Test Data Research Articles

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.

Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga

Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. The classification process can be performed by examining external features such as fruit color, shape, and size. Classifying different types of mango fruits accurately can assist researchers in developing superior varieties and also aid farmers for cultivation purposes, sales, distribution, and selecting the right varieties for local growth and weather conditions. This research conducts the classification of mango types based on color from mango images using machine learning. The study compares three methods, namely Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), to determine the best method for classifying mango types based on their images. The dataset underwent preprocessing, where image sizes were standardized to 300 x 300 pixels, and color was changed to grayscale. The dataset was then divided into training and testing data with a ratio of 70:30. Subsequently, the dataset was processed using three methods, and their accuracy results were compared. The findings indicate that the Random Forest method yielded the highest accuracy compared to the other methods, with an accuracy rate of 96%. The accuracy of the SVM method was 95%, and the accuracy of the CNN method was 33%. From these results, it can be concluded that the Random Forest method is highly effective for classifying mango types based on their image compared to SVM and CNN methods.

Modeling of ordinal longitudinal accelerometer data

Improvements in accelerometer technology has led to new types of data on which more powerful predictive models can be built to assess physical activity. This paper implements an ordinal random forest model with recursive forecasting to take into account the ordinal, longitudinal nature of responses. The data comes from 28 adults performing activities of daily living in two visits, while wearing accelerometers on the ankle, hip, right and left wrist. The first visit provided training data and the second testing data so that an indepen- dent sample, cross-validation approach could be used. For this type of data, prior responses are not available at the testing stage or in practice. However, recursive forecasts can be made with prior predictions in place of lagging responses on models which were built to use lagging responses as explanatory variables. Models are fit to account for multiple time series, with different time series for each participant in the study. We found that ordinal random forest, when the time series is taken into account, produces better accuracy rates and better linearly weighted kappa values than both ordinary ordinal forest and random forest. On the testing set, the lowest error rates were produced by the ankle (28.0%), followed by the left wrist (28.7%), hip (28.9%) and then the right wrist (30.2%) using the best performing decision model for a four-activity level response. In addition, linearly weighted kappa values indicated substantial agreement. The approach of this work can be adapted to other types of longitudinal ordinal models for improvements in modeling techniques. Journal of Statistical Research 2022, Vol. 57, No. 1-2, pp.1-19

TRUENESS OF INTRAORAL SCANNERS IN DIFFERENT SCAN PATTERNS FOR FULL-ARCH DIGITAL IMPLANT IMPRESSIONS.

The optimal scan pattern for full-arch digital implant impressions remains to be determined. This study aimed to analyze the effects of different scan patterns on the trueness of intraoral scanners for full-arch digital implant impressions. A maxillary plaster model with four implant analogs was employed as the master model. Scan bodies were attached to the master model and scanned with a laboratory scanner to obtain reference data. Test scans were obtained using three different scan patterns with Cerec Primescan and Trios 3. Each test data was superimposed onto the reference data. The trueness was assessed by determining the 3D distance and angular deviations between the test and reference data. Significant differences in 3D distance deviation were detected among the scan patterns for both scanners. Significant differences in angle deviation were detected among the scan patterns for the Cerec Primescan, whereas it was not substantial for the Trios 3. Cerec Primescan exhibited superior trueness across all scan patterns compared to Trios 3. The zigzag pattern resulted in more accurate scans for the Cerec Primescan, while both the zigzag and occlusal-palatal-buccal patterns showed higher accuracy for the Trios 3.

Undrained cavity expansion-contraction analysis in CASM and its application for pressuremeter tests

Many geotechnical scenarios involve cavity unloading from a loaded state, particularly in pressuremeter tests, and the unloading data of pressuremeter tests has exceptional attraction as it is less disturbed by the insertion process. However, the analyses for continuous cavity loading and unloading (i.e., cavity initially experiences expansion and then contracts) in critical state soils are rarely studied. To this end, a novel semi-analytical solution based on the unified state parameter model for clay and sand (CASM) is proposed for the whole expansion-contraction of spherical and cylindrical cavities under undrained condition. The problem assumes that the cavity is unloaded after a monotonic loading stage, leading to plastic regions during both loading and unloading periods. The cavity response for the whole expansion-contraction process is investigated, with the total pressure and stress paths at the cavity wall presented and validated against numerical simulation. The developed solution is successfully implemented to interpret both loading and unloading data of pressuremeter tests. The undrained shear strength, in-situ effective horizontal stress and initial overconsolidation ratio are back analyzed by using a curve fitting method based on the proposed solution.

Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames

In this study, predicting the maximum drift ratio (MDR) for two types of structures: 8-story regular (R8) and vertically irregular (IR8) reinforced concrete (RC) frames are focused. To accomplish this, advanced tree-based machine learning (ML) methods, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Stochastic Gradient Boosting (SGB) are utilized. 2300 ground motion (GM) records, includes twenty-one input parameters and their nonlinear time history analyses results are considered as a dataset. The performance of the RF, XGBoost, and SGB models was examined considering the impact of the data pre-processing issue to test whether the use of data pre-processing approaches in model development could improve the prediction performance of the advanced ML models. Various data pre-processing techniques, including outlier detection using boxplot, missing data imputation using RF, data transformation using log transformation, and feature selection using Hill Climbing are employed as part of ML pipeline. The models' performance is evaluated using six different well-established statistical evaluation metrics. Subsequently, the results are systematically compared to identify the most effective prediction model. Our findings highlight the substantial impact of data pre-processing on the performance of ML models when predicting seismic responses, specifically the MDR of RC frames. Notably, RF consistently outperformed other ML models for both R8 and IR8 structures in the case of the simple ML pipeline strategy and data pre-processing approach. The RF model achieved R2 scores of R2 = 0.87 and R2 = 0.91 on the test data before implementing data pre-processing for R8 and IR8, respectively. After data pre-processing, R2 scores showed an improvement of 12% and 6.22% in the cases of R8 and IR8. In contrast, the performance scores of SGB consistently fell behind those of RF and XGBoost models.

Improve robustness of machine learning via efficient optimization and conformal prediction

AbstractThe advance of machine learning (ML) systems in real‐world scenarios usually expects safe deployment in high‐stake applications (e.g., medical diagnosis) for critical decision‐making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance‐regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end‐to‐end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample‐efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP‐based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk‐aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms.

Strain rate-temperature effects and constitutive models for Q690D QT steel

This paper investigates the mechanical properties of the high-strength structural steel Q690D QT (Quenched and Tempered) over a wide temperature range and a wide strain rate range. Firstly, a series of quasi-static and dynamic tests adopting the universal testing machine and Split Hopkinson Pressure Bar were carried out to investigate the mechanical properties at strain rates from 0.00025 s−1 to 5000 s−1 and temperatures from room temperature (20 °C) to 700 °C. Stress-strain curves and dynamic yield strength increase factors were extracted and investigated. Test results show that Q690D QT exhibits different strain rate effects at different temperatures, more significant at the temperatures of 600 and 700 °C, as well as the high-temperature softening effect. Dynamic strain aging effect is identified at temperatures between 300 − 600 °C for plastic strain rates of 800 s−1 and 2300 s−1, nevertheless, this effect is observed at a higher temperature range of 500–700 °C for higher plastic strain rates of 4000 s−1 and 6400 s−1. From the analysis, the adiabatic thermosoftening effect cannot be ignored when the plastic strain rates reach 4000 s−1. Finally, an improved form of the Johnson-Cook model is proposed for capturing the adiabatic temperature effects at different temperatures and validated by the test data. A parameter dynamic temperature factor is proposed to describe the comprehensive effect of strain rate, test temperature, and adiabatic thermosoftening.

APPLICATION OF RANDOM FOREST METHOD TO PREDICTION OF STUDENT CANDIDATES ACCEPTED IN THE SNMPTN PATHWAY (CASE STUDY AT UNIVERSITAS LAMBUNG MANGKURAT)

Academic planning is an essential aspect that needs to be carried out to plan the teaching and learning process in a campus, such as the admission of new students through the SNMPTN. At Lambung Mangkurat University, it is known that the partici- pants who took the SNMPTN during the 2021 admission period amounted to 7,703. It is recognized that selecting candidates is not easy due to many prospective students passing the selection but choosing to withdraw. Therefore, a system is needed to predict the graduation of prospective students through the SNMPTN. This research utilizes the Random Forest method to predict the graduation of prospective students through the SNMPTN. The data will be divided into 90% for training data and 10% for testing data, then using classification parameters with 300 n-estimators. The research yielded a precision value of 0.72, recall value of 0.46, and system accuracy of 89.3% For further research recommendations, other prediction methods can be explored to forecast the graduation rate of students through the SNMPTN, or a comparison of methods can be con- ducted to determine which method is more effective.

A new approach to predict pump transient phenomena in Molten salt reactor Experiment (MSRE) by missing data identification and regeneration

Molten Salt Reactor Experiment (MSRE) is being extensively used for validating the computational tools for Molten Salt Reactors (MSRs). The pump transient tests were performed during the operation of MSRE to obtain the reactivity response to flow perturbation. The transient flow rate during this set of tests is required as input to predict the reactivity response. Since the primary loop of MSRE was not equipped with a flow rate meter, the transient flow rate, which is the crucial factor for the reactivity change in MSRE, is missing. Achieving an accurate simulation of the reactivity response to the MSRE pump transient tests necessitates a precise estimation of the transient flow rate.This paper endeavors to reconstruct the missing flow rate with the aim of offering a valuable input for simulating reactivity responses in the pump transient test series. In order to obtain an accurate transient flow rate, we employed a centrifugal pump transient model based on the affinity laws and solved the model for the MSRE secondary pump to validate the underlying assumptions of the model. In addition, we constructed the homologous pump head curves based on the water test data for the same purpose. The affinity law approximation is proved to be adequate in predicting the MSRE pump startup transient with the root-mean-square error (RMSE) in the normalized flow rate to be 0.03. On the other hand, for the MSRE pump coastdown transient, the preliminary results indicate that neither the affinity law approximation nor the homologous head curve is sufficient to provide acceptable predictions. To overcome this noticeable modeling deficit, we propose an innovative approach based on a straightforward data mining technique to regenerate the MSRE pump coastdown homologous relations using the measured data of the secondary pump. These relations are transferred to the primary pump and used to simultaneously solve for the impeller speed and the flow rate of the pump in the primary pump coastdown. With the new approach, the estimated RMSE in the normalized pump speed for the primary pump coastdown is reduced 0.0052. This excellent agreement validates the accurate calculations for the flow rate during the pump coastdown transient. We also performed an uncertainty analysis to quantify the confidence interval of the predicted quantities, which further justifies the robustness of the proposed approach.

Pullout behaviour of strip plate anchors in clay overlying sand

Plate anchors are being increasingly employed to establish mooring systems for offshore floating facilities owing to their notable efficiency and cost-effectiveness. The majority of prior studies have concentrated on plate anchors, examining the vertical to horizontal orientations, particularly when embedded in single layer clay or sand. However, it is common to encounter multiple layers of soil with varying characteristics in practical engineering, leading to inaccurate estimation of pullout capacity when based solely on the properties of a single soil layer. This paper reports pullout behaviours of strip plate anchors in clay overlying sand by adopting two dimensional finite element analysis. To simulate the hardening and subsequent softening behaviour of medium to dense sand, a modified Mohr-Coulomb model is considered. The current Finite Element (FE) results are in good agreement when compared with both previous FE simulations and physical model test data. Then, the soil flow mechanisms (e.g. the accumulated plastic shear strain, the friction angle) surrounding the anchor during pullout out are revealed. Based on a series of FE results, a calculation framework considering the effect of thickness of clay layer is designed to predict the pullout behaviour of plate anchors in clay overlying sand.

Fatigue enhancement of welded thin-walled tubular joints made of lean duplex steel

In the present work, the fatigue strength and enhancement techniques of welded thin-walled (wall thickness of t = 2 mm) square hollow section (SHS) joints made of lean duplex stainless steel (grade EN 1.4162) are experimentally and numerically studied. Experimental fatigue tests were carried out on gas metal arc-welded tubular X-joints subjected to fully reversed (applied stress ratio of R = −1) constant amplitude in-plane bending moment. Geometrical improvement techniques were investigated by applying fully penetrating fillet welds and using structural reinforcements. The applications of high-frequency mechanical impact (HFMI) treatment were examined in the context of further enhancing the fatigue strength of the geometrically improved joints. In addition to the experimental work carried out in this study, the fatigue test data of welded thin-walled SHS and rectangular hollow section (RHS) joints were extracted from literature. Structural stress concentration factors were numerically and analytically obtained to validate the fatigue assessment model for welded thin-walled tubular joints using the structural hot-spot stress method. In addition, the effective notch stress (ENS) concept with the reference radius of rref = 0.05 mm was implemented. Compared to the joints in the as-welded state, fatigue strength improvements of 1.53–2.7 were found in the geometrically improved joints. On the other hand, the HFMI treatment did not lead to an additional increase in the fatigue strength capacity of the studied AW joints. The CIDECT guidelines for the structural hot-spot stress method were found conservative for thin-walled joints (t < 3 mm) and could be recommended for fatigue design.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Penerapan Data Mining untuk Menentukan Produk Terlaris pada PT Joynare Mitra Teknologi menggunakan Metode Naive Bayes

The research applies the Naive Bayes method to determine the best-selling products at PT Joynare Mitra Teknologi. The contribution of this research is to provide a reference for other companies interested in using similar techniques to analyze their product data. Testing was carried out by processing data using Microsoft Excel and RapidMiner, with training and testing data. The test results show that the Naive Bayes method can classify best-selling products with a high level of accuracy, reaching 87.00%. Apart from that, the resulting precision reached 72.41%, and recall was 80.77%. The best-selling products in hardware sales such as motherboards, CPUs, RAM, hard disks, SSD, and printers can be identified through data analysis. The conclusion from the research is that the Naive Bayes method is very appropriate to use to optimize sales strategies for hardware products based on customer preferences.

Investigation into soil ratcheting behind integral bridges using centrifuge modelling

U.K. integral bridges are commonly designed according to PD 6694-1, with guidance on backfill ratcheting based on experimental data that is limited by: testing at a small-scale low stress state, a lack of abutment foundation and soil below, and a limited range of soil-structure configurations. As integral bridge use increases, there is a need for further understanding of the strain ratcheting mechanism which can lead to smarter designs that minimise material usage and lifetime maintenance. This research presents the suitability of modern centrifuge techniques to simulate ratcheting of the soil behind an integral bridge abutment over a design life of thermal loading. A high accuracy actuation system was developed and used in centrifuge testing, with test data provided to demonstrate its capabilities. This is followed by a comparison of surface settlements, deck axial forces and abutment bending moment distributions recorded for various soil-structure combinations, highlighting the sensitivity of soil ratcheting. The paper concludes that centrifuge modelling can successfully simulate backfill strain ratcheting behind integral abutments over a range of soil-structure configurations. Furthermore, results suggest that global rotations and base sliding are significant to the overall response, clarifying the importance of modelling at an appropriate stress state with a foundation and the soil below.

Reliability analysis of rolling bearings considering failure mode correlations

AbstractAs an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing.

SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies.

University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching. We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples. In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread. We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.

A method for studying the influence of Mn oxyhydroxides on the trace element content of aquatic bryophytes

The main objective of this study was to develop and test a method of separating externally deposited Mn oxyhydroxides and co-precipitated elements from samples of aquatic moss (the moss Fontinalis antipyretica). The method, which uses 0.1 M hydroxylamine to dissolve the oxyhydroxides, was tested with samples collected in rivers with slightly acidic, well‑oxygenated waters, where high rates of Mn precipitation occur. The method was effective (it extracted up to 84 % of the Mn) and selective (Fe oxyhydroxides were not extracted). The elements Ba, Cd, Zn and Ni were associated with the Mn oxyhydroxides, while Al, As, Cr, Cu, Fe, Hg and Pb were not. Deposition of Mn therefore increased the concentration of some elements in the moss samples. However, as Mn precipitation depends on Eh and pH, which are independent of the concentrations of the elements in water, the relationship between water and moss element concentrations is not clear (i.e. the data are noisy). This is a problem in biomonitoring studies, which assume a close relationship between element concentrations in moss and water. The value of the proposed extraction method is that it can be used to correct the effect of Mn deposition. We present an example of this correction applied to the Cd concentrations in the test data. We found that the noise introduced by the Mn, including age-related effects (observed by comparing concentrations in 0–2.5 and 2.2–5.0 cm sections from the shoot apex), can be reduced. Additionally, the correction revealed recent increases in Cd concentrations in one site that were not observed in the uncorrected data. Another finding of interest was the low content of total Mn and different extractability (of most elements) observed in moss samples collected in alkaline waters. Finally, we discuss how future studies designed for different environmental scenarios can benefit from application of the proposed method.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario

This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices.