Background: Infliximab and adalimumab are widely used for the treatment of Crohn’s disease and ulcerative colitis. Aim: To compare the long-term efficacy and safety of infliximab and adalimumab in a large cohort of Crohn’s disease and ulcerative colitis patients reflecting real-life clinical practice. Methods: Seven hundred twelve patients were retrospectively reviewed, 410 with Crohn’s disease (268 treated with adalimumab and 142 with infliximab; median follow-up 60 months, range, 36–72) and 302 with ulcerative colitis (118 treated with adalimumab and 184 with infliximab; median follow-up 48 months, range, 36–84). Results: In Crohn’s disease, clinical remission was maintained in 75.0% of adalimumab vs. in 72.5% of infliximab patients (P = 0.699); mucosal healing and steroid-free remission were maintained in 49.5% of adalimumab vs. 63.9% of infliximab patients (P = 0.077) and in 77.7% of adalimumab vs. 77.3% in infliximab group (P = 0.957), respectively. In ulcerative colitis, clinical remission was maintained in 50.0% of adalimumab vs. 65.8% of infliximab patients (P < 0.000); mucosal healing and steroid-free remission were maintained in 80.6% of adalimumab vs. 77.0% of infliximab patients (P = 0.494) and in 90.2% of adalimumab vs. 87.5% of infliximab patients (P = 0.662), respectively. At the multivariate analysis, ileocolonic location and simple endoscopic score for Crohn’s disease >10 were predictors of failure in Crohn’s disease; treatment with adalimumab, BMI ≥30 and Mayo score >10 were predictors of failure in ulcerative colitis. infliximab was more likely to cause adverse events than adalimumab (16.6 vs. 6.2%, P < 0.000). Conclusion: Both adalimumab and infliximab are effective in long-term outpatients management of inflammatory bowel diseases. Adalimumab had a lower rate of adverse events.

Background: Comprehensive datasets quantifying the coupled thermo-mechanical and electrical properties of shape memory alloys (SMAs) are lacking, as are standardized techniques for robust characterization. This hampers accurate modeling and design of SMA-based components. Objective: This work develops an automated experimental system to enable simultaneous measurement of stress-strain-temperature behavior and electrical resistivity evolution in NiTi SMA wires under controlled stress conditions. Methods: Customized test frames apply precise mechanical stresses while allowing for in situ electrical measurements and infrared imaging during complete thermal cycling protocols. Specialized instrumentation including a Keithley 2002 multimeter, Agilent E3631A programmable power supply, and FLIR A615 thermal camera are integrated with LabVIEW-based software routines for complete automation of the characterization process. Rigorous metrology principles are implemented throughout the measurement procedure to improve accuracy, repeatability, and consistency compared to prior manual techniques. Results: Extensive datasets are generated which reveal pronounced stress-dependencies in key SMA material parameters including transformation temperatures, recoverable strain, and electrical resistivity. A 3D regression model describes the comprehensive relationship between resistivity, temperature, and applied stress across the entire characterization domain. Conclusions: The automated measurement framework and methodology establishes a foundation for high-fidelity, reliable acquisition of coupled SMA property data. This will enable more accurate modeling and design of components and systems incorporating SMA actuation or sensing functions.

This work was carried out within the context of an R&D project on morphable polymer matrix composites (PMC), actuated by shape memory alloys (SMA), to be used for active aerodynamic systems in automotives. Critical issues for SMA–polymer integration are analyzed that are mostly related to the limited strength of metal–polymer interfaces. To this aim, materials with suitable thermo-mechanical properties were first selected to avoid premature activation of SMA elements during polymer setting as well as to avoid polymer damage during thermal activation of SMAs. Nonstandard samples were manufactured for both static and fatigue pullout tests under thermo-mechanical loading, which are made of SMA wires embedded in cylindrical resin blocks. Fully coupled thermo-mechanical simulations, including a special constitutive model for SMAs, were also carried out to analyze the stress and temperature distribution in the SMA–polymer samples as obtained from the application of both mechanical loads and thermal activation of the SMA wires. The results highlighted the severe effects of SMA thermal activation on adhesion strength due to the large recovery forces and to the temperature increase at the metal–polymer interface. Samples exhibit a nominal pullout stress of around 940 MPa under static mechanical load, and a marked reduction to 280 MPa was captured under simultaneous application of thermal and mechanical loads. Furthermore, fatigue run-out of 5000 cycles was achieved, under the combination of thermal activation and mechanical loads, at a nominal stress of around 200 MPa. These results represent the main design limitations of SMA/PMC systems in terms of maximum allowable stresses during both static and cyclic actuation.

Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003–2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (−7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI’s capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning’s exploration–exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications.

Shape memory alloy (SMA) actuated composites enable real-time geometry modification for enhanced automotive aerodynamic performance. However, integrating SMAs into polymer matrices presents significant challenges in thermomechanical coupling and interface reliability under cyclic loading conditions. This study investigates SMA-polymer active composites for vehicle underbody shield applications through integrated experimental-computational methods. The approach combines multiphysics finite element modeling with precision manufacturing protocols, incorporating SMA-based actuators into a hybrid PC/ABS-carbon fiber reinforced polymer system. Multiple configurations with varying actuator densities and composite layer architectures (2–3 plies) were systematically characterized. The optimized configuration achieves 30 mm deflection at 135 °C with peak stresses below 435 MPa, demonstrating a displacement-to-length ratio of 0.4. The validated multiphysics framework predicts system behavior within 5% of experimental measurements. A scalable three-phase manufacturing protocol (carbon fiber reinforced polymer lamination, component assembly, SMA integration) enables consistent production of meter-scale adaptive structures while maintaining precise geometric tolerances. This research establishes new performance benchmarks for morphing automotive components through validated design methodologies. Future development should focus on thermal management optimization for enhanced durability under varied environmental conditions.