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Electric Vehicles (EV) are embedded with increased software algorithms coupled with several physical systems. It demands the efficacy of components which are linked together to build a system. The digital models reviewed in this paper are at system-level and full vehicle-level, comprising many components and control design, analysis, and optimization. Systems pertaining to each functionality such as, A/C (Air Conditioning) loop, E-Powertrain (Electric Powertrain), HEVC (Hybrid Electric Vehicle Controller), Cooling system, Battery Management System (BMS), Vehicle control system etc. together make an ‘Integrated Digital Vehicle.’ Fidelity of Intersystem co-simulation [AMESIM + SIMULINK] is key to validating thermal and energy strategies. This paper elucidates the correlation of Digital Vehicle compared to Test for Thermal Strategy in different driving scenarios and Energy management. Validation of Digital vehicle with 52kWh, 40kWh High Voltage Battery for Intercity Travel of Customer usage -5°C and Traffic Jam with ERP for Cold condition of 9°C). Also, to evaluate range prediction, autonomy, Energy balance to meet Thermal comfort (based on PTC & Compressor activation strategy). In precedence, we validate the Pre-conditioning strategy of battery to reach optimal temperature for efficient charging and link with navigation system. Thermal validation also encompasses the Heat Recovery from Electric motor loop to Battery loop across dynamic drive-cycles and under a range of weather conditions. Digital Vehicle entails a System level correlation to ascertain the robustness SOC: ±2%, HVBAT: ±3°C accuracy, Energy Balancing, Charging Efficiency and furthermore.

Monitoring the health of heterogeneous industrial robot fleets is severely challenged by the multi-modal nature of their operational cycles and a persistent scarcity of run-to-failure data. Standard data-driven approaches, particularly deep learning architectures relying on sequential reconstruction, often struggle in this specific setting; they tend to over-smooth complex dynamics, masking early signs of degradation. To address these industrial constraints, we reframe the monitoring problem through a framework based on Phase Space Reconstruction (PSR). Instead of predicting temporal sequences, this framework transforms univariate sensor data into a geometric attractor, explicitly unfolding the mechanical states independently of their temporal occurrence. By evaluating various anomaly scoring techniques within this space, we demonstrate that discrete support estimation provides an effective and computationally frugal Health Indicator (HI). Validated on a real-world dataset of 21 heterogeneous robots over three years and a synthetic Langevin system, our approach outperforms standard deep learning baselines. We show that aligning the algorithmic bias with the geometric properties of the target system yields a pragmatic, traceable and easily deployable approach perfectly tailored to the realities of industrial constraints.

This study investigates physics-informed and data-driven hybrid modeling strategies for an automotive-grade electrohydraulic (EH) semi-active damper system. Although deep sequence learning architectures such as Long Short-Term Memory (LSTM) networks and Transformers can provide high predictive accuracy, purely data-driven approaches may struggle to preserve physical consistency and maintain robustness under unseen operating conditions. These limitations become more pronounced for EH dampers, whose hysteretic characteristics exhibit highly nonlinear and non-proportional variations under different current and frequency excitations, unlike the more scalable behavior commonly observed in magnetorheological (MR) dampers. To address these challenges, two physics-informed integration strategies are investigated. The first strategy combines physical and data-driven models through parallel loss-function synthesis. The second strategy introduces a learnable physics layer (PINN-Hybrid), in which the coefficients of the extended hyperbolic tangent formulation are adaptively learned within the neural network architecture. In this framework, the physical model acts as a structural regularization mechanism that guides the learning process while preserving the flexibility of data-driven sequence modeling. The proposed models are evaluated under abrupt valve-control operating conditions. Comparative results indicate that the proposed physics-informed architectures improve hysteresis continuity, physical plausibility, and robustness compared with purely data-driven approaches, particularly in low-velocity and transition regions. The proposed framework therefore demonstrates the potential of physics-informed learning strategies for reliable real-time modeling of nonlinear automotive EH damper systems.

Aims This registry-based study aimed to evaluate the long-term survivorship of dual-mobility components (DMCs) compared to conventional components (CCs) in primary total hip arthroplasty (THA), and to assess potential adverse effects, particularly in younger patients (aged < 75 years). Methods Data from 58,314 primary THAs recorded between January 2006 and December 2023 in the French national SOFCOT/RENACOT registry were analyzed, including 25,545 DMCs (46%). The primary outcome was revision for any cause. Kaplan-Meier survival curves and Cox proportional hazards models were used to compare implant survivorship, adjusting for age, sex, primary diagnosis, and fixation method. Results Among the 25,545 primary THAs performed with a DMC, 284 revisions (1.1%) were recorded. In adjusted Cox regression analysis, DMC use was not significantly associated with an increased risk of revision compared to CCs (hazard ratio 0.83 (95% CI 0.66 to 1.04); p = 0.118). Periprosthetic fracture was the leading cause of revision in the DMC group (98/284, 34.5%), occurring significantly more frequently than in the CC group (77/493, 15.6%; p < 0.001), while dislocation-related revisions were less common with DMCs. Overall, the combined proportion of revisions due to dislocation or fracture was lower in the DMC group (112/284, 39.4%) than in the CC group (254/493, 51.5%). No risk factors for implant failure were identified in multivariable Cox regression analysis. Conclusion DMCs provide excellent long-term survivorship and effectively reduce the risk of dislocation in primary THA. Although a higher incidence of periprosthetic fractures was observed with DMCs, the overall outcomes support their safe and effective use. Nevertheless, ongoing surveillance remains important to monitor these risks. Cite this article: Bone Joint J 2026;108-B(3):294–301.

Credibility of a simulation model is an important topic. Several approaches try to quantify the credibility of simulation. However, models are mostly assembled within a simulation architecture. Can the credibility of a simulation architecture be assessed based on the credibility of the models that comprise it? This paper aims to address this issue by providing an overview of the current state of the art in the field of assembly credibility. It will compare sensitivity analysis techniques, qualitative analysis by experts, explainability in AI, and networks. Finally, an assessment of the proposed approaches, based on criteria such as rigor, generalization, and resource requirements, will reveal the strengths and weaknesses of each approach.