National Institute of Statistics and Geography

A recently developed hot-melt adhesive (HMA) based on cyclic olefin chemistry, supplied as a transparent, non-tacky thermoplastic film with a distinctive polymer microstructure, was evaluated in this study. The aim was to compare the performance of the HMA with commonly used epoxy-based structural adhesives in adhesively bonded composite joints. Composite single-lap joints (SLJs) were used as test specimens, effectively representing adhesion quality and overall joint performance. Two epoxy-based adhesives (paste and film forms) served as benchmarks, while the HMA was tested to highlight its advantages and limitations. All joints were evaluated under four-point bending at various loading rates. Under static loading, the HMA showed comparable performance to the epoxy adhesives, although the failure mode shifted from delamination in epoxy joints to adhesive failure in HMA joints. Under high-rate four-point bending, the HMA demonstrated superior strength and toughness, reflecting its pronounced rate-dependent behavior. Fatigue tests under four-point bending were also conducted for all adhesive systems, and their performance was evaluated comprehensively. Furthermore, HMA SLJs initially tested under quasi-static and fatigue loading were re-bonded and retested under quasi-static loading to assess performance after a second curing cycle, demonstrating the reusability advantage of the HMA. Overall, the study highlights that the HMA provides competitive static performance, enhanced high-rate strength, and potential for re-bonding, making it a promising alternative to conventional epoxy adhesives in composite joint applications. This work emphasizes the benefits of thermoplastic HMA in terms of mechanical performance, fatigue resistance, and reusability, supporting its adoption in advanced structural bonding of composites.

Digital twins (DTs) have emerged as a transformative technology for modeling and simulation in various industries, including defense. This paper provides a comprehensive review of DT applications in defense modeling and simulation, focusing on how DTs can enhance simulation fidelity, interoperability, and decision support within defense systems. We consolidate existing research into a unified framework that links DT concepts, simulation-driven applications, and real-world deployments in defense scenarios. We discuss the role of the DT in applications like planning, training, execution, monitoring, and debriefing. We introduce a standardized DT characterization framework suitable for defense applications that aligns with industrial modeling and simulation standards and present a taxonomy of defense-specific use cases, highlighting recurring requirements. In addition, practical evidence is provided from a targeted questionnaire distributed to defense stakeholders and the ministries of defense (MoDs), revealing current challenges in DT integration and deployment. Finally, we conclude by identifying key gaps in DTs applications for defense modeling and simulation, including interoperability, security, and system integration, and we outline future research directions and development opportunities. This review aims to inform defense modeling and simulation practitioners and researchers, guiding future work on DT design, implementation, and deployment across defense applications.

Background Patients with rectal cancer who have a complete clinical response (cCR) to neoadjuvant chemo/radiotherapy (nCRT) may opt for organ preservation and watch and wait (W&W). This consists of an intense surveillance program including serial endoscopies, pelvic magnetic resonance imaging (MRI), carcinoembryonic antigen (CEA), and computed tomography (CT) scans to detect regrowth at an early stage. However, residual lesions or regrowths can be challenging to identify endoscopically, due to mucosal changes such as friability and neovascularization. We developed a novel deep learning model to assist in the detection of residual or regrown rectal cancer lesions during proctosigmoidoscopy.Methods We trained a convolutional neural network (Wide ResNet-101-2) on a dataset of 1795 annotated frames from proctosigmoidoscopy exams of 97 patients treated at a tertiary referral center. Residual or regrowth was defined by histopathological confirmation. The dataset was split into training and testing cohorts using a 90/10% patient-level split.Results Out of 97 patients, 24 (363 frames) had confirmed residual disease or regrowths, while 73 (1432 frames) presented normal rectal mucosa. The model achieved an overall accuracy of 92.8%, with a sensitivity of 80.0%, specificity of 97.3%, positive predictive value (PPV) of 90.9%, negative predictive value (NPV) of 94.0%, and an area under the receiver operating characteristic curve (AUROC) of 0.886.Conclusions To the best of our knowledge, this is the first deep learning model specifically developed for the detection of residual disease or regrowth following cCR in W&W patients during endoscopic examination. This tool has the potential to aid lesion detection, guide clinical decision-making, and increase opportunities for salvage, curative treatment strategies.

Air bending is a critical operation in the metalworking industry, where dimensional accuracy and process efficiency are essential to ensure product quality and economic viability. This work proposes an AI-driven design and optimization strategy which couples artificial intelligence, specifically artificial neural networks, with a quasi-random search algorithm for the metamodeling and optimization of the air bending process. An extensive simulation database was generated by varying geometrical, material, and process parameters, and neural-network-based metamodels were trained to predict the maximum punch force, maximum thickness reduction, and final bending angle, achieving high predictive accuracy with R² values exceeding 0.96. The metamodel was subsequently used to optimize process configurations by simultaneously minimizing the maximum punch force and the maximum thickness reduction while ensuring the target bending angle, leading on average to reductions of 46.7% in maximum force and 31.5% in thickness reduction compared to non-optimized cases. The results demonstrate that artificial intelligence provides an efficient and effective tool for the design and optimization of the bending process, significantly accelerating parameter selection while improving process quality and reducing manufacturing costs.

A numerical methodology is proposed to determine the 3D failure criteria of directional composite joints, accounting for both in-plane (in-/off-axis) and out-of-plane behavior. The approach involves simulating standard composite joint characterization tests using a high-fidelity, three-dimensional finite element damage modeling strategy. A simplified modeling strategy to simulate multi-bolt joints is then proposed: the joints are simulated using a combination of linear-elastic multilayer composite shell elements and connector elements, which fail once their internal forces intercept the previously determined 3D failure envelope. The methodology is applied to five multi-bolt single lap shear geometries and the results are compared with those obtained using high-fidelity simulations. Results indicate that the methodology developed to numerically determine the bolt failure envelope, combined with the use of connector elements as fastener representatives, is appropriate to accurately simulate the joint stiffness and load ratios in the first failed bolt, and predict first-drop laminate-level failure in composite bolted connections while providing a substantial reduction in computation expenses, establishing their potential for future use in large-scale models.