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Abstract Polyploid speciation has played an important role in evolutionary history across the tree of life, yet there remain large gaps in our understanding of how polyploid species form and persist. Although systematic studies have been conducted in numerous polyploid complexes, recent advances in sequencing technology have demonstrated that conclusions from data-limited studies may be spurious and misleading. The North American gray treefrog complex, consisting of the diploid Hyla chrysoscelis and the tetraploid H. versicolor, has long been used as a model system in a variety of biological fields, yet all taxonomic studies to date were conducted with only a few loci from nuclear and mitochondrial genomes. Here, we utilized anchored hybrid enrichment and high-throughput sequencing to capture hundreds of loci along with whole mitochondrial genomes to investigate the evolutionary history of this complex. We used several phylogenetic and population genetic methods, including coalescent simulations and testing of polyploid speciation models with approximate Bayesian computation, to determine that H. versicolor was most likely formed via autopolyploidization from a now extinct lineage of H. chrysoscelis. We also uncovered evidence of significant hybridization between diploids and tetraploids where they co-occur, and show that historical hybridization between these groups led to the re-formation of distinct polyploid lineages following the initial whole-genome duplication event. Our study indicates that a wide variety of methods and explicit model testing of polyploid histories can greatly facilitate efforts to uncover the evolutionary history of polyploid complexes.

The mechanism of lagging water inrush in underground tunnel constructions due to the proximity of a karst cavern with confined water is investigated via large-scale physical three-dimensional (3D) model testing and 3D numerical simulations. A new method is proposed for the preparation of modeled karst caverns filled with confined water. The physical 3D model testing is divided into two stages: tunnel excavation and hydraulic pressure loading. Multivariate information is obtained at the two stages using multiple measurement techniques. The results indicate that the displacement, hydraulic pressure, and the developmental trend of the damage zone in the tunnel excavation process are related. It is evident from the physical 3D model testing results that the process of water inrush can be divided into three stages, which include the initiation of group cracks, the formation of a water inrush channel, and the complete collapse of the water-resistant slab. The 3D model testing in conjunction with the 3D numerical simulations reveal that the disturbance due to excavation has an obvious impact on water inrush channel formation. However, an increasing hydraulic pressure in the karst cavern has a greater impact on the collapse of the water-resistant slab. These test results can provide support and guidance for tunnel construction under conditions that are susceptible to water inrush events.

ABSTRACT Identifying and characterizing stressor interactions is central to multiple stressor research. Such interactions refer to stronger (synergism) or weaker (antagonism) joint effects of co‐occurring stressors on biological entities, when compared to the predictions of a theoretical null model. Various null models have been developed, and the selection of the most appropriate null model for a specific research question is ideally based on assumptions on co‐tolerance patterns in communities and mechanisms of stressor effects. Statistical models are commonly used to evaluate the statistical significance of interaction terms. However, they introduce constraints by imposing a specific null hypothesis on stressor combinations that cannot be flexibly changed. This can introduce a mismatch between the null model that the analyst wants to test and the one imposed by the statistical model. Here, we show under which conditions the statistical null hypothesis for interaction terms misaligns with a multiple‐stressor null model and propose to resolve such misalignments using post‐estimation inference. Null‐model specific interaction estimates can be calculated from adjusted predictions of a fitted regression model, and associated standard errors are derived using the delta method, posterior simulations, or bootstrapping. We illustrate the suggested approach with three case studies and validate statistical conclusions through data simulations. Post‐estimation inference has the potential to advance hypothesis‐driven research on stressor interactions by flexibly testing any a priori defined null model independent from regression model structure. Multiple‐stressor research is relevant across ecological sub‐disciplines (terrestrial, marine, and freshwater), aiming to predict how anthropogenic stressors combine. Different null models have been developed for this purpose. Here, we introduce an analytical framework to statistically evaluate any a priori chosen null model to facilitate hypothesis‐driven research on multiple stressors.

Testing safety-critical systems is crucial since a failure or malfunction may result in death or serious injuries to people, equipment, or environment. An important challenge in testing is the derivation of test cases that can identify the potential faults. Model-based testing adopts models of a system under test and/or its environment to derive test artifacts. This paper aims to provide a systematic mapping study to identify, analyze, and describe the state-of-the-art advances in model-based testing for software safety. The systematic mapping study is conducted as a multi-phase study selection process using the published literature in major software engineering journals and conference proceedings. We reviewed 751 papers and 36 of them have been selected as primary studies to answer our research questions. Based on the analysis of the data extraction process, we discuss the primary trends and approaches and present the identified obstacles. This study shows that model-based testing can provide important benefits for software safety testing. Several solution directions have been identified, but further research is critical for reliable model-based testing approach for safety.

In this paper, for the engineering problem of tunnel deformation damage caused by a tunnel–landslide orthogonal system landslide in northwest China, based on similar theory, a variety of materials and their ratios were explored to make similar models according to the physical and mechanical parameters of the site engineering rock, indoor physical model tests were constructed, and monitoring techniques, such as speckle displacement strain measurement, strain acquisition, and pressure sensors, were used to test the macroscopic deformation characteristics; through the detailed analysis and law summary of the test macroscopic deformation characteristics, displacement, anchor cable axial force, and strain data, it was finally found that under the orthogonal system of tunnel and sliding surface, the damage form of tunnel lining is mainly compression damage and shear damage, and the cracks are mainly tension cracks, and the effect of tunnel primary lining on the surrounding rock support is extremely limited under the action of huge sliding force; combined with the analysis of the model deformation characteristics and the axial force curve of the anchor cable during loading, it was found that the axial force change curve is consistent with the NPR Newton force curve can be used to evaluate slope stability and predict the occurrence of landslide disasters.HighlightsThis paper focuses on the tunnel and surrounding rock damage evolution characteristics under tunnel–landslide orthogonal and tunnel crossing sliding surface conditions.The evolution of displacement field, NPR anchor cable axial force, and strain field were revealed using indoor large similar physical model test.The mechanism of tunnel–landslide interaction within the slope and the deformation and damage characteristics of the tunnel surrounding rock were analyzed to reveal the slope deformation law under different loads.The stress change law of constant-resistance large deformation anchor cable (NPR anchor cable) is explored, and a landslide warning mechanism based on constant-resistance large deformation anchor cable is established.

In order to investigate the mechanism of ground settlement induced by shield tunnels better, this study proposes a novel simplified physical model testing method. In this physical model, double layer tubes with different materials are used to model the tunnel boring machine (TBM) and tunnel, respectively. When the outer tube in the experimental box is removed, the gap between the two different tubes can be utilized to reflect the ground settlement caused by TMB construction. Meanwhile, 3D image monitoring technology is introduced to collect ground settlement data for research on the mechanism of ground settlement induced by TBM construction. In order to validate the proposed testing method, firstly, the pilot experiment is performed; then, the obtained settlement curve obeys the Gaussian distribution, and the obtained settlement process is similar to that of the practical situation. Furthermore, based on the proposed testing method, an orthogonal experiment is designed to investigate the influences of the ground loss ratio, burial depth, and stratum condition on the ground settlement during the construction process. The results indicate that the ground loss ratio caused by the gap during construction excavation has a more significant impact than the tunnel burial depth and ground conditions. The findings in this study provide a quantitative guide for settlement monitoring during TBM construction, demonstrating that the ground loss ratio has the most significant impact on settlement (up to 28.7% deviation), while the effects of burial depth and stratum conditions are relatively minor (4.4% and 4.2% deviation, respectively). This method offers a practical and efficient approach for predicting and controlling ground settlement in TBM construction, which is of great importance in its practical application.

PurposeTesting business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.Design/methodology/approachThe methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.FindingsResults of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.Research limitations/implicationsThe databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.Originality/valueThree main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

It is important that a Cyber-Physical System (CPS) with uncertainty in its behavior caused by its unpredictable operating environment, to ensure its reliable operation. One method to ensure that the CPS will handle such uncertainty during its operation is by testing the CPS with model-based testing (MBT) techniques. However, existing MBT techniques do not explicitly capture uncertainty in test ready models, i.e., capturing the uncertain expected behavior of a CPS in the presence of environment uncertainty. To fill this gap, we present an Uncertainty-Wise test-modeling framework, named as UncerTum , to create test ready models to support MBT of CPSs facing uncertainty. UncerTum relies on the definition of a UML profile [the UML Uncertainty Profile ( UUP )] and a set of UML Model Libraries extending the UML profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE). UncerTum also benefits from the UML Testing Profile V.2 to support standard-based MBT. UncerTum was evaluated with two industrial CPS case studies, one real-world case study, and one open-source CPS case study from the following four perspectives: (1) Completeness and Coverage of the profiles and Model Libraries in terms of concepts defined in their underlying uncertainty conceptual model for CPSs, i.e., U-Model and MARTE, (2) Effort required to model uncertainty with UncerTum , and (3) Correctness of the developed test ready models, which was assessed via model execution. Based on the evaluation, we can conclude that we were successful in modeling all the uncertainties identified in the four case studies, which gives us an indication that UncerTum is sufficiently complete. In terms of modeling effort, we concluded that on average UncerTum requires 18.5% more time to apply stereotypes from UUP on test ready models.

Software testing plays a crucial role in enhancing software quality. A significant portion of the time and cost in software development is dedicated to testing. Automation, particularly in generating test cases, can greatly reduce the cost. Model-based testing aims at generating automatically test cases from models. Several model based approaches use model checking tools to automate test case generation. However, this technique faces challenges such as state space explosion and duplication of test cases. This paper introduces a novel solution based on data mining algorithms for systems specified using graph transformation systems. To overcome the aforementioned challenges, the proposed method wisely explores only a portion of the state space based on test objectives. The proposed method is implemented using the GROOVE tool set for model-checking graph transformation systems specifications. Empirical results on widely used case studies in service-oriented architecture as well as a comparison with related state-of-the-art techniques demonstrate the efficiency and superiority of the proposed approach in terms of coverage and test suite size.

The effectiveness of model-based testing (MBT) is mainly due to its potential for automation. If the model is formal and machine-readable, test cases can be derived automatically. One of the most used formal modeling techniques is the interpretation of a system as an extended finite state machine (EFSM). However, formal models are not a common practice in the industry. The Unified Modeling Language (UML) has become the de facto standard for software modeling. Nevertheless, due to the lack of formal semantics, its diagrams can be given ambiguous interpretations and are not suitable for testing automation. This article introduces a systematic procedure for the generation of tests from UML models that uses concepts of model-driven engineering (MDE) for formalizing UML sequence diagrams into extended finite state machines and providing a precise semantics for them. It also applies ModelJUnit and JUnit libraries for an automatic generation of test cases. A case study was conducted in a real software towards the evaluation of its applicability.