Conventional Data Models Research Articles

Air Quality Data Acquisition Through Temporal Data Modeling

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.

Stock markets from COVID-19 to the Russia–Ukraine crisis: Structural breaks in interactive effects panels

Financial markets are frequently exposed to a variety of crises at the national, regional, and global levels, with potentially heterogeneous effects on market performance. To make sound investment and policy choices, investors and policymakers are constantly concerned about the market's behavior during such times of extreme stress. This article explores the impact of the two recent crises—the Russian–Ukraine war and the COVID-19 pandemic—on equity markets using Karavias et al.’s (2022) panel data approach and daily data from January 2020 to April 2022. Unlike conventional panel data models, this novel technique assesses the presence and location of common structural breaks across the studied countries while accommodating unobserved heterogeneity and panel dependency. We hypothesize that the conflict's impact on global equity markets is heterogeneous and based on countries' economic-political connection or proximity to the war zone, notably among sanctioning countries (the G7) and non-sanctioning countries (the Russia-China-India triple or RIC). Our results suggest that the G7's financial markets are more sensitive to country-specific macroeconomic factors and commodity price changes during extreme market stress than those of the RIC triad. Moreover, the war has a stronger influence on the G7 stock markets through commodity prices, with a greater impact of natural gas and wheat prices for this group. These findings are consistent with the fact that markets in developed economies with an extreme reliance on commodities are more sensitive to crises and international conflicts.

The causality analysis of incipient fault in industrial processes using dynamic data stream transfer entropy

The propagation of the developing incipient fault embeds potential risks to the safety management of industrial processes. The transfer entropy (TE) based causality analysis method is an effective solution for early detection and timely intervention of fault propagation. However, the slow-varying incipient fault could not be analyzed timely since the conventional TE is restricted by the limited data processing capacity and fixed data selection mode. For real-time causality analysis of developing industrial fault, we proposed the dynamic data stream transfer entropy (DDSTE) algorithm and presented the DDSTE-based causality analysis method in this paper. Firstly, the conventional static data model is replaced by dynamic data stream model, which could be updated and expanded with continues import of new data and would not be restricted by the limited storage capacity. Secondly, concerning the varying timing feature of incipient fault, the adaptive data sampling window is designed to match with incipient fault evolution stages. Thirdly, the long-lasting fault sequence is coarse-grained to improve the calculation efficiency for on-line using. Compared to conventional methods, DDSTE-based causality analysis outperforms in rapid tracking of incipient fault propagation and accurate estimation of fault location. The data collected from a real coal gasification process is used to verify the reliability and superiority of the proposed method.

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis.

Detection of Unknown DDoS Attack Using Reconstruct Error and One-Class SVM Featuring Stochastic Gradient Descent

The network system has become an indispensable component of modern infrastructure. DDoS attacks and their variants remain a potential and persistent cybersecurity threat. DDoS attacks block services to legitimate users by incorporating large amounts of malicious traffic in a short period or depleting system resources through methods specific to each client, causing the victim to lose reputation, finances, and potential customers. With the advancement and maturation of artificial intelligence technology, machine learning and deep learning are widely used to detect DDoS attacks with significant success. However, traditional supervised machine learning must depend on the categorized training sets, so the recognition rate plummets when the model encounters patterns outside the dataset. In addition, DDoS attack techniques continue to evolve, rendering training based on conventional data models unable to meet contemporary requirements. Since closed-set classifiers have excellent performance in cybersecurity and are quite mature, this study will investigate the identification of open-set recognition issues where the attack pattern does not accommodate the distribution learned by the model. This research proposes a framework that uses reconstruction error and distributes hidden layer characteristics to detect unknown DDoS attacks. This study will employ deep hierarchical reconstruction nets (DHRNet) architecture and reimplement it with a 1D integrated neural network employing loss function combined with spatial location constraint prototype loss (SLCPL) as a solution for open-set risks. At the output, a one-class SVM (one-class support vector machine) based on a random gradient descent approximation is used to recognize the unknown patterns in the subsequent stage. The model achieves an impressive detection rate of more than 99% in testing. Furthermore, the incremental learning module utilizing unknown traffic labeled by telecom technicians during tracking has enhanced the model’s performance by 99.8% against unknown threats based on the CICIDS2017 Friday open dataset.

Process parameters design of squeeze casting through an improved KNN algorithm and existing data

Process parameters are key to the production and cast quality of squeeze casting, and conventional methods to obtain the process parameters are based on experiments, which is costly and time-consuming. Based on big data, this paper proposes a data-driven method of designing squeeze-casting process parameters. Based on the potential impact features and differences in the process influential factors on squeeze-casting process parameters, an E-TKNN algorithm was developed by improving the k-nearest neighbor (KNN) algorithm for data clustering to obtain the process parameters of a new squeeze cast based on the similarity principle and existing data. To promote similarity accuracy, the impact difference of the process influential factors is first weighted by assembling the data of the process parameter and the factors using a matrix and calculating the entropy of their data matrix; second, a change trend measurement method is developed to consider the impacts of trace elements. Furthermore, an application framework (SR-E-TKNN) to design the process parameter of squeeze-casting through E-TKNN is established, in which a support vector machine–recursive feature elimination (SVM-RFE) algorithm is introduced to remove the redundant factors to optimize the data input of E-TKNN. Two experiments were conducted to verify the feasibility and superiority of the proposed method. The results demonstrated that the designed process parameter can aid in achieving practical production quality. Compared with the traditional design method, the accuracy of the design method based on E-TKNN outperforms conventional data modeling methods such as linear regression. This method utilizes the existing relevant squeeze-casting process data, eliminates the tedious research process, and provides a new concept for the design of other process parameters.

Detecting Injury Risk Factors with Algorithmic Models in Elite Women's Pathway Cricket.

This exploratory retrospective cohort analysis aimed to explore how algorithmic models may be able to identify important risk factors that may otherwise not have been apparent. Their association with injury was then assessed with more conventional data models. Participants were players registered on the England and Wales Cricket Board women's international development pathway (n=17) from April 2018 to August 2019 aged between 14-23 years (mean 18.2±1.9) at the start of the study period. Two supervised learning techniques (a decision tree and random forest with traditional and conditional algorithms) and generalised linear mixed effect models explored associations between risk factors and injury. The supervised learning models did not predict injury (decision tree and random forest area under the curve [AUC] of 0.66 and 0.72 for conditional algorithms) but did identify important risk factors. The best-fitting generalised linear mixed effect model for predicting injury (Akaike Information Criteria [AIC]=843.94, conditional r-squared=0.58) contained smoothed differential 7-day load (P<0.001), average broad jump scores (P<0.001) and 20 m speed (P<0.001). Algorithmic models identified novel injury risk factors in this population, which can guide practice and future confirmatory studies can now investigate.

Does industrial structure distortion impact the energy intensity in China?

Abstract Industrial structure change can affect aggregate energy intensity through sector transfer effect. There are still obvious structural distortions in the process of China's industrial structure change. This structural distortion will inevitably have an adverse impact on the allocation efficiency of resources (including energy), which may hinder the decline of aggregate energy intensity. Based on the idea of ​​equalized labor productivity across sectors under the equilibrium of free entry and exit, this paper measures the industrial structure distortion index of China's provinces from 1978 to 2016, and examines the impact of the structural distortion index on China's energy intensity by using conventional panel data model estimators. The results show that during the period of 1978-2016, China's industrial structure distortion index decreased significantly, and the distortion index was a key determinant of the energy intensity at the province level. We also found that energy price and import and export were important factors driving the decline of China's energy intensity, and FDI had an obvious negative effect, while R&D expenditure had no significant impact on it. In order to reduce the energy intensity, China should dedicate to eliminating the micro-roots of the industrial structure distortion, establish a market-based factor price formation mechanism.

Heterogeneity, transient and persistent technical efficiency of Polish crop farms

Accounting for heterogeneity in the measurement of farm efficiency is crucial to avoid biases related to climate and soil quality diversity in a given area. Therefore, this paper investigates the level of technical efficiency (TE) of Polish crop farms based on several stochastic frontier panel data models with different approaches to the measurement of unobserved heterogeneity, short- and long- run inefficiency. In our study, we show that ignoring farm heterogeneity can lead to underestimation of the level of TE in conventional stochastic frontier panel data models. Moreover, we have found empirically that not accounting for heterogeneity in the Generalized True Random Effects model may lead to incorrect estimates of persistent TE. The obtained results for Polish crop farms indicate that the level of transient TE (0.81) is lower than the level of persistent TE (0.88). This result suggests that Polish farms may have, for example, problems with adopting new technologies and poor managerial skills.

A heterogeneous coefficient approach to the knowledge production function

ABSTRACTPast literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. However, research conducted on regional innovation systems points to regional disparities in both regions’ ability to turn their knowledge inputs into innovation and to access external knowledge. Applying a heterogeneous coefficients spatial autoregressive panel model, we estimate region-specific knowledge production functions (KPFs) for 94 NUTS-3 regions in France using a panel covering 21 years from 1988 to 2008 and four high-technology industries. A great deal of regional heterogeneity in the KPF relationship exists across regions, providing new insights regarding spatial spillin and spillout effects between regions.

Sub-basalt Imaging of Hydrocarbon-Bearing Mesozoic Sediments Using Ray-Trace Inversion of First-Arrival Seismic Data and Elastic Finite-Difference Full-Wave Modeling Along Sinor\u2013Valod Profile of Deccan Syneclise, India

Imaging below the basalt for hydrocarbon exploration is a global problem because of poor penetration and significant loss of seismic energy due to scattering, attenuation, absorption and mode-conversion when the seismic waves encounter a highly heterogeneous and rugose basalt layer. The conventional (short offset) seismic data acquisition, processing and modeling techniques adopted by the oil industry generally fails to image hydrocarbon-bearing sub-trappean Mesozoic sediments hidden below the basalt and is considered as a serious problem for hydrocarbon exploration in the world. To overcome this difficulty of sub-basalt imaging, we have generated dense synthetic seismic data with the help of elastic finite-difference full-wave modeling using staggered-grid scheme for the model derived from ray-trace inversion using sparse wide-angle seismic data acquired along Sinor–Valod profile in the Deccan Volcanic Province of India. The full-wave synthetic seismic data generated have been processed and imaged using conventional seismic data processing technique with Kirchhoff pre-stack time and depth migrations. The seismic image obtained correlates with all the structural features of the model obtained through ray-trace inversion of wide-angle seismic data, validating the effectiveness of robust elastic finite-difference full-wave modeling approach for imaging below thick basalts. Using the full-wave modeling also allows us to decipher small-scale heterogeneities imposed in the model as a measure of the rugose basalt interfaces, which could not be dealt with ray-trace inversion. Furthermore, we were able to accurately image thin low-velocity hydrocarbon-bearing Mesozoic sediments sandwiched between and hidden below two thick sequences of high-velocity basalt layers lying above the basement.

A Heterogeneous Coefficient Approach to the Knowledge Production Function

We apply a heterogeneous coefficients spatial autoregressive panel model capable of producing estimates of knowledge production function parameters for each region in the sample. Past literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. These models summarize the relationship involving N regions over T time periods by restricting coefficients to be homogeneous across regions and time periods. Of course, we can allow for region-specific and time-specific fixed effects in an attempt to ameliorate the fact that we rely on homogeneous coefficients in the model. Introducing these allows for region- and time-specific differences in the model intercept. A heterogeneous coefficients spatial autoregressive (HSAR) panel model specification allows for variation in the level of spatial dependence/interaction as well as knowledge production function coefficients, intercepts and noise variances across each region. In many contexts (including our case of 94 French NUTS3 regions) a small group of regions within a country account for the majority of innovation activity. Some regions might exhibit knowledge production output that is sensitive to private R&D inputs, while others involve relationships where the knowledge base or public inputs are more important. Some regions might benefit from spill-out impacts of neighbors, while others do not. From an econometric viewpoint, it would be desirable to have estimates of the knowledge production function at the regional level that allow for differences in the level of interaction/spatial dependence between regions as well as knowledge production function coefficients, intercepts and disturbance variances. This type of model would allow inference regarding how knowledge output of each region responds to changing levels of own- and neighboring-region knowledge inputs, and how knowledge production is influenced by neighboring region production as well as time-invariant region-specific factors. We estimate region-specific knowledge production functions for 94 NUTS3 regions in France using a panel covering 21 years from 1988 to 2008 and 4 high-technology industries: chemistry, pharmaceutical, mechanics and materials. A great deal of regional heterogeneity in the knowledge production function relationship exists across regions, providing new insights regarding spatial spill-in and spill-out effects between regions.

Temporal Database Models Validation and Verification using Mapping Methodology

The majority of any temporal data model is claimed to be superset of the conventional relational data model as well as it has the ability to be represented in the main temporal data models in literature. In this paper, a complete and formal proof is provided in order to show that the A Tuple Timestamping Historical Relational (TTHR) temporal data model is a consistent extension of conventional relational model, and has the expressive power to be represented in other reference temporal data model and the modeling approaches in relational temporal data model.

Fiscal austerity, growth prospects, and sovereign CDS spreads: The Eurozone and beyond

This paper applies both conventional panel data models and a dynamic simultaneous equations model to analyze the impact of fiscal austerity and growth prospects along with other macro fundamentals on the pricing of sovereign credit default swaps (CDS) for a panel of 36 countries including the Eurozone. We find that austerity practice generally leads to an expectation of improved fiscal situations, which tends to help the CDS market grow more confidence in indebted sovereigns׳ ability to service their debt. The public debt to GDP ratio and projected future output growth also play an important role in determining the prices of sovereign debt insurance. Our analyses further show that the behavior of CDS spreads is considerably affected by common time trends over the sample period.

Fiscal Austerity, Growth Prospects, and Sovereign CDS Spreads: The Eurozone and Beyond

This paper applies both conventional panel data models and a dynamic simultaneous equations model to analyze the impact of fiscal austerity and growth prospects along with other macro fundamentals on the pricing of sovereign credit default swaps (CDS) for a panel of 36 countries including the Eurozone. We find that austerity practice generally leads to an expectation of improved fiscal situations, which tends to help the CDS market grow more confidence in indebted sovereigns’ ability to service their debt. The public debt to GDP ratio and projected future output growth also play an important role in determining the prices of sovereign debt insurance. Our analyses further show that the behavior of CDS spreads is considerably affected by common time trends over the sample period.

Two New Estimation Algorithms for Sensor Gain and Phase Errors Based on Different Data Models

Gain and phase errors sensitivity is an issue common to all high-resolution direction-of-arrival estimators. In this paper, we first provide the conventional and improved data models. Second, based on the conventional data model, an algorithm called estimation algorithm for the conventional data model (EACDM) is proposed to estimate the gain and phase errors existing in the sensor array for the uniform linear array (ULA). Finally, based on the improved data model, another algorithm called estimation algorithm for the improved data model (EAIDM) is proposed to estimate the gain and phase errors for the ULA. Derivation of these two proposed algorithms (EACDM and EAIDM) is similar. Moreover, the two proposed algorithms perform independently of phase errors, namely, the estimation accuracy is not affected regardless of how large the phase errors are. Furthermore, the computational complexity of the two proposed algorithms is lower than the conventional estimation method. Computer simulations are shown to verify the efficacy of the two proposed algorithms.

Aggregation and Labor Supply Elasticities

The aggregate Frisch elasticity of labor supply has played a key role in business cycle analysis. This paper develops a statistical aggregation procedure which allows for worker heterogeneity in observables and unobservables and is applicable to an individual labor supply function with non-employment as a possible outcome. Performing a thought experiment in which all offered or paid wages are subject to an unanticipated temporary change, we can derive an analytical expression for the aggregate Frisch elasticity and illustrate its main components: (i) the intensive and extensive adjustment of hours worked, (ii) the extensive adjustment of wages, and (iii) the aggregate employment rate. We use individual-specific data from the German Socio-Economic Panel (SOEP) for males at working-age in order to quantify each component. This data base provides indirect evidence on non-employed workers' reservation wages. We use this variable in conjunction with a two-step conditional density estimator to retrieve the extensive adjustment of hours worked and wages paid. The intensive hours' adjustment follows from estimating a conventional panel data model of individual hours worked. Our estimated aggregate Frisch elasticity varies between .63 and .70. These results are sensitive to the assumed nature of wage changes.

The use of STA Data Model in Learning and Teaching of Data Modeling

The development of a database needs to go through the process of data modelling. There are currently a few conventional data modelling techniques, one of which is popular among the students of Faculty of Information Science and Technology (FTSM: Fakulti Teknologi dan Sains Maklumat), National University of Malaysia is the entity relationship diagram (ERD). This research looks at the use of a new technique, the STA (Source-Transaction-Agent) data model, which is introduced to FTSM students who had gone for courses on Databases and System Analysis & Design. The STA data model is applied in the Accounting Information Systems (AIS) course. This model introduces the 5-steps technique in its data modelling. The use of business forms (source documents) is the priority of the modelling technique. The AIS students’ exposure to the conventional technique from their previous courses enables them to make comparisons between both the conventional and STA data models. The students were required to solve selected case studies by developing AIS using the STA data modelling technique. A set of pre and post questionnaire was distributed to each student, both prior to and after they were introduced to the STA data model. The research looks at the readiness of the students, and also the effectiveness and efficiency of the STA data model. A comparison between two groups of students from different sessions was also made. The results of the research prove the readiness of the students on conventional modelling technique and their acceptance of the new technique (STA data model). It is hoped that the STA data model is able to provide an effective and efficient alternative in business data modelling.

Nicotine Effects on Brain Function during a Visual Oddball Task: A Comparison between Conventional and EEG-informed fMRI Analysis

In a previous oddball task study, it was shown that the inclusion of electrophysiology (EEG), that is, single-trial P3 ERP parameters, in the analysis of fMRI responses can detect activation that is not apparent with conventional fMRI data modeling strategies [Warbrick, T., Mobascher, A., Brinkmeyer, J., Musso, F., Richter, N., Stoecker, T., et al. Single-trial P3 amplitude and latency informed event-related fMRI models yield different BOLD response patterns to a target detection task. Neuroimage, 47, 1532-1544, 2009]. Given that P3 is modulated by nicotine, including P3 parameters in the fMRI analysis might provide additional information about nicotine effects on brain function. A 1-mg nasal nicotine spray (0.5 mg each nostril) or placebo (pepper) spray was administered in a double-blind, placebo-controlled, within-subject, randomized, cross-over design. Simultaneous EEG-fMRI and behavioral data were recorded from 19 current smokers in response to an oddball-type visual choice RT task. Conventional general linear model analysis and single-trial P3 amplitude informed general linear model analysis of the fMRI data were performed. Comparing the nicotine with the placebo condition, reduced RTs in the nicotine condition were related to decreased BOLD responses in the conventional analysis encompassing the superior parietal lobule, the precuneus, and the lateral occipital cortex. On the other hand, reduced RTs were related to increased BOLD responses in the precentral and postcentral gyri, and ACC in the EEG-informed fMRI analysis. Our results show how integrated analyses of simultaneous EEG-fMRI data can be used to detect nicotine effects that would not have been revealed through conventional analysis of either measure in isolation. This emphasizes the significance of applying multimodal imaging methods to pharmacoimaging.

Geographic information systems for transportation: from a static past to a dynamic future

Geographic information systems for transportation earned its moniker of GIS-T because of active GIS-T research and applications carried out over the years. There were several GIS-T books and review papers published around the turn of the twenty-first century. As we are now 10 years into the new century, it is an appropriate time to assess the past accomplishments and look into the future of GIS-T. This article first discusses the ‘T’ in GIS-T with a review of the critical transportation issues identified by the US Transportation Research Board. Past GIS-T developments are then presented from three different perspectives of using GIS-T as a spatial database, using GIS-T as an analysis and modeling environment, and using GIS-T as a communication tool. Because of the static map layers view and the conventional GIS data models, past GIS-T developments are weak in handling dynamic movements, changes, and processes in transportation systems. Our world, on the other hand, has become increasingly dynamic because of the widespread use of modern information and communication technologies. GIS-T therefore must transform itself to accommodate this dynamic and changing world. A discussion of the dynamic future of GIS-T is presented in this article.