-year analysis on ship flow, daily throughout and berthing time of quays. Finally, statistical analysis is used for the short-term impact analysis. This research examines a case study based on four-year AIS data in the Oslo port area. The results show that the proposed method can identify abnormal patterns caused by COVID-19 and estimate its impacts. Passenger ships are influenced heavily compared with cargo ships. The variation of passenger ships’ flow is over 90% during 2020, larger than the average variation before 2020. The discovered knowledge could be used for future decision-making and preplanning in the next health crisis.

Pragmatic functions of evidentiality in diplomatic discourse: Toward a new analytical framework.

This paper examines the pragmatic functions of evidentiality categories in diplomatic discourse by illustrating a new classification of English evidentiality. It adopts a data-based approach by analyzing a corpus of thirty English political speeches from three US presidents (including Bush, Obama, and Trump). The results show that: (i) Evidentiality can be classified into three categories: personal sources; shared sources and other sources. (ii) Besides the function of (de)legitimation, evidentiality can also be used to normalize the speaker's ideology. (iii) Shared sources of evidentials reflect the speaker's ideological bias, because they encode the speaker's presupposition of authority, facts, or shared knowledge. (iv) Personal sources of evidentials mean that the speaker is more willing to take verbal responsibility. (v) Other sources of evidentials reflect the speaker's lower responsibility for the information he/she offered. (vi) The use of the three evidential sources reflects the speakers' different responsibilities for their propositions and reveals their subjective or intersubjective stance.

User Authentication Recognition Process Using Long Short-Term Memory Model

User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.

Development of a Suicide Prevention Policies Based on the Busan Metropolitan Injury Surveillance System

Purpose: The purposes of this study was to develop the suicide prevention policy and solutions base on the statistics from the Busan Metropolitan injury surveillance system. Methods: The Annual Statistics on Causes of Deaths from Statistics Korea, Statistics on Unnatural Deaths from the National Police Agency and the National Safety Index of the Ministry of the Interior and Safety were used for analysis. The collected data were statistically analyzed with frequencies and percentages with IBM’s SPSS Window 27.0 program. Results: The high risk factors of suicide were identified as middle age men, elderly people, people with physical or mental health problem and unemployed person. The most common place of suicide was in the home by hanging oneself. Conclusion: Suicide is a comprehensive result of social, economic, psychological, and physical factors. It is necessary to use an injury surveillance system to scientifically analyze suicides with evidence or data. The matrix identified the why, when, who, and where related to suicide, and it was possible to identify intervention measures to solutions. This study will be provide an guideline of a data-based suicide prevention approach.

Ecological Impacts and Socio-Legal Infrastructure as an Approach to Environmental Management in Ex-Mining Land Reclamation

Mining, along with plantations, is one of the main economic backbones of the Indonesian provinces of Kalimantan. The main concern of extractive economics is deforestation and environmental damage that threatens natural sustainability. Most of the previous research focused on the issue of environmental sustainability in the industrial context and regional spatial planning. To fill this void, this study originally aims to analyze how local wisdom is useful in managing ex-mining reclamation practices. This research was conducted in Margahayu, Kutai Kertanegara, East Kalimantan Province. The method used in this research is empirical legal research by adopting a data-based approach. The results show that ex-mining reclamation in Margahayu aims to restore the land use according to its function and is beneficial for agriculture and small-scale plantations. The findings underline that the participation of local communities is very useful in restoring the function of the ex-mining land, due to their interest in rehabilitating spatial planning and ecological supports that are useful for their livelihoods. In this context, this finding requires inclusion of local interest-based participation as an important social infrastructure in reforestation and mine land reclamation in Kalimantan.

An advection-reaction model of emerging contaminants in ground and surface waterbodies with known topology: a data-based approach

A simple data-based advection-reaction (reactive transport) model applicable to both rivers and aquifers monitoring networks is proposed. It is built on (a) available monitoring data, and (b) graph-theoretical concepts, specifically making use of the Laplacian matrix to capture the network topology and the advection process. The method yields useful information regarding the dynamic spatial behavior of the variables monitored, expressed in terms of quantitative parameters like characteristic length, entropy, first-order decay constants, synchronization between sites, and the external inputs/outputs to the system.The model was tested in an unconfined shallow aquifer located in the lower Besòs River (Spain), in which 37 pharmaceutical compounds were monitored at 7 sites, alongside two campaigns (February and May 2021). Characteristic lengths were, on average, of the same order (24.5 m) as the mean distance between consecutive monitoring sites (33.6 m), thus reflecting an adequate monitoring network design. From an estimated mean advection velocity (0.24 m·h−1), first-order decay constants were calculated for each compound and campaign, with mean values of 0.025 h−1 (February) and 0.005 h−1 (May). Whereas entropy was generally slightly larger values in February than in May (mean values of 1.02 and 0.9 entropy units respectively), synchronization showed the opposite trend (mean values of 62.4% and 68.8% respectively). The input/output profiles were generally site-dependent, regardless of the compound, and campaign considered. • A new advection-reaction modeling approach directly based on experimental data obtained from monitoring campaigns together with the network topology is proposed. • The method yields new quantitative information regarding the dynamic behavior of the variables monitored, useful for both research and management purposes.

Building with Nature—Ecosystem Service Assessment of Coastal-Protection Scenarios

The aim of this study is to assess existing conventional and hypothetical nature-based coastal-protection schemes using a comparative ecosystem service assessment, based on quantitative data and literature as well as on stakeholder views. We assessed three conventional groin systems and three building-with-nature scenarios including an expanded beach area, a mussel farm and seagrass beds. Stakeholders perceived the nature-based scenarios as positive and assumed an overall increase in the ecosystem service provision. The quantitative data-based approach showed similar results. Building-with-nature approaches were considered to provide economical and/or environmental benefits to human beings, beyond coastal protection and safety. Especially for the combination of coastal-protection measures with submerged vegetation in shallow waters, a strong increase in ecosystem service potential is assumed, e.g., on nature restoration as well as on touristic and landscape attractiveness. Our approach turned out to be suitable for assessing different coastal-protection scenarios with reasonable effort. Our methodology can help to catch the views of people, raise awareness on the multiple consequences of these measures and enable an improved and structured participatory dialogue with locals and stakeholders. Our approach may support coastal-protection planning and help to reduce local resistance against measures and their implementation.

Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams

Performance efficiency of data-based hybrid intelligent approaches to predict crest settlement in rockfill dams

A digital twin-based framework for multi-element seismic hybrid simulation of structures

This paper proposes a digital twin-based multi-element hybrid simulation (DMHS) framework to predict the nonlinear cyclic response of structural components (digital twin), e.g., seismic fuses, that are not physically tested due to laboratory limitations by leveraging the experimental test data collected from the physical test specimen (physical twin) during hybrid simulation. This data-based simulation approach can address biased results of hybrid simulation of structures that contain multiple critical components while improving the efficiency of the seismic hybrid simulation. The digital twin is trained in two phases: (1) passive (initial) training phase using past experimental test data before the hybrid simulation starts, and (2) recursive model updating phase using the active (real-time) data produced by the physical specimen during hybrid simulation. The passive training is achieved using the Prandtl–Ishlinskii (PI) hysteresis model combined with the sparse identification technique, while the recursive least-squares algorithm is used in the second phase as the model updating scheme. In particular, the sparse identification technique facilitates the selection of the optimal number of hysteretic model parameters in the passive training phase, which are then tuned in the model updating phase. The architecture of the proposed DMHS framework is first presented, followed by digital twin training steps. The application of the proposed DMHS is then demonstrated, and its simulation accuracy is assessed through virtual hybrid simulation of a two-storey steel buckling-restrained braced frame, which consists of a digital twin (second-storey brace) and a virtual experimental specimen (first-storey brace) integrated into the numerical model of the structure that is subjected to a set of earthquake ground motion accelerations. The results obtained from the verification study serve to validate the proposed architecture of the DMHS framework and evaluate the accuracy and efficiency of this technique in simulating the nonlinear seismic response of structural systems.

Call for Papers: Media and Moral Understanding

Call for Papers: Media and Moral Understanding

Guidelines for data-driven approaches to study transitions in multiscale systems: The case of Lyapunov vectors.

This study investigates the use of covariant Lyapunov vectors and their respective angles for detecting transitions between metastable states in dynamical systems, as recently discussed in several atmospheric sciences applications. In a first step, the needed underlying dynamical models are derived from data using a non-parametric model-based clustering framework. The covariant Lyapunov vectors are then approximated based on these data-driven models. The data-based numerical approach is tested using three well-understood example systems with increasing dynamical complexity, identifying properties that allow for a successful application of the method: in particular, the method is identified to require a clear multiple time scale structure with fast transitions between slow subsystems. The latter slow dynamics should be dynamically characterized by invariant neutral directions of the linear approximation model.

Estimating municipal economic activity: An alternative data-based approach

Remote sensing techniques permit characterizing urban landscapes through urban spectral indices that distinguish built from unbuilt areas. These indices are considered a good proxy for economic activity as they reflect the development of the country, state, or municipality. However, urban spectral indices easily confuse built-up areas with bare land, compromising economic estimates. This confusion is associated with the resolution of the satellite image, the spectral index, and the threshold value that classifies pixels as built-up or unbuilt areas. This research introduces the Constrained Double-threshold method that allows for the reduction of this confusion. The method allows constraining the confusion between built and unbuilt areas to a certain percentage (e.g., 10%), reducing this error by 2.9 times and the overall error by 7%. The paper results show that the capacity of the spectral index to estimate economic activity depends more on the confusion between built-up and non-built-up areas than on the overall classification error. The two best-performing spectral indexes showed high explanatory power (R = 0.88; R = 0.86) and prediction (R2 = 90; R2 = 0.62) of the economic activity of the municipalities. The estimated growth in economic activity between 2018 and 2019 obtained from satellite images of 7.2% is consistent with official data for civil construction of 6.9%.

Data-Driven Analysis of Short-Distance Freight Rail Transportation Potential: A Case Study of Log Movements in the U.S. Lake Superior Region

As freight rail transportation in the U.S.A. increasingly concentrates on high-volume and longer distance train movements, shippers in rural areas with fragmented freight volumes, lighter line densities, and shorter distances have struggled to maintain the rail share of their shipments. The forest products industry in the mostly rural Lake Superior region is no exception and has seen a transition toward truck transportation for its main raw material, logs. The forest industry has attributed the phenomenon to reduced rail access and increased rail rates, and asked us to use their log shipment and rail operations network data to explore if (1) improved access through shared sidings, re-opened sidings, or both, and (2) rail rate reductions would encourage a modal shift from trucks back to rail. We developed four integer/mixed-integer programming models and several scenarios to research the problem. We concentrated on (1) allowing log consolidation from multiple companies at rail sidings and (2) rail rate reductions with and without volume thresholds. Our results showed that additional flexibility through shared rail sidings, re-opening of unused/closed sidings, or both, has minimal impact on increasing the rail modal share. Rail rate discounts with or without minimum volume thresholds were able to encourage a modal shift, but growing volume through rate reductions may not be attractive from the rail service provider perspective. While the results were not encouraging, our models offer the industry a data-based approach for further investigations, such as the impacts of potential shifts to a shortline cost model, or targeted analysis of specific mills that showed greater promise for a modal shift.

A database approach to examine the relation between function and interventions in the adaptive reuse of industrial heritage

• Database to investigate existing examples’ interventions per original/new function. • A support tool to decide on facility to be reused, new function and/or intervention. • Estimation of intervention amount and reducing uncertainties in reuse process. • Possibility of unveiling some common intervention patterns in the reuse process. • The type/magnitude of intervention made on building elements during reuse process. Conservation of the industrial facilities, which reflect their period's production and construction technology, social, cultural, and economic life, is a subject of concern since 1960–70 s. In this respect, adaptive reuse is seen as one of the appropriate methods to conserve and supply their continuous use. In adaptive reuse process, there are some risks and uncertainties because of different parameters like the requirements about conservation of the building and about the new function, time, budget, etc. It is possible to alleviate them by examining existing reused examples adapted to a similar function and reviewing possible different reuse strategies and interventions suitable for the characteristics of the building to be reused together with their outcomes. Hereof, it is aimed to develop a database to be used as a guiding tool while scrutinizing a building to be reused, a new function and interventions, and thus help minimizing the possible risks and uncertainties. To provide information to this database, a systematic approach was developed to assess the relationship between new function and the interventions made for reuse. The approach and possible uses of the database in this process are explained through seven reused industrial facilities in Turkey adapted from food and textile production to assembly and education facilities.

Characterization and propagation of RTD uncertainty for continuous powder blending processes

Residence time distribution (RTD) is a probability density function that describes the time materials spend inside a system. It is a promising tool for mixing behavior characterization, material traceability, and real-time quality control in pharmaceutical manufacturing. However, RTD measurements are accompanied with some degree of uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, two approaches were developed in this work, namely model-based and data-based approaches. The model-based approach characterizes the RTD uncertainty via RTD model parameters and uses Monte Carlo sampling to propagate and analyze the effects on downstream processes. To avoid bias and possible reduction of uncertainty during model fitting, the data-based approach characterizes RTD uncertainty using the raw experimental data and utilizes interval arithmetic for uncertainty propagation. A constrained optimization approach was also proposed to overcome the drawback of interval arithmetic in the data-based approach. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty.

Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location

Background:Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inverse problem, inferring arterial properties from waveform measurements, is not well understood today. Consequently, uncertainties within the estimation hinder the diagnostic application of such methods. Method:This work contributes a publicly available data set measured at an in-vitro cardiovascular simulator, focusing on a set of input conditions (heart rate, waveform) and stenosis locations. Furthermore, a first attempt is undertaken to perform classification and regression on this data set using standard machine learning methods on features extracted from four peripheral pressure signals. Results:The locations of six different stenoses could be distinguished at high accuracy of 93%, where transfer function-based features outperformed features based solely on signal shape in almost all cases. Furthermore, regression on the stenosis position could be performed with a root mean square error of 2.4 cm along a 20 cm section of the arterial system using a shallow neural network. However, the performance difference between shape and transfer function features was not clear for this task. Conclusion:The data set contains 800 measurements and allows investigating the influence of different heart boundary conditions, such as heart rate and waveform shape, on classification and regression tasks. Extracting features that minimise this influence is a promising way of improving the performance of these tasks.