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Synthetic 3D printed tibial plateau with gradient material properties for biomechanical accuracy
(2025) Coato, Damiano; Dolino, Gianmarco; Berardo, Alice; Belluzzi, Elisa; Pozzuoli, Assunta; Ruggieri, Pietro; Carniel, Emanuele Luigi; Gargiulo, Paolo; Department of Engineering
Introduction: This study presents the design and fabrication of a synthetic 3D printed tibial plateau, complete with tibial cartilages, developed to replicate the mechanical behavior of its natural counterpart. Methods: Patient-specific anatomical data were used to design the model, which was fabricated using advanced PolyJet™ multi-material printing. Gradient material properties were integrated within the construct to reproduce the stiffness variations observed in native cartilage. Three different material mixes were developed and tested under indentation loading, and the optimal configuration (Mix 3) was selected based on its mechanical fidelity to biological tissue. Results: Mix 3 successfully reproduced the regional stiffness variations of native tibial cartilage. The instantaneous modulus (IM) of the synthetic cartilage closely matched that of the biological sample, with values of 3.19 (Formula presented.) 1.95 (Formula presented.) vs. 3.31 (Formula presented.) 2.33 (Formula presented.) in the lateral compartment and 3.71 (Formula presented.) 1.38 (Formula presented.) vs. 3.72 (Formula presented.) 2.56 (Formula presented.) in the medial compartment. Statistical analysis confirmed that most regional comparisons showed no significant differences (p (Formula presented.) 0.05), supporting the strong mechanical agreement between synthetic and native cartilage. Conclusion: This study demonstrates the potential of Digital Anatomy materials produced with PolyJet™ technology as a viable method for 3D printing anatomically and mechanically accurate models of the human tibial plateau. Overall, this approach provides a reproducible and ethically sustainable alternative to biological specimens, with implications for preclinical testing, implant design optimization, and the advancement of high-fidelity surgical training models.
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Novelle approach to simulating spinal cord stimulation during tSCS using CT images and FEM
(2024-12-01) Árnason, Jón Andri; Gumundsdóttir-Korchai, Ragnhildur; Afework Tesfahunegn, Yonatan; Helgason, Pórur; Department of Engineering
Transcutaneous spinal cord stimulation (tSCS) offers non-invasive relief for chronic pain and improves motor function in spinal cord injured (SCI) patients. However, its mechanisms are not currently fully understood, and patientspecific factors, such as Body mass index (BMI) and age, complicate treatments. This paper aims to understand tSCS better by developing a novel Finite Element Model (FEM) of the human body using CT scans. Three subjects (sex, male, female, male. Age: 26, 27, 64. BMI: 38.9, 24.1, 28.4) underwent a CT scan, performed on a Cannon Aquilion Prime (Slice thickness [mm]: 0.8, Voxel size [mm3]: 0.564, 0.328, 0.527), which imaged the trunk of the body, from top of the abdomen to the bottom of the pelvis. The images were then used in Materialize Mimics Research 21.0 to create 3D images of individual organs, skin, fat, muscles, skeleton, and spinal cord. After pre-processing in Autodesk Meshmixer, the models were converted into solid CAD objects in Ansys SpaceClaim R2021 and combined into a single abdominal model. Ansys Maxwell R2021 was then used for simulations. Five different two-electrode configurations were tested in the prototype phase with a simplified model setup. The positive electrodes were placed over the (Thoracic) T10, T12, (Lumbar) L2 and L4 vertebrae sequentially, with the negative electrode over (Sacral) S2. The simulations took on average 47.4 hours. A marked decline in electrical current penetration depth was observed as the electrodes were placed closer together which was consistent with known current distribution patterns. A preliminary validation test was also performed using a lamb's thigh. Two electrodes were placed a known distance apart and a stimulation was given, needle electrodes were then inserted in a grid-like pattern to obtain voltage values. The setup was recreated and simulated in Ansys Maxwell. The resulting average percent difference was 44.93 ± 33.3 % (5Vpp Square wave) and 35.02 ± 23.38 % (5V DC). In both instances, the highest difference was at the edges of the electrodes and the lowest difference in the midpoint between electrodes. Later versions of FEM models incorporated more organs and had improved on previous mesh generation flaws, but encountered new mesh generation errors which could not be rectified before the conclusion of the master's project. Despite complications, this project has provided a pipeline for creating similar models and shown their usability. Future work will involve overcoming the current mesh generation errors, reducing calculation time, and performing a thorough validation test.
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Robustness of Hilbert space-valued stochastic volatility models
(2024-10) Benth, Fred Espen; Eyjolfsson, Heidar; Department of Engineering
AbstractIn this paper, we show that Hilbert space-valued stochastic models are robust with respect to perturbations, due to measurement or approximation errors, in the underlying volatility process. Within the class of stochastic-volatility-modulated Ornstein–Uhlenbeck processes, we quantify the error induced by the volatility in terms of perturbations in the parameters of the volatility process. We moreover study the robustness of the volatility process itself with respect to finite-dimensional approximations of the driving compound Poisson process and semigroup generator, respectively, when considering operator-valued Barndorff-Nielsen and Shephard stochastic volatility models. We also give results on square root approximations. In all cases, we provide explicit bounds for the induced error in terms of the approximation of the underlying parameter. We discuss some applications to robustness of prices of options on forwards and volatility.
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Where Do Core Thalamocortical Axons Terminate in Mammalian Neocortex When There Is No Cytoarchitecturally Distinct Layer 4?
(2024-07) Bhagwandin, Adhil; Molnár, Zoltán; Bertelsen, Mads F.; Karlsson, Karl; Alagaili, Abdulaziz N.; Bennett, Nigel C.; Hof, Patrick R.; Kaswera-Kyamakya, Consolate; Gilissen, Emmanuel; Jayakumar, Jaikishan; Manger, Paul R.; Department of Engineering
Although the mammalian cerebral cortex is most often described as a hexalaminar structure, there are cortical areas (primary motor cortex) and species (elephants, cetaceans, and hippopotami), where a cytoarchitecturally indistinct, or absent, layer 4 is noted. Thalamocortical projections from the core, or first order, thalamic system terminate primarily in layers 4/inner 3. We explored the termination sites of core thalamocortical projections in cortical areas and in species where there is no cytoarchitecturally distinct layer 4 using the immunolocalization of vesicular glutamate transporter 2, a known marker of core thalamocortical axon terminals, in 31 mammal species spanning the eutherian radiation. Several variations from the canonical cortical column outline of layer 4 and core thalamocortical inputs were noted. In shrews/microchiropterans, layer 4 was present, but many core thalamocortical projections terminated in layer 1 in addition to layers 4 and inner 3. In primate primary visual cortex, the sublaminated layer 4 was associated with a specialized core thalamocortical projection pattern. In primate primary motor cortex, no cytoarchitecturally distinct layer 4 was evident and the core thalamocortical projections terminated throughout layer 3. In the African elephant, cetaceans, and river hippopotamus, no cytoarchitecturally distinct layer 4 was observed and core thalamocortical projections terminated primarily in inner layer 3 and less densely in outer layer 3. These findings are contextualized in terms of cortical processing, perception, and the evolutionary trajectory leading to an indistinct or absent cortical layer 4.
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Multi-SpacePhish : Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors Using Machine Learning
(2024-06-20) Yuan, Ying; Apruzzese, Giovanni; Conti, Mauro; Department of Computer Science
Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space,"making the corresponding evaluations of questionable value. Simply put, the current situation does not allow one to estimate the actual threat posed by adversarial attacks, leading to a lack of secure ML systems.We aim to clarify such confusion in this article. By considering the application of ML for Phishing Website Detection (PWD), we formalize the "evasion-space,"in which an adversarial perturbation can be introduced to fool an ML-PWD-demonstrating that even perturbations in the "feature-space"are useful. Then, we propose a realistic threat model describing evasion attacks against ML-PWD that are cheap to stage, and hence intrinsically more attractive for real phishers. After that, we perform the first statistically validated assessment of state-of-The-Art ML-PWD against 12 evasion attacks. Our evaluation shows (i) the true efficacy of evasion attempts that are more likely to occur; and (ii) the impact of perturbations crafted in different evasion-spaces; our realistic evasion attempts induce a statistically significant degradation (3-10% at p< 0.05), and their cheap cost makes them a subtle threat. Notably, however, some ML-PWD are immune to our most realistic attacks (p = 0.22).Finally, as an additional contribution of this journal publication, we are the first to propose and empirically evaluate the intriguing case wherein an attacker introduces perturbations in multiple evasion-spaces at the same time. These new results show that simultaneously applying perturbations in the problem-and feature-space can cause a drop in the detection rate from 0.95 to 0.Our contribution paves the way for a much-needed re-Assessment of adversarial attacks against ML systems for cybersecurity.

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