In the past decade, mechanical metamaterials have garnered increasing attention owing to its novel design principles which combine the concept of hierarchical architecture with material size effects at micro/nanoscale. This strategy is demonstrated to exhibit superior mechanical performance that allows us to colonize unexplored regions in the material property space, including ultrahigh strength‐to‐density ratios, extraordinary resilience, and energy absorption capabilities with brittle constituents. In the recent years, metamaterials with unprecedented mechanical behaviors such as negative Poisson's ratio, twisting under uniaxial forces, and negative thermal expansion are also realized. This paves a new pathway for a wide variety of multifunctional applications, for example, in energy storage, biomedical, acoustics, photonics, and thermal management. Herein, the fundamental scientific theories behind this class of novel metamaterials, along with their fabrication techniques and potential engineering applications beyond mechanics are reviewed. Explored examples include the recent progresses for both mechanical and functional applications. Finally, the current challenges and future developments in this emerging field is discussed as well.
Valentin J. M. Manès, HyungSeok Han, Choongwoo Han
et al.
Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.
Chimeric antigen receptor (CAR) T-cell therapy has achieved significant success in hematologic malignancies, but its efficacy in solid tumors remains limited, primarily due to the immunosuppressive tumor microenvironment (TME) that hinders CAR-T cell trafficking and function. NKG2D CAR-T cells, which target stress-induced NKG2D ligands (NKG2DLs) broadly expressed on tumor cells, have shown promising potential in overcoming the immunosuppressive barriers of the solid TME. This review highlights recent advances in NKG2D CAR-T cell strategies for solid tumors, including innovations in CAR architecture, signaling pathway engineering, combination immunotherapy, and the development of armored CAR constructs. We further discuss the therapeutic potential, current challenges, and future directions of these approaches to inform the design of more effective and durable CAR-T cell therapies for solid tumors.
Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.
Cultured meat is emerging as a sustainable alternative to conventional animal agriculture, with scaffolds playing a central role in supporting cellular attachment, growth, and tissue maturation. This review focuses on the development of gel-based hybrid biomaterials that meet the dual requirements of biocompatibility and food safety. We explore recent advances in the use of naturally derived gel-forming polymers such as gelatin, chitosan, cellulose, alginate, and plant-based proteins as the structural backbone for edible scaffolds. Particular attention is given to the integration of food-grade functional additives into hydrogel-based scaffolds. These include nanocellulose, dietary fibers, modified starches, polyphenols, and enzymatic crosslinkers such as transglutaminase, which enhance mechanical stability, rheological properties, and cell-guidance capabilities. Rather than focusing on fabrication methods or individual case studies, this review emphasizes the material-centric design strategies for building scalable, printable, and digestible gel scaffolds suitable for cultured meat production. By systemically evaluating the role of each component in structural reinforcement and biological interaction, this work provides a comprehensive frame work for designing next-generation edible scaffold systems. Nonetheless, the field continues to face challenges, including structural optimization, regulatory validation, and scale-up, which are critical for future implementation. Ultimately, hybrid gel-based scaffolds are positioned as a foundational technology for advancing the functionality, manufacturability, and consumer readiness of cultured meat products, distinguishing this work from previous reviews. Unlike previous reviews that have focused primarily on fabrication techniques or tissue engineering applications, this review provides a uniquely food-centric perspective by systematically evaluating the compositional design of hybrid hydrogel-based scaffolds with edibility, scalability, and consumer acceptance in mind. Through a comparative analysis of food-safe additives and naturally derived biopolymers, this review establishes a framework that bridges biomaterials science and food engineering to advance the practical realization of cultured meat products.
Underwater photogrammetry presents unique challenges, including light attenuation, refraction, and turbidity, that affect the accuracy and quality of 3D reconstructions. This study investigates the performance of novel neural rendering techniques, Neural Radiance Fields (NeRF), SeaThru-NeRF, and 3D Gaussian Splatting (3DGS), in comparison to conventional Structure-from-Motion (SfM) workflows. Using a dataset acquired during the SIFET benchmark campaign on a submerged Roman archaeological site, we processed image data via Nerfacto, SeaThru, and Jawset Postshot (3DGS) and compared outputs against a reference model produced in Agisoft Metashape. Evaluation criteria included processing time, geometric accuracy (via M3C2 analysis), point cloud density and roughness, and point cloud completeness. Results show that radiance fields-based methods significantly reduce processing time while providing competitive visual results. SeaThru-NeRF demonstrated the highest geometric accuracy, benefiting from underwater-specific corrections, while 3DGS offered photorealistic rendering. These findings highlight the potential of neural methods for underwater cultural heritage documentation, though further improvements are needed in data fidelity and robustness under challenging underwater conditions.
Randika K. Makumbura, Jagath Manatunge, Upaka Rathnayake
Accurate streamflow prediction is essential for effective water resource planning and management. Although physics-based hydrological models such as SWAT and WEAP are commonly used for streamflow simulation, they often encounter limitations due to structural complexity, rigid conceptual assumptions, and sensitivity to parameter calibration. In this study, LSTM models are utilized as a data-driven alternative for monthly streamflow prediction in the Kelani River Basin (KRB), Sri Lanka. Three variations of the LSTM architecture, Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM (Bi-LSTM), are assessed and compared against conventional physics-based models, including SWAT and WEAP. Results illustrated that LSTM models consistently outperform SWAT and WEAP during both calibration and validation phases. During calibration, LSTM models achieved high accuracy with NSE values nearing 0.95, R² between 0.95 and 0.96, PBIAS ranging from 2.03 to 4.56, and RSR between 0.21 and 0.23. Physics-based models exhibited lower performance (NSE: 0.71–0.74; R²: 0.74–0.83; PBIAS:23.67 to 4.7; RSR: 0.51–0.54). Validation results confirmed this trend, with LSTM models maintaining strong performance (NSE: 0.82–0.84; R²: 0.84–0.88; PBIAS:11.5 to –15.60; RSR: 0.40–0.43), while physics-based models displayed weaker predictive capability (NSE: 0.50–0.61; R²: 0.66–0.81; PBIAS:16.33 to –42.14; RSR: 0.62–0.71). Among the LSTM variations, Bi-LSTM demonstrated the best performance during calibration, while Stacked LSTM proved to be more effective during validation. The study underscores the robustness and reliability of LSTM models for monthly streamflow prediction, presenting a valuable approach for long-term water resource management in the KRB.
Many studies have used various time-frequency feature extraction methods to convert ship-radiated noise into three-dimensional (3D) data suitable for computer vision (CV) models, which have shown good results in public datasets. However, traditional feature engineering (FE) has been enhanced to interface matching–feature engineering (IM-FE). This approach requires considerable effort in feature design, larger sample duration, or a higher upper limit of frequency. In this context, this paper proposes a one-dimensional network design for underwater acoustic target recognition (UATR-ND1D), only combined with fast Fourier transform (FFT), which can effectively alleviate the problem of IM-FE. This method is abbreviated as FFT-UATR-ND1D. FFT-UATR-ND1D was applied to the design of a one-dimensional network, named ResNet1D. Experiments were conducted on two mainstream datasets, using ResNet1D in 4320 and 360 tests, respectively. The lightweight model ResNet1D_S, with only 0.17 M parameters and 3.4 M floating point operations (FLOPs), achieved average accuracies were 97.2% and 95.20%. The larger model, ResNet1D_B, with 2.1 M parameters and 5.0 M FLOPs, both reached optimal accuracies, 98.81% and 98.42%, respectively. Compared to existing methods, those with similar parameter sizes performed 3–5% worse than the methods proposed in this paper. Additionally, methods achieving similar recognition rates require more parameters of 1 to 2 orders of magnitude and FLOPs.
Compared with traditional educational assessment methods, cognitive diagnosis theory pays more attention to the interpretability and guidance of assessment results, with the aim of better-guiding teaching and conducting personalized learning. This study aims to determine the learning progression, cognitive diagnosis, hierarchical relationship of cognitive attributes, and cognitive diagnosis model for undergraduate students majoring in electrical communication engineering with different learning levels, taking the learning of “electromagnetic wave polarization” as an example. Guided by the Q-matrix theory and the cognitive development theory, the cognitive attributes of the concept of “electromagnetic wave polarization” were defined: A1 (Definition and Classification of Electromagnetic Wave polarization), A2 (linearly polarized waves), A3 (circularly polarized waves), A4 (Elliptically polarized waves), and A5 (Decomposition and Application of polarized waves) have determined the hierarchical relationship among each cognitive attribute. Based on the attribute hierarchical relationship, combined with the ChemQuery evaluation system, an advanced learning model for the concept of “electromagnetic wave polarization” has been preset. Based on the cognitive diagnosis theory, the Q matrix of the concept of “electromagnetic wave polarization” was established. The test Q matrix used for the design of project test questions was calculated, and the reliability of the test attributes was tested. Finally, by testing the examination scores of the knowledge point of “electromagnetic wave polarization” of two groups of different students, it was further demonstrated that the cognitive diagnosis model designed in this paper is reasonable and can be used to improve the achievement of the teaching objectives of the course.
The living quarters (LQ) on jack-up rigs play a critical role in ensuring crew safety and operational functionality under extreme offshore conditions. This study presents a comprehensive structural engineering procedure for the design and analysis of LQ structures, addressing the absence of specific industry standards. The methodology integrates global and local load effects from critical equipment, such as helidecks and lifeboat stations, under harsh environmental conditions during wet towing. A multi-level analysis approach, including finite element analysis (FEA), nonlinear evaluations, and fatigue assessments, was employed to verify structural resilience. The study successfully validates the LQ structures against ultimate limit state (ULS), serviceability limit state (SLS), and accidental limit state (ALS) criteria. The maximum plastic strain observed under green water pressure was 3.8 %, well below the allowable threshold of 15 %, demonstrating adequate safety margins. Fatigue analysis confirmed resistance to vortex-induced vibrations (VIV), ensuring the durability of tubular members. Optimization efforts reduced LQ structural weight by 20 %, enhancing efficiency without compromising safety. The proposed procedure bridges the gap in industry standards, providing a robust framework for designing safer and more reliable LQ structures. This study advances offshore engineering practices by addressing complex loading scenarios and operational challenges, thereby supporting the development of resilient jack-up rigs capable of enduring extreme marine conditions.