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This article portrays the authors' experience with a complex lower limb bone and soft tissue defect, following chronic osteomyelitis and pathological fracture, which was managed by the multidisciplinary orthoplastic team. The decision for functional amputation versus limb salvage was deemed necessary, enhanced by the principles of “spare parts” in reconstructive microsurgery. This case describes the successful use of the osteocutaneous distal tibia turn-up fillet flap that allowed “lowering the level of the amputation” from a through knee to a below-knee amputation (BKA) to preserve the knee joint function. We comprehensibly review reports of turn-up flaps which effectively lower the level of amputation, also applying “spare-parts” surgery principles and explore how these concepts refine complex orthoplastic approaches when limb salvage is not possible to enhance function. The osteocutaneous distal tibia turn-up fillet flap is a robust technique for modified BKA reconstructions that provides sufficient bone length to achieve a tough, sensate stump and functional knee joint.

Abstract Aims We aim to assess the theoretical impact of the atrial flow regulator (AFR) on survival in heart failure. Methods and results The prospective, multicentre, open‐label, non‐randomised PRELIEVE study (NCT 03030274) assessed the safety and efficacy of the Occlutech AFR device in patients with symptomatic heart failure with reduced ejection fraction (HFrEF) (left ventricular ejection fraction (LVEF) ≥ 15% and

Electric vehicles (EVs) and photovoltaics (PVs) are expected to be broadly adopted in future power systems. However, the temporal variability of EV load and PV production presents challenges for integrating them into the power grid. This study evaluates and assesses the synergies between EVs and PV systems to maximize solar energy utilization for EV load coverage. The configurations studied include EV charging via the national grid as a reference case (Case 1) and two solar energy harvesting options: EVs powered directly by vehicle-mounted PVs (Case 2) and EV chargers connected to residential PV installations (Case 3). These cases are evaluated across different urban environments with large EV fleets and dissimilar weather conditions: Berlin and Los Angeles. A customized operation profile based on the worldwide harmonized light-duty test cycle (WLTC) and a charge-right-away (CRA) strategy is used. Energy performance analysis is conducted through dynamic simulations using the Modelica language, with environmental and economic indices derived. Key findings highlight the superior performance of residential PV systems in both cities compared to current solar EV technologies, with both solutions offering significant benefits over the reference case. Cases 2 and 3 result in a 44% and 59% reduction in annual energy consumption, greenhouse gas emissions, and charging costs in Berlin, while in Los Angeles, the reductions are 67% and 98%. The average daily solar driving range reaches 20.3% in Berlin and 30.4% in Los Angeles.

This study emphasizes the explainability of machine learning (ML) models in predicting the seismic response of rigid rocking structures, specifically using the LightGBM algorithm. By employing SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), and accumulated local effects (ALE), a comprehensive feature importance analysis has been performed. This revealed that ground motion parameters, particularly peak ground acceleration (PGA), are critical for predicting small rotations, while structural parameters like slenderness and frequency are more significant for larger rotations. Utilizing an extensive dataset generated from nonlinear time history analyses, the trained LightGBM model demonstrated high accuracy in estimating the maximum rotation angle of rigid blocks under natural ground motions. The study also examined the sensitivity of model performance to lower bound thresholds of the target variable, revealing that reduced feature sets can maintain predictive performance effectively. These findings advance ML-based modeling of seismic rocking responses, providing interpretable and accurate models that enhance our understanding of rocking structures’ dynamic behavior, which is crucial for designing resilient structures and improving seismic risk assessments. Future research will focus on incorporating additional parameters and exploring advanced ML techniques to further refine these models.

Abstract Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used ‘stubby building’ approximation as well as a more realistic and innovative ‘building hole’ approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement.

Abstract Background Postpartum depression affects around 12% of mothers in developed countries, with consequences for the whole family. Many women with depressive symptoms remain undetected and untreated. The aim of this study was to investigate to what extent women with depressive symptoms at 6 weeks postpartum are identified by the healthcare system, the interventions they received, and remission rates at 6 months postpartum. Methods Postpartum women scoring 12–30 on the Edinburgh Postnatal Depression Scale (EPDS) at 6 weeks after delivery (n = 697) were identified from the longitudinal cohort study “Biology, Affect, Stress, Imaging and Cognition” (BASIC) in Uppsala, Sweden. A total of 593 women were included. Background and remission information at 6 months was collected from the BASIC dataset. Medical records were examined to identify interventions received. Results Most women (n = 349, 58.7%) were not identified by the healthcare system as having depressive symptoms and 89% lacked any record of interventions. Remission rates at 6 months postpartum were 69% in this group. Among women identified by the healthcare system, 90% received interventions and about 50% were in remission at 6 months postpartum. The EPDS reduction during the study period was largest in the group identified by the child health services (CHS, −5.15) compared to the non-identified (−4.24, p

Objective: Several supra-annular self-expanding transcatheter systems are commercially available for transcatheter aortic valve implantation (TAVI). Comparative data in large-scale multicenter studies are scant. Methods: This study included a total of 5175 patients with severe aortic stenosis undergoing TAVI with the ACURATE neo (n = 1095), Evolut R (n = 3365), or Evolut PRO (n = 715) by a transfemoral approach at five high-volume centers. Propensity score matching resulted in 654 triplets. Outcomes are reported according to the Valve Academic Research Consortium-3 (VARC-3) consensus. Results: Moderate or severe paravalvular regurgitation (PVR) occurred significantly more often in the ACURATE neo group (5.2%) than in the Evolut R (1.8%) and Evolut PRO (3.3%) groups (p = 0.003). The rates of major vascular-/access-related complications (4.6%, 3.8%, and 5.0%; p = 0.56), type 3 or 4 bleeding (3.2%, 2.1%, and 4.1%; p = 0.12), and 30-day mortality (2.4%, 2.6%, and 3.7%; p = 0.40) were comparable. The rate of new permanent pacemaker implantation (PPI) was significantly lower in the ACURATE neo group (9.5%, 17.6%, and 16.8%; p

Seismic vulnerability assessment is one of the most impactful engineering challenges faced by modern societies. Thus, authorities require a reliable tool that has the potential to rank given structures according to their seismic vulnerability. Various countries and organizations over the past decades have developed Rapid Visual Screening (RVS) tools aiming to efficiently estimate vulnerability indices. In general, RVS tools employ a set of structural features and their associated weights to obtain a vulnerability index, which can be used for ranking. In this paper, Machine Learning (ML) models are implemented within this framework. The proposed formulation is used to train binary classifiers in conjunction with ad hoc rules, employing the features of various Codes (e.g., the Federal Emergency Management Agency, New Zealand, and Canada). The efficiency of this modeling effort is evaluated for each Code separately and it is clearly demonstrated that ML-based models are capable of outperforming currently established engineering practices. Furthermore, in the spirit of the aforementioned Codes, a linearization of the fully trained ML model is proposed. ML feature attribution techniques, namely SHapley Additive exPlanations (SHAP) are employed to introduce weights similar to engineering practices. The promising results motivate the potential applicability of this methodology towards the recalibration of the RVS procedures for various types of cases.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, as the battery is a key element in the energy performance of an EV powertrain system. The equivalent circuit model (ECM) technique at the cell level is commonly employed for this purpose, offering a balance of accuracy and efficiency in representing battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell has been developed to ensure high accuracy and performance. Modelica, an acausal and object-oriented equation-based modeling language, has been used for its advantageous features, including the development of extendable, modifiable, modular, and reusable models. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from lithium-ion (Li-ion) battery cells with four different commonly used cathode materials, have been utilized. This approach allows for the representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed, and the simulation results of the US Environmental Protection Agency Federal Test Procedure (FTP-75) driving cycle have been compared with an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both models across different system points. Specifically, the maximum vehicle velocity deviation during the cycle reaches 1.22 km/h, 8.2% lower than the corresponding value of the reference application. The maximum deviation of SoC is limited to 0.06%, and the maximum value of relative voltage deviation is 1.49%. The verified model enables the exploration of multiple potential architecture configurations for EV powertrains using Modelica.