Due to their widespread use in industry, several techniques have been proposed in the literature to fuzz REST APIs. Existing fuzzers for REST APIs have been focusing on detecting crashes (e.g., 500 HTTP server error status code). However, security vulnerabilities can have major drastic consequences on existing cloud infrastructures.In this paper, we propose a series of novel automated oracles aimed at detecting violations of access policies in REST APIs, as well as executing traditional attacks such as SQL Injection and XSS. These novel automated oracles can be integrated into existing fuzzers, in which, once the fuzzing session is completed, a “security testing” phase is executed to verify these oracles. When a security fault is detected, as output our technique is able to generate executable test cases in different formats, like Java, Kotlin, Python and JavaScript test suites.Our novel techniques are integrated as an extension of EvoMaster, a state-of-the-art open-source fuzzer for REST APIs. Experiments are carried out on 9 artificial examples, 8 vulnerable-by-design REST APIs with black-box testing, and 36 REST APIs from the WFD corpus with white-box testing, for a total of 52 distinct APIs. Results show that our novel oracles and their automated integration in a fuzzing process can lead to detecting security issues in several of these APIs.
Short carbon fiber-reinforced polymer (SCFRP) composites exploit the intrinsic conductivity of the carbon fiber network for self-sensing, yet no predictive model couples their anisotropic, rate-dependent fracture to piezoresistive damage identification. This work presents a finite deformation multiphysics phase-field framework coupling a viscoelastic-viscoplastic constitutive model, an anisotropic crack resistance formulation, and a piezoresistive conductivity model. The three sub-problems are unified through the second-order fiber orientation tensor, which simultaneously defines fiber family directions, crack resistance anisotropy, and principal conduction paths of the carbon fiber network. A damage-coupled conductivity tensor captures both strain-driven geometric-kinematic resistance changes and irreversible network severance driven by the phase-field variable. The framework is coupled to an eight-electrode electrical impedance tomography configuration, and the normalized inter-electrode conductance ratios serve as inputs to a feedforward artificial neural network that infers normalized crack length and mechanical compliance without mechanical sensing. The network achieves R2 = 0.99 on held-out configurations, confirming generalization across the microstructure space. The framework establishes a physics-based, computationally efficient route for real-time structural health monitoring and inverse damage assessment in SCFRP composites.
Organizations that innovate with emerging digital technologies increasingly manage portfolios in which activities differ not only in risk and maturity, but in the kind of management they require. Research on innovation portfolio management has often approached the portfolio as a managerial object whose practices can be optimized, offering limited guidance for managing heterogeneous portfolios in contexts shaped by emerging digital technologies. Drawing on interviews with 26 informants in seven incumbent Scandinavian organizations, this study explores how organizations differentiate and configure their innovation portfolio management practices. Through an abductive analysis, we develop a taxonomy comprising three innovation portfolio management practice configurations: the product-developing, platform-mediating, and problem-solving configuration. The taxonomy helps explain portfolio heterogeneity by showing how qualitatively different innovation activities correspond to different configurations of innovation portfolio management practices. By reconceptualizing portfolio heterogeneity as the coexistence of qualitatively different practice configurations, the study extends innovation portfolio management theory and offers a practical framework for managing innovation portfolios in contexts shaped by emerging digital technologies.
Increasing residential segregation of affluent families is widespread internationally, raising concerns about "opportunity hoarding" and the perpetuation of social inequalities. Yet, we do not know whether this phenomenon is driven by families selecting neighborhoods characterized by their profiles of earnings or wealth. We model neighborhood selection during the 1993-2017 period by native Norwegian parents of children born 1992-2003, using a conditional logit analysis. We employ population register data from the Oslo metropolitan area that allows the calculation of neighborhood wealth profiles. We find that neighborhood residents' real capital and financial capital distributions are empirically distinct from their earnings distribution and strongly predict residential selections, but with considerable heterogeneity depending on wealth of the moving family and child's gender. Economic homophily dominates the neighborhood selections of wealthy families; others appear driven by avoiding status discrepancy. Neighborhood economic profiles are stronger predictors when moving with girls.
The use of fiber-reinforced polymers (FRPs) for strengthening existing reinforced concrete (RC) structures has significantly improved structural rehabilitation processes, providing efficient, durable, and non-invasive solutions. This study presents an advanced deep learning-based predictive model specifically developed to estimate the shear strength of concrete beams strengthened externally with carbon fiber-reinforced polymer (CFRP) composites. Using a comprehensive dataset of 216 experimentally tested CFRP-wrapped concrete beams drawn from existing research, a deep neural network model was rigorously optimized with the Optuna hyperparameter tuning framework and k-fold cross-validation to ensure robustness and generalizability. Model validation involved a thorough comparative analysis against established international design codes (ACI PRC-440.2-17, CSA-S806-12, JSCE) and a parametric study examining the sensitivity of shear strength predictions to key influencing factors, including concrete compressive strength, beam depth, and CFRP wrap thickness. Results demonstrated superior prediction accuracy and reliability of the deep learning approach compared to traditional empirical design models. Consequently, this research significantly enhances the precision of shear strength predictions for CFRP-strengthened concrete beams, supporting the development of more efficient and accurate structural rehabilitation and design guidelines.
