Detailed Data Processing Research Articles

Comparative Study of θ Projection Method and Its Modified Forms on Creep Life Prediction

The phenomenological characteristics of θ projection method constrain its prediction accuracy, especially at the primary creep stage. Herein, the mathematical features of the classical θ projection method and its modified forms based on a group of creep curves of a newly designed low‐cost heat‐resistant steel are analyzed. A detailed data processing method is introduced, and the prediction accuracy is comparatively studied with respect to the relative error on different creep stages. Results reveal that the simplified 3‐parameter method brings a balanced improvement on the residual errors by sacrificing the prediction accuracy on primary creep. However, the 5‐parameter method can precisely predict the characteristics of primary creep, and the tolerance fitting process can further reduce the deviation at the tertiary creep stage. Furthermore, the 5‐parameter method shows a larger reliable prediction region than the 3‐parameter method, and the two methods present good consistency when the applied stress is larger than 134 MPa. Herein, theoretical guidance for the selection of different θ projection methods is provided.

From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration

This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sensors, such as GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit), and odometry sensors, into the system’s architecture to accurately estimate the position and orientation of the LiDAR sensor at any given time. Specialized algorithms compensate for the vehicle’s motion, and procedural requirements must be followed to ensure safe operation. Hardware integration is critical, requiring proper calibration and validation. Data processing workflows, including algorithms and software tools, play a crucial role in mobile LiDAR mapping systems. The quality and accuracy of the data depend on various factors, and practical considerations such as cost and time are also important. The proposed methodology provides a clear and effective way to transform a terrestrial LiDAR sensor into a mobile mapping system, increasing its flexibility and usability for various applications. However, the detailed analysis, system validation, and data processing details fall outside this paper’s scope and will be discussed in future publications.

Protocols of extra-judicial instances as a source for the study of mass operations of 1937-1938 in the Kazakh SSR

This article characterizes such a massive source for the study of the Great Terror as an extrajudicial decision. During the period 1937–1938. The NKVD authorities carried out repression in three main areas, which included the “kulak” operation, a series of “national” punitive actions, as well as prosecutions against violators of the passport regime, criminal and declassed elements. Endowed with extrajudicial powers, the security officers independently sentenced those arrested to various penalties, including the death penalty. Extrajudicial bodies (“kulak”, police, Special troika, Special meeting under the NKVD of the USSR, Commission of the NKVD and the Prosecutor of the USSR), created to pronounce and approve sentences, recorded their repressive decisions. Until now, access to these types of sources is difficult, but they contain rich material for the study of mass operations. The researchers of this article managed to collect decisions of extrajudicial instances in the Kazakh SSR, deposited in Russian and Kazakh archives. Using their example, a description is given of each type of extrajudicial protocols that functioned during the years of the Great Terror. It is concluded that this type of sources is important material for the study of political terror, since detailed data processing allows us to draw conclusions about the scale of repression, target categories, mechanisms of implementation, both at the regional and union levels, and territorial features of persecution.

Protocols of extra-judicial instances as a source for the study of mass operations of 1937-1938 in the Kazakh SSR

This article characterizes such a massive source for the study of the Great Terror as an extrajudicial decision. During the period 1937–1938. The NKVD authorities carried out repression in three main areas, which included the “kulak” operation, a series of “national” punitive actions, as well as prosecutions against violators of the passport regime, criminal and declassed elements. Endowed with extrajudicial powers, the security officers independently sentenced those arrested to various penalties, including the death penalty. Extrajudicial bodies (“kulak”, police, Special troika, Special meeting under the NKVD of the USSR, Commission of the NKVD and the Prosecutor of the USSR), created to pronounce and approve sentences, recorded their repressive decisions. Until now, access to these types of sources is difficult, but they contain rich material for the study of mass operations. The researchers of this article managed to collect decisions of extrajudicial instances in the Kazakh SSR, deposited in Russian and Kazakh archives. Using their example, a description is given of each type of extrajudicial protocols that functioned during the years of the Great Terror. It is concluded that this type of sources is important material for the study of political terror, since detailed data processing allows us to draw conclusions about the scale of repression, target categories, mechanisms of implementation, both at the regional and union levels, and territorial features of persecution.

Design of Bad Information Filtering System for Web Pages

The openness and globalization of the Internet make the information spread on the network mixed, and all kinds of advertisements, spam, reactionary, obscene, violence and other bad information seriously interfere with the normal use of the Internet by Internet users. This paper studies the workflow of the web page bad information filtering system, analyzes the functional modules of the filtering system, and designs the network data processing, text data processing and adaptive processing modules in detail.

Current Centre Line Control, Results and Comparison After the Manufacturing of the ITER Toroidal Field Coils

In 2021, seven Toroidal Field Coil (TF) are already built by European industries and delivered to the ITER Organization (IO). The TF are composed mainly by a superconducting Winding Pack (WP), and the enclosing Coil Cases (TFCC). One of the main parameters characterizing the TFCs is the Current Centre Line, defined as the barycentre of the WP conductors. The CCL has been initially calculated for all the TFCs, and Fusion for Energy (F4E) developed a methodology to monitor and control the CCL during the subsequent manufacturing processes, including WP insertion, TFCC welding, preparation for machining and final measurements. The method is based on detailed laser measurements, CAE models and data processing, and it provides one of the main inputs to be considered when defining the final machining of the component. The final CCL position after machining can be further used as input parameter during the machine assembly and it is useful to understand important aspects for the operation of ITER reactor such as the Error Field. This paper presents the results of the abovementioned strategy for the coils completed so far, during the different manufacturing phases, and the intermediate results of the remaining coils under construction. It focuses on the similarities and differences obtained comparing the TFs, and it assesses the correspondence of the CCL data with other parameters related to the magnet.

Single-Ended Quadrature Coupler-Based VSWR Resilient Joint mm-Wave True Power Detector and Impedance Sensor

Intelligent reconfigurable RF/millimeter-wave (mm-Wave) front ends primarily rely on built-in-self-test (BiST) circuits for real-time performance monitoring, particularly in phased arrays, where antenna elements&#x2019; driving impedance varies substantially during beam steering due to the undesired element couplings. This severely degrades the power amplifier&#x2019;s (PA) performance. To address this problem, we propose a quadrature coupler-based voltage standing wave ratio (VSWR)-resilient mm-Wave joint impedance/true power detector integrated with a PA using the GlobalFoundries 45-nm CMOS silicon on insulator (SOI) process. A comprehensive theoretical comparison between the traditional and proposed coupler sensors is presented, including implementation details and design tradeoffs. Design guidelines for both the proposed coupler sensor and the integrated PA are presented to maximize sensing accuracy. The detailed data processing techniques are explained to accurately extract the sensor outputs and highlight hardware limitations. At 38 GHz, the proposed sensor measures the antenna impedance for VSWR <inline-formula> <tex-math notation="LaTeX">$=3$ </tex-math></inline-formula>:1 with a maximum <inline-formula> <tex-math notation="LaTeX">$\vert \Gamma \vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\angle \Gamma $ </tex-math></inline-formula> error of 0.238 and 28.9&#x00B0; respectively. The 90&#x00B0; coupler network demonstrates a 16-dB dynamic range with an error within &#x00B1;0.5 dB for 50-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> power sensing, as well as sensing the real power delivered to a complex antenna load for VSWR <inline-formula> <tex-math notation="LaTeX">$=3$ </tex-math></inline-formula>:1 with less than &#x00B1;3.35-dB power sensing error. The chip die occupies an area of <inline-formula> <tex-math notation="LaTeX">$1.31\times1.36$ </tex-math></inline-formula> mm², while the sensor alone occupies an area of <inline-formula> <tex-math notation="LaTeX">$0.47\times0.971$ </tex-math></inline-formula> mm².

Integrative ‐omics for discovery of network‐level disease biomarkers for Alzheimer’s disease

AbstractBackgroundA large number of genetic variations have been identified to be associated with Alzheimer’s disease (AD) and related traits. But the downstream biology through which they exert effect on the development of AD remains unknown. Leveraging genomic, transcriptomic and proteomic data, and various biological networks, we proposed a modularity‐constrained logistic regression model (M‐logistic) to identify a set of functionally connected SNPs, genes and proteins related to AD.MethodThe GWAS genotype data, RNA‐Seq gene expression data and Protein expression data were obtained from the ROS/MAP Project. Detailed data processing steps can be found in AMP‐AD (https://www.synapse.org). In total, 179 subjects with full set of data were included (Table. 1). Using peptides as seed, we prefiltered the data and included 186 peptides, 743 unique genes and 822 Single‐nucleotide polymorphisms (SNPs) from upstream of their connected genes (Fig. 1). Functional interactions from REACTOME database [1] and the SNP‐gene mapping relationship were used to construct the prior network. These functional connected ‐omics features were used to classify AD patients from non‐symptomatic group, including cognitive normal and mild cognitive impairment.ResultAcross 10‐fold cross‐validation, we observed that M‐logistic largely outperforms other state‐of‐the‐art models (Table. 2). For feature selection, the proposed model identified 305 features predictive of disease status, which appeared in more than 6 folds. When mapped back to the prior network, these features formed three big sub‐networks with &gt; 10 nodes (Fig. 2). For 20 genes in the largest sub‐network, their brain‐wide expression profile in the Allen Human Brain Atlas were found to be mostly correlated with the activation map of brain function term “vision” in Neurosynth [2] (Fig. 3).ConclusionM‐Logistic identified a set of SNPs, genes and proteins that are not only predictive of AD but also densely connected in the prior network. The trans‐omic paths in the selected sub‐networks indicates a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes. Future efforts are warranted to enable integrative analysis of multi‐omics data from decoupled subjects. [1] Fabregat et al., Nucleic acids research, 2018. [2] Yarkoni et al., Nature Method, 2011.

AOL4PS: A Large-scale Data Set for Personalized Search

Personalized search is a promising way to improve the quality of Websearch, and it has attracted much attention from both academic and industrial communities. Much of the current related research is based on commercial search engine data, which can not be released publicly for such reasons as privacy protection and information security. This leads to a serious lack of accessible public data sets in this field. The few publicly available data sets have not become widely used in academia because of the complexity of the processing process required to study personalized search methods. The lack of data sets together with the difficulties of data processing has brought obstacles to fair comparison and evaluation of personalized search models. In this paper, we constructed a large-scale data set AOL4PS to evaluate personalized search methods, collected and processed from AOL query logs. We present the complete and detailed data processing and construction process. Specifically, to address the challenges of processing time and storage space demands brought by massive data volumes, we optimized the process of data set construction and proposed an improved BM25 algorithm. Experiments are performed on AOL4PS with some classic and state-of-the-art personalized search methods, and the experiment results demonstrate that AOL4PS can measure the effect of personalized search models.

Improving crosshole ground‐penetrating radar full‐waveform inversion results by using progressively expanded bandwidths of the data

ABSTRACTIn the last decade, time‐domain crosshole ground‐penetrating radar full‐waveform inversion has been applied to several different test sites and has improved the resolution and reconstruction of subsurface properties. The full‐waveform inversion requires several diligent executed pre‐processing steps to guarantee a successful inversion and to minimize the risk of being trapped in a local minimum. Thereby, one important aspect is the starting models of the full‐waveform inversion. Generally, adequate starting models need to fulfil the half‐wavelength criterion, which means that the modelled data based on the starting models need to be within half of the wavelength of the measured data in the entire investigation area. Ray‐based approaches can provide such starting models, but in the presence of high contrast layers, such results do not always fulfil this criterion and need to be improved and updated. Therefore, precise and detailed data processing and a good understanding of experimental ground‐penetrating radar data are necessary to avoid erroneous full‐waveform inversion results. Here, we introduce a new approach, which improves the starting model problem and is able to enhance the reconstruction of the subsurface medium properties. The new approach tames the non‐linearity issue caused by high contrast complex media, by applying bandpass filters with different passband ranges during the inversion to the modelled and measured ground‐penetrating radar data. Thereby, these bandpass filters are considered for a certain number of iterations and are progressively expanded to the selected maximum frequency bandwidth. The resulting permittivity full‐waveform inversion model is applied to update the effective source wavelet and is used as an updated starting model in the full‐waveform inversion with the full bandwidth data. This full‐waveform inversion is able to enhance the reconstruction of the permittivity and electrical conductivity results in contrast to the standard full‐waveform inversion results. The new approach has been applied and tested on two synthetic case studies and an experimental data set. The field data were additionally compared with cone penetration test data for validation.

Initial scalar lithospheric magnetic anomaly map of China and surrounding regions derived from CSES satellite data

The China Seismo-Electromagnetic Satellite (CSES), China’s first satellite to measure geophysical fields with scientific goals in both space and solid earth physics, was launched successfully in February 2018. It carries high-precision magnetometers to measure the geomagnetic field. In this study, the CSES magnetic data were used to extract the signal of the lithospheric magnetic field caused by magnetized rocks in the crust and uppermost mantle. First, an along-track analysis of the CSES magnetic data was undertaken near the Bangui magnetic anomaly in central Africa and the Tarim magnetic anomaly in China, demonstrating that the CSES magnetic data are indeed sensitive to the lithospheric magnetic anomaly field. Then a lithospheric magnetic anomaly map over China and surrounding regions was derived. This map is consistent with the lithospheric part of the CHAOS-7 model. In particular, it clearly reveals four major magnetic anomalies containing long-wavelength signals at the altitude of low-earth-orbiting satellites. Three magnetic highs are located over the Tarim, Sichuan and Songliao basin, the origins of which could be related to large-scale tectonic-magmatic activities during geological history. A prominent magnetic low is otherwise found in the southern Himalayan-Tibetan plateau, possibly caused by the shallow Curie depth in this region. To further improve the precision of the lithospheric magnetic field model, more detailed data processing and multi-source data merging are needed.

Multi-Mode Surface Generalization Supports a Detailed Urban Flooding Simulation Model

With the aim of achieving high precision and high efficiency, recent research on hydraulic flood models has tended to focus on the algorithms for solving the shallow water equations of Saint-Venant. While development of the algorithms will help to improve the simulation precision and the solving of specific problems, the other influential factor in flood risk modeling is the precision and reasonable generalization of the data. This is even more important for increasing the model’s computational accuracy and efficiency but is frequently undervalued. Frequent rainstorm waterlogging is having a serious impact on China’s large cities. Early warning of waterlogging risk following a rainstorm forecast is an effective method for preventing or reducing potential losses. Concrete waterlogging information including locations, depth, and process is essential for early warnings. This paper focuses on the influence of data precision and reasonable generalization on an urban flooding model. A detailed hydraulic urban flooding model characterized by detailed data processing and component coupling can help to provide advance information. In Beijing city, road networks, overpasses, and buildings are so highly concentrated that rainstorms easily result in waterlogging in low-lying locations. Therefore, partial microrelief is the determinate factor in waterlogging simulation. This paper shows that multi-mode surface data generalization and detailed model coupling support a perfect simulation of a Beijing urban flooding model. The influence of buildings, roads, and overpasses on surface water flow was carefully considered based on the innovative aspect of a fine generalization of partial microrelief. One novelty is the simulation of the whole overland flow from the beginning of rainfall to mesh, not only from manhole overflow, because the waterlogging is caused mainly by initial surface water runoff rather than by manhole overflow. A second novelty is the use of a new kind of coupling mode based on physical mechanisms between surface and pipe models. Here, rain perforated strainers instead of manholes play a role as flow exchange points between pipe and surface. This coupling mode is much closer to the real world. Based on a detailed Beijing urban flooding model, a scenarios library of typical rainfall events and corresponding waterlogging results was constructed. Several years of practice have demonstrated that a rich library of scenarios can be used effectively for the quick identification and early warning of waterlogging risk for a forecast rainfall. Test results show that multi-mode surface generalization is effective in improving outcomes and useful for scientific decision making in controlling urban waterlogging.

Internet Appendix to `Forecasting Uncertainty of the Oil Future Prices via Machine Learning'

The Internet Appendix consists of three sections. Section A shows data sources and detailed data processing procedures. In Section B, we outline seven forecasting models. Last, Section C represents the empirical results.

Fast decision-making tool for monitoring recirculation aquaculture systems based on a multivariate statistical analysis

Aquaculture requires the monitoring of several parameters simultaneously such as temperature, salinity, ammonia, potential of hydrogen, nitrogen dioxide, bromine, among others to preserve the well-being of the fish, optimize production and meet the requirements of environmental ethics. To contribute to the fulfillment of these requirements, we propose here to use a data analysis technique called principal component analysis (PCA). This data processing technique is a mathematical procedure that performs a transformation on the data to convert possibly correlated parameters into a new set of parameters called principal components. Once the PCA performs an exploratory analysis on the data, we can use it to reveal the internal structure of the aquaculture data, to explain not only the variance in the data, but also the correlation between them. The choice of PCA in this study was based on the fact that it is a multivariate statistical technique of data that is very well known and widely applied in several scientific fields. Furthermore, the PCA allows to reduce the dimensionality of datasets, increasing their interpretability, but at the same time minimizing the loss of information. Results obtained with the use of PCA on the data related to the weaning and pre-fattening phases of Senegalese sole (species Solea senegalensis) collected during the years 2018 and 2019, respectively, show the feasibility of this proposal in the simultaneous monitoring of aquaculture parameters and take future critical conclusions to predict events in the aquaculture data analysis. Furthermore, there are no records to date of studies in the aquaculture industry in terms of data processing and analysis in detail. Currently, the scientific literature in the area of aquaculture exhibits a consistent body of knowledge based on the characterization of the physiology and environment of fish, with research on instrumentation still scarce. In this perspective, this study not only represents a substantial novelty for the field of aquaculture for performing the data processing directed at the aquaculture industry for the first time, but it will also allow the development of a new branch of research focused specifically in a support tool for aquaculture recirculating systems-based industry.

Assessment of Inter-Laboratory Variation in the Characterization and Analysis of the Mucosal Microbiota in Crohn's Disease and Ulcerative Colitis.

BackgroundIn studies evaluating the microbiome, numerous factors can contribute to technical variability. These factors include DNA extraction methodology, sequencing protocols, and data analysis strategies. We sought to evaluate the impact these factors have on the results obtained when the sequence data are independently generated and analyzed by different laboratories.MethodsTo evaluate the effect of technical variability, we used human intestinal biopsy samples resected from individuals diagnosed with an inflammatory bowel disease (IBD), including Crohn’s disease (n = 12) and ulcerative colitis (n = 10), and those without IBD (n = 10). Matched samples from each participant were sent to three laboratories and studied using independent protocols for DNA extraction, library preparation, targeted-amplicon sequencing of a 16S rRNA gene hypervariable region, and processing of sequence data. We looked at two measures of interest – Bray–Curtis PERMANOVA R2 values and log2 fold-change estimates of the 25 most-abundant taxa – to assess variation in the results produced by each laboratory, as well the relative contribution to variation from the different extraction, sequencing, and analysis steps used to generate these measures.ResultsThe R2 values and estimated differential abundance associated with diagnosis were consistent across datasets that used different DNA extraction and sequencing protocols, and within datasets that pooled samples from multiple protocols; however, variability in bioinformatic processing of sequence data led to changes in R2 values and inconsistencies in taxonomic assignment and abundance estimates.ConclusionAlthough the contribution of DNA extraction and sequencing methods to variability were observable, we find that results can be robust to the various extraction and sequencing approaches used in our study. Differences in data processing methods have a larger impact on results, making comparison among studies less reliable and the combined analysis of bioinformatically processed samples nearly impossible. Our results highlight the importance of making raw sequence data available to facilitate combined and comparative analyses of published studies using common data processing protocols. Study methodologies should provide detailed data processing methods for validation, interpretability, reproducibility, and comparability.

Measurement of electro-optical coefficients based on the Stokes vectors

A method based on the Stokes vectors was developed for the measurement of electro-optical (EO) coefficients, and the detailed test principle, experimental apparatus and data processing were described. The practicability of this method was illustrated by the determination of the EO coefficient of lanthanum-modified lead zirconate titanate (PLZT) transparent ceramics, and the measurement results indicated that the standard deviation values of the EO coefficients obtained by the Stokes vector method was almost half of that of the values measured using the Sénarmont compensator method. The apparatus used for the Stokes vector method consists of only a few optical elements and was easy to adjust and calibrate, thereby enabling scientific, accurate, and efficient EO coefficient measurements for the development of EO materials and devices.

Exploring the Essence of the Right to Data Protection and Smart Cities

This report has been written in the context of the SPECTRE project. SPECTRE stands for 'Smart city Privacy: Enhancing Collaborative Transparency in the Regulatory Ecosystem'. It is a four-year interdisciplinary research project funded by FWO (Flanders Research Foundation - Belgium). The main goal is to examine privacy and data protection challenges in the smart city from the perspective of law, communication studies and economics and to propose solutions to address the privacy/utility challenge that more and more cities are called to face. The report aims to provide an analysis of the ‘essence’ of the right to data protection and how it is challenged by the smart city paradigm that leaves individuals with limited opportunities for control over their personal data. The focus is on the fundamental rights component of data protection and Article 8 of the EU Charter of Fundamental Rights rather than the secondary legal framework regulating personal data processing in detail. Even though in the day-to-day legal practice secondary acts like the General Data Protection Regulation dominate the scene, we consider that at both a conceptual and pragmatic level, legal analyses of the scope, interests and values of the right to data protection and the conditions under which it can be limited are essential for the application and future shaping of data protection law. The report transposes the findings of the analysis in the smart city context to demonstrate the tensions between the right to data protection, centred around control and the need to counter power and information asymmetries, and the paradigm of smart city processing.

Ultimate strength prediction of T-bar stiffened panel under longitudinal compression by data processing: A refined empirical formulation

In the present study, a data processing technique was introduced to develop a closed form shape empirical formulation in predicting ultimate strength of structures. The proposed method was verified by applying a ship's stiffened panel as an applied example. In particular, a refined empirical formulation in predicting the ultimate strength of stiffened panel subjected to longitudinal compression was proposed. Recently, Kim et al. (2017) observed that the ultimate strength behaviours fluctuated in small value of column slenderness ratio and urged the need for a more accurate empirical formulation. In order for an accurate ultimate strength behaviour of stiffened panel to be obtained, a total of 10,500 cases of numerical simulation results using the ANSYS Finite Element Method (FEM) were employed by considering the relevant size change of stiffened panels including plate thickness, web thickness, flange thickness, height of web, and breadth of flange. The simulation results were processed, in the case of initial imperfection, only for average level of initial deflection to plate. On the other hand, initial distortion to stiffener elements and no residual stress by welding were considered in this study. A detailed data processing technique and detailed modelling procedures, i.e, the scenario selection, FE modelling, FE analysis, were documented. From the obtained data processing results by this study, we have found that four (4) important parameters, i.e., P1=λ,P2=β,P3=hw/twandP4=Ipz/Isz are to be considered for the formulation of empirical formulation in predicting ultimate strength of stiffened panel under longitudinal compression (σxu/σYeq). The proposed new empirical formulation revealed positive agreement with ANSYS FEM results (R2 = 0.98 for overall case).

Parameters Measurement of Nuclear Graphite Based on Digital Image Correlation

This paper develops a method for measuring the mechanical parameters of nuclear graphite based on digital image correlation (DIC) method, and gives detailed data processing method and measurement procedure. The experimental results show that the Young's modulus and Poisson's ratio extracted from DIC data are in good agreement with those from the strain-gauge-based method, which verifies the feasibility and accuracy of the DIC method. In addition, the complete stress-strain relationship was obtained from DIC method, confirming the superiority of DIC method in measuring the mechanical parameters of materials with large deformation. Furthermore, the DIC method is easy to operate and efficient in identify the loading misalignment, making it convenient in large number of tests and reliable for the data acquisition for brittle materials testing.

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. The ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.