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AreTomo, an abbreviation for Alignment and Reconstruction for Electron Tomography, is a GPU accelerated software package that fully automates motion-corrected marker-free tomographic alignment and reconstruction in a single package. By correcting in-plane rotation, translation, and importantly, the local motion resulting from beam-induced motion from tilt to tilt, AreTomo can produce tomograms with sufficient accuracy to be directly used for subtomogram averaging. Another major application is the on-the-fly reconstruction of tomograms in parallel with tilt series collection to provide users with real-time feedback of sample quality allowing users to make any necessary adjustments of collection parameters. Here, the multiple alignment algorithms implemented in AreTomo are described and the local motions measured on a typical tilt series are analyzed. The residual local motion after correction for global motion was found in the range of ± 80 Å, indicating that the accurate correction of local motion is critical for high-resolution cryo-electron tomography (cryoET).

Evidentiary requirements for relative effectiveness assessment vary among European health technology assessment (HTA) bodies, affecting the time to HTA decision-making and potentially delaying time to patient access. Improved alignment may reduce this time; therefore, we aim to analyze the differences in evidentiary requirements for oncology drug assessments among European HTA bodies and provide recommendations toward an increased alignment. Interviews were conducted with stakeholders in drug assessments of Italy, the Netherlands, Poland, Portugal, England and Wales, and Sweden about evidentiary requirements for several subdomains to identify differences and obtain recommendations for addressing differences. The interview results were analyzed on degrees of evidence acceptability per HTA body and alignment on evidentiary requirements among HTA bodies. Subdomains demonstrating noteworthy differences concerned the acceptability of extrapolation to other populations, class effects, progression-free survival and (other) surrogate endpoints as outcomes, the absence of quality-of-life data, single-arm trials, cross-over trial designs, short trial duration, and the clinical relevance of effect size. Alignment can be enhanced to reduce time to decision-making and to improve equity in patient access. Proposed recommendations to achieve this included joint early dialogues, intensified collaboration and exchange between countries, joint relative effectiveness assessments, and the use of access agreements. [ABSTRACT FROM AUTHOR]

Abstract TRIM25 is an RNA-binding ubiquitin E3 ligase with central but poorly understood roles in the innate immune response to RNA viruses. The link between TRIM25’s RNA binding and its role in innate immunity has not been established. Thus, we utilized a multitude of biophysical techniques to identify key RNA-binding residues of TRIM25 and developed an RNA-binding deficient mutant (TRIM25-m9). Using iCLIP2 in virus-infected and uninfected cells, we identified TRIM25’s RNA sequence and structure specificity, that it binds specifically to viral RNA, and that the interaction with RNA is critical for its antiviral activity.

Increasing urbanisation and growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front-axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry-standard load cases for the strength design of the components. To design the chassis components, the available installation space is determined, and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify the component geometries that are suitable for the force flow. The optimisation setup is selected in such a way that it is possible to integrate information, energy, and material-carrying conductions into the suspension arms. High-strength light metals are used to minimise the component masses. Apertures are provided through the components for the routing of electrical conductors. The transport of fluids is realised by conductions integrated into the wishbones. The final geometries of the suspension components are then validated by a finite element analysis (FEA) of the overall suspension model. The results of the applied method are lightweight suspension components with a high degree of functional integration. This improves the vehicle package and achieves higher front-wheel clearance, increasing the possible steering angles and thus improving manoeuvrability. The saving of unsprung mass can improve handling and has a positive effect on the vehicle’s energy consumption. Furthermore, the sectional conduction integration is followed by a simplified assembly of the front-axle suspension.

Novel vehicle concepts are needed to meet the requirements of resource-conserving and efficient mobility in the future, especially in urban areas. In the automated, driverless electric vehicle concept U-Shift, a new form of mobility is created by separating a vehicle into a drive module and a transport capsule. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and is therefore used to transport both people and goods. The wide range of possible capsules poses major challenges for the development of the Driveboard and the chassis in particular. A lifting actuator integrated into the chassis concept enables levelling and, thus, the raising and lowering of the Driveboard and the capsules to ground level. This means that no additional lifting devices are required for changing the capsules or for lowering them to the ground, e.g., for loading and unloading the capsules. To realise this mechanism simply and efficiently, a fully electromechanical actuator is designed and constructed. The actuator consists primarily of a profile rail guide, a steel cable winch, an electric motor, a housing that connects the subsystems and a locking mechanism. The electric motor is used to lift the vehicle and regulate the weight force-driven lowering of the vehicle. This paper describes the design of the actuator and shows the dimensioning of all main components according to the boundary conditions. Finally, the prototype model of the realised concept is presented.

The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature. This work extends a method for the development of Quantile Convolutional and Quantile Recurrent Neural Networks (namely Q*NN). Fleet data of 225 629 drives are clustered and balanced, simulation data from 971 simulations are augmented before they are combined for training and testing. The Q*NN hyperparameters are optimized using an efficient Bayesian optimization, before the Q*NN models are compared with regression and quantile regression models for four horizons. The analysis of point-forecast and quantile-related metrics shows the superior performance of the novel Q*NN models. The median predictions of the best performing model achieve an average RMSE of 0.66°C and R2 of 0.84. The predicted 0.99 quantile covers 98.87% of the true values in the test data. In conclusion, this work proposes an extended development and comparison of Q*NN models for accurate battery temperature prediction.

Abstract Background In recent years, projects to develop reporting guidelines have attempted to integrate the perspectives of patients and public members. Best practices for patient and public involvement (PPI) in such projects have not yet been established. We recently developed an extension of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), to be used for systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) for OMIs 2024. Patients and public members formed a small but impactful stakeholder group. We critically evaluated the PPI component in this project and developed recommendations for conducting PPI when developing reporting guidelines. Main text A patient partner was an integral research team member at the project development and grant application stage. Once the project started, five patient and public contributors (PPCs) were recruited to participate in the Delphi study; three PPCs contributed to subsequent steps. We collected quantitative feedback through surveys; qualitative feedback was garnered through a focus group discussion after the Delphi study and through debrief meetings after subsequent project activities. Feedback was thematically combined with reflections from the research team, and was predominantly positive. The following themes emerged: importance of PPI partnership, number of PPCs involved, onboarding, design of Delphi surveys, flexibility in the process, complexity of PPI in methodological research, and power imbalances. Impacts of PPI on the content and presentation of the reporting guideline were evident, and reciprocal learning between PPCs and the research team occurred throughout the project. Lessons learned were translated into 17 recommendations for future projects. Conclusion Integrating PPI in the development of PRISMA-COSMIN for OMIs 2024 was feasible and considered valuable by PPCs and the research team. Our approach can be applied by others wishing to integrate PPI in developing reporting guidelines.

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development.

Abstract Background The CHILD-BRIGHT Network, a pan-Canadian childhood disability research Network, is dedicated to patient-oriented research, where numerous stakeholders, including patient-partners, researchers, and clinicians are involved at different levels. The Network is committed to continuously improving the level of engagement and partnerships’ impact. Measuring patient engagement is therefore important in reflecting on our practices and enhancing our approaches. We aimed to measure patient engagement longitudinally and explore in greater depth the perceived benefits, barriers and facilitators, and overall satisfaction with patient engagement, from the perspectives of the different stakeholders. Methods Patient engagement was measured using online surveys. In a longitudinal study design over a 3-years period (2018–2020) the Community-Based Participatory Research (CBPR) questionnaire was used. To enrich our understanding of patient engagement in Year 3, we employed the Public and Patient Engagement Evaluation Tool (PPEET) in a cross-sectional, convergent parallel mixed-method study design. Descriptive statistics and a thematic-based approach were used for data analysis. Results The CBPR questionnaire was completed by n = 167 (61.4% response rate), n = 92 (30.2% response rate), and n = 62 (14.2% response rate) Network members in Years 1, 2, and 3, respectively. Ninety-five (n = 95, 21.8% response rate) members completed the PPEET in Year 3. CBPR findings demonstrate a stable and high satisfaction level with patient engagement over time, where 94%, 86%, and 94% of stakeholders indicated that the project is a “true partnership” in Years 1, 2, and 3, respectively. In Years 2 and 3, we noted an improvement in patient-partners’ comfort level in sharing their views and perspectives (92% and 91% vs. 74%). An increase in critical reflective trust (i.e., allowing for discussing and resolving mistakes) from Year 1 to 3 was found, both from the perspectives of patient-partners (51–65%) and researchers (48–75%). Using the PPEET, patient engagement factors (i.e., communications and supports for participation, ability to share views and perspectives) and impact were highly rated by most (80–100%) respondents. PPEET’s qualitative responses revealed several patient engagement advantages (e.g., increased projects’ relevance, enhanced knowledge translation), barriers (e.g., group homogeneity), facilitators (e.g., optimal communication strategies), and solutions to further improve patient engagement (e.g., provide clarity on goals). Conclusion Our 3-years patient engagement evaluation journey demonstrated a consistent and high level of satisfaction with patient engagement within the Network and identified advantages, barriers, facilitators, and potential solutions. Improvements were observed in members’ comfort in sharing their views and perspectives, along with an increase in critical reflective trust. These findings underscore the Network's commitment to enhancing patient engagement and provide valuable insights for continued improvement and optimization of collaborative efforts.

The cellular thermal shift assay (CETSA®) is a target engagement method widely used for preclinical characterization of small molecule compounds. CETSA® has been used for semi-quantitative readouts in whole blood with PBMC isolation, and quantitative, plate-based readouts using cell lines. However, there has been no quantitative evaluation of CETSA® in unprocessed human whole blood, which is preferred for clinical applications. Here we report two separate assay formats – Alpha CETSA® and MSD CETSA® – that require less than 100 μL of whole blood per sample without PBMC isolation. We chose RIPK1 as a proof-of-concept target and, by measuring engagement of seven different inhibitors, demonstrate high assay sensitivity and robustness. These quantitative CETSA® platforms enable possible applications in preclinical pharmacokinetic-pharmacodynamic studies, and direct target engagement with small molecules in clinical trials.

Abstract Homozygous CDKN2A/B deletion has been associated with an increased risk of recurrence in meningiomas. However, the evidence is confined to a limited number of studies, and the importance of heterozygous CDKN2A/B deletions remains insufficiently investigated. Hence, the present meta-analysis reconstructs individual patient data (IPD) and reconstructs the probabilities of progression-free survival (PFS) stratified by CDKN2A/B status. IPD of PFS rates were extracted from published Kaplan–Meier plots using the R package IPDfromKM in R studio (RStudio, Boston, MA, USA). Reconstructed Kaplan–Meier Plots of the pooled IPD data were created. One-stage and two-stage meta-analyses were performed. Hazard ratios (HR) were used as effective measures. Of 181 records screened, four articles with 2521 participants were included. The prevalence of homozygous CDKN2A/B deletions in the included studies was 0.049 (95% CI 0.040–0.057), with higher tumor grades associated with a significantly greater proportion of CDKN2A/B deletions. The reconstructed PFS curves for the pooled cohort showed that the median PFS time of patients with a CDKN2A/B wild-type status, heterozygous or homozygous CDKN2A/B deletion was 180.0 (95% CI 145.7–214.3), 26.1 (95% CI 23.3–29.0), and 11.00 (95% CI 8.6–13.3) months, respectively (p

Abstract Understanding the migratory patterns of large whales is of conservation importance, especially in identifying threats to specific populations. Migration ecology, including migratory destinations, movements and site fidelity for humpback whales (Megaptera novaeangliae) remain poorly studied in parts of the range of the Central America population, considered endangered under the United States Endangered Species Act. This study aimed to investigate the migratory destinations of humpback whales sighted at two study sites in Nicaragua, which are part of the Central America population. A ten-year photographic database of humpback whales observed off Nicaragua was combined with citizen science contributions and sightings from dedicated research programs. The resulting image collection was compared with available historical photo identifications and databases using an automated image recognition algorithm. This approach yielded 36 years of photographic identification totaling 431 recaptures in Nicaragua (2006–2008 and 2016–2021) and 2539 recaptures (1986–2020) in both feeding and breeding grounds of 176 unique individuals sighted in Nicaragua. Our results showed that photo-identified whales were recaptured between October and April in breeding grounds and year-round in feeding grounds between British Columbia and California, with peak recaptures between June and October. Our study provided first-time evidence on fine-scale site affinity of individual humpback whales within Nicaraguan waters and to other breeding and feeding grounds.

Introduction Rates of incapacity to work due to mental disorders have increased in many European countries. The consequences of persistent stress can impact individuals’ physical and psychological well-being and gradually develop into chronic stress. Mental disorders or symptoms of burn-out syndrome can have severe consequences. Mental disorders leading to work incapacity significantly burden the health system. Prevention interventions can protect against burn-out, depression, anxiety and other mental health disorders. Digital health is a promising approach to increase the utilisation of effective prevention interventions. This mixed-methods study evaluates a newly developed app-supported psychosocial prevention intervention called ‘RV Fit Mental Health’ to strengthen participation in working life.Methods and analysis The study uses a three-stage parallel mixed-methods design. This study accompanies the development (stage 1), piloting (stage 2) and evaluation (stage 3) of the new intervention. Within the stages, there is a quantitative as well as a qualitative research strand. Employed persons with an incipient mental disorder will be included. Additionally, experts within the project or connected areas will be included. Quantitative data will be analysed using multifactorial variance analyses in a pre–post design. Qualitative data will be analysed using qualitative content analysis. The study is a comprehensive research approach to investigate the development, piloting and evaluation of an app-supported psychosocial app-based prevention intervention. The rigour of the study will be achieved through data triangulation.Ethics and dissemination All participants will receive detailed study information and give written informed consent before data collection. Ethical approval was obtained from the Technical University of Munich Ethics Committee. All data collection will follow all legislative rules regarding data protection, also following the Declaration of Helsinki. The study results will be disseminated in peer-reviewed journals and presented at international conferences.Trial registration numbers DRKS00030818 and DRKS00033080.

Mediante la Ley de Modernización de la Seguridad Social en Salud, Ley N° 26790, se regula el Seguro Complementario de Trabajo de Riesgo (SCTR) en el sistema peruano, un seguro de caracter obligatorio a cargo del empleador en beneficio de los trabajadores que realizan labores de alto riesgo, a fin de dar cobertura a siniestros laborales como accidentes de trabajo y enfermedades profesionales. No obstante, una de las problemáticas es el dictamen, informe o examen médico que sirve de sustento para determinar el beneficio económico a favor del asegurado una vez suscitado el siniestro, debido a que en el mismo procedimiento pueden presentarse innumerables dictámenes, informes o examenes médicos con o sin menoscabos, lo que generan incertidumbre sobre su real condición de invalidez. En tal sentido, se reflexiona sobre el SCTR en el sistema peruano y desarrolla los pronunciamientos del Tribunal Constitucional sobre el valor probatorio de los dictámenes, informes o examenes médicos.

The Stanley controller is a proven approach for path tracking control in automated vehicles. If time delays occur, for example, in signal processing and steering angle control, precision and stability decrease. In this article, enhancements for the Stanley controller are proposed to achieve stable behavior with improved tracking accuracy. The approach uses the curvature of the path as feedforward, whereby the reference point for the feedforward input differs from that of the controller setpoints. By choosing a point further along the path, the negative effects of system delay are reduced. First, the parameters of the Stanley controller are calibrated using a straight line and circle maneuver. Then, the newly introduced feedforward parameter is optimized on a dynamic circuit. The approach was evaluated in simulation and validated on a demonstrator vehicle. The validation tests with the demonstrator vehicle on the dynamic circuit revealed a reduction of the root-mean-square cross-track error from 0.11 m to 0.03 m compared to the Stanley controller. We proved that the proposed approach optimizes the Stanley controller in terms of compensating for the negative effects of system delay. This allows it to be used in a wider range of applications that would otherwise require a more complex control approach.

Abstract Recent work has shown that perceptual training can be used to improve the performance of novices in real-world visual classification tasks with medical images, but it is unclear which perceptual training methods are the most effective, especially for difficult medical image discrimination tasks. We investigated several different perceptual training methods with medically naïve participants in a difficult radiology task: identifying the degree of hepatic steatosis (fatty infiltration of the liver) in liver ultrasound images. In Experiment 1a (N = 90), participants completed four sessions of standard perceptual training, and participants in Experiment 1b (N = 71) completed four sessions of comparison training. There was a significant post-training improvement for both types of training, although performance was better when the trained task aligned with the task participants were tested on. In both experiments, performance initially improves rapidly, with learning becoming more gradual after the first training session. In Experiment 2 (N = 200), we explored the hypothesis that performance could be improved by combining perceptual training with explicit annotated feedback presented in a stepwise fashion. Although participants improved in all training conditions, performance was similar regardless of whether participants were given annotations, or underwent training in a stepwise fashion, both, or neither. Overall, we found that perceptual training can rapidly improve performance on a difficult radiology task, albeit not to a comparable level as expert performance, and that similar levels of performance were achieved across the perceptual training paradigms we compared.

Plain English summary This qualitative research paper seeks to understand patient engagement in large teams and networks. Patient engagement is the meaningful and active partnership of patients on a research team. We aim to understand the factors needed in a research environment that consider and include patients. Patient engagement was measured through interviews with 25 CHILD-BRIGHT Network members, either patient-partners or researchers, about their experiences. In this study, patient-partners were the parents of youth affected by brain-based disorders. We identified factors that made it easier or more difficult for patient-partners to engage with the projects and the network. Additionally, we looked at the impacts of patient engagement as observed by the interviewees. We found that at the project level and network level, the factors that helped engagement and made it difficult to engage were similar for both patient-partners and researchers. At the project level for example, open communication and factors specific to patient-partners (e.g., motivation to contribute) were identified by patient-partners and researchers as helping engagement. Maintaining long-term engagement and ensuring meaningful collaboration were identified as factors that make engagement difficult. At the network level, both patient-partners and researchers noted that communication (e.g., regular follow-cup) made it easier to engage while time constraints and asking too much from patient-partners made engagement more difficult. Finally, interviewees shared that patient engagement impacted patient-partners, researchers, and the research being conducted. Patient engagement helped ensure that the research reflected patient-partners’ priorities, allowed collaboration, and provided patient-partners and researchers with learning opportunities. The results of our research have allowed us to identify strategies that can be used to create more meaningful engagement within large research teams.

As fleet electrification progresses, vehicles are continuously becoming heavier, while the used electric motors, with their high torques, enable longitudinal dynamics to be maintained or even increased. This raises the question of what effect electric vehicles have on the emission of tire–road particulate matter (PM). To answer this question, investigations were carried out in this study on a tire internal drum test bench with real road surfaces. In addition to the vertical load, the tire inflation pressure and the driving speed were varied. PM emissions were recorded in real time, resulting in emission factors (emission per kilometer driven) for different load conditions. This allows statements to be made about both the effect on the total emission and on the particle size distribution. It was shown that the PM emission increases linearly with the vertical load at constant longitudinal dynamics. If the tire inflation pressure is increased, the emission also increases linearly, and the increases in emission are equally large for both influences. A clear influence of the driving speed on the emission factor could not be determined. With regard to the particle size distribution, the following correlations were found: higher vertical load and higher tire inflation pressure result in a larger mean particle diameter, while a higher driving speed reduces it. Thus, this study contributes to a better understanding of the expected changes in tire-road PM emissions as a result of electrification.

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

Abstract Infection of Arabidopsis with avirulent Pseudomonas syringae and exposure to nitrogen dioxide (NO2) both trigger hypersensitive cell death (HCD) that is characterized by the emission of bright blue‐green (BG) autofluorescence under UV illumination. The aim of our current work was to identify the BG fluorescent molecules and scrutinize their biosynthesis, localization, and functions during the HCD. Compared with wild‐type (WT) plants, the phenylpropanoid‐deficient mutant fah1 developed normal HCD except for the absence of BG fluorescence. Ultrahigh resolution metabolomics combined with mass difference network analysis revealed that WT but not fah1 plants rapidly accumulate dehydrodimers of sinapic acid, sinapoylmalate, 5‐hydroxyferulic acid, and 5‐hydroxyferuloylmalate during the HCD. FAH1‐dependent BG fluorescence appeared exclusively within dying cells of the upper epidermis as detected by microscopy. Saponification released dehydrodimers from cell wall polymers of WT but not fah1 plants. Collectively, our data suggest that HCD induction leads to the formation of free BG fluorescent dehydrodimers from monomeric sinapates and 5‐hydroxyferulates. The formed dehydrodimers move from upper epidermis cells into the apoplast where they esterify cell wall polymers. Possible functions of phenylpropanoid dehydrodimers are discussed.

Detection dogs were trained to detect SARS-CoV-2 infection based on armpit sweat odor. Sweat samples were collected using cotton pads under the armpits of negative and positive human patients, confirmed by qPCR, for periods of 15–30 min. Multiple hospitals and organizations throughout Belgium participated in this study. The sweat samples were stored at −20°C prior to being used for training purposes. Six dogs were trained under controlled atmosphere conditions for 2–3 months. After training, a 7-day validation period was conducted to assess the dogs’ performances. The detection dogs exhibited an overall sensitivity of 81%, specificity of 98%, and an accuracy of 95%. After validation, training continued for 3 months, during which the dogs’ performances remained the same. Gas chromatography/mass spectrometry (GC/MS) analysis revealed a unique sweat scent associated with SARS-CoV-2 positive sweat samples. This scent consisted of a wide variety of volatiles, including breakdown compounds of antiviral fatty acids, skin proteins and neurotransmitters/hormones. An acceptability survey conducted in Belgium demonstrated an overall high acceptability and enthusiasm toward the use of detection dogs for SARS-CoV-2 detection. Compared to qPCR and previous canine studies, the detection dogs have good performances in detecting SARS-CoV-2 infection in humans, using frozen sweat samples from the armpits. As a result, they can be used as an accurate pre-screening tool in various field settings alongside the PCR test.

Only a small part of the high performance of electric drive systems in vehicles is used in everyday operation by customers. As a result, most drives are not operated in the optimum efficiency range. Designing a suitable drive system, whose performance is aligned with actual customer requirements, presents the potential to increase efficiency. Based on the findings of previous research, this paper serves to complement an existing method, which already introduced the basic method of transferring statistical customer data into relevant parameters for the design of a customer-specific drive system. In order to improve the method, further criteria for the selection of relevant time series come into place. Furthermore, the impact on maximum loads resulting from various sequences of the selected time series is identified and evaluated with time frame-based analysis. A new approach for the effective computation of maximum design-relevant loads in the admissible time frame range is introduced and validated. By taking this approach, the sensitivity of the derived design parameters regarding various time series sequence is evaluated in the context of selected datasets. In addition, concatenations of time series are identified which may have a relevant influence on the maximum loads. Consequently, the design process is safeguarded thoroughly against potential maximum loads as well as the associated thermal stresses.

El artículo, aborda algunas reflexiones teórico-metodológicas del problema de la relación ciencia-axiología, donde se tiene en cuenta los factores valorativos de la ciencia como proceso social y como una forma fundamental de actividad investigativa, valorativa y orientadora, desde un enfoque marxista y leninista. Se destaca la ciencia como valor,la cual adquiere su verdadero sentido por sus fines cognoscitivos y prácticos, como funciones sociales esenciales y la multiplicidad de valores que les dan sentido como praxis científica. El trabajo contribuye a la educación CTS de los profesionales de cualquier especialidad y expresa la necesidad de la asimilación de sus factores valorativos y los problemas ético-morales de la ciencia que manifiestan en la práctica. El trabajo puede constituir una referencia teórica para la educación axiológica de los profesionales y a la crítica de las concepciones burguesas contemporánea de la Filosofía de la ciencia.

The complexity of ADAS (Advanced Driving Assistance Systems) and AD (Autonomous Driving) functions increased in the last years. As the effort required for conventional test methods has grown significantly, new test procedures on vehicle test benches have been developed. This article deals with Vehicle in the Loop testing on vehicle test benches for ADAS and AD functions. The focus is set on the vehicle body movements caused by the vehicle on the test bench. For this purpose, the different pitch motions for road and test bench are derived using a vehicle dynamics model. On this basis, a novel model approach is developed, which uses the Lagrange’s II. equation to determine the pitch motion of the vehicle on the test bench. The validity of the new model approach is confirmed by measurements and creates a generic basis to consider the influence of the vehicle's pitch motion while testing ADAS and AD functions on a vehicle test bench.

In articulated vehicles steering is accomplished by adjusting the articulation angle. Commonly a steering actuator is used to pivot the two vehicle sections. This actuator can be dispensed with if the pivoting is controlled by selective distribution of the drive torques to the individually driven wheels instead. Since steering is a safety-critical function, it must be ensured even if one of the drive motors fails. For this purpose, we propose a control method for a fault-tolerant four wheel independently driven articulated vehicle. The control method was developed and tested in a simulation environment and validated on a 1:5 scale demonstrator vehicle. The proposed method with constrained control allocation maintains the desired velocity and articulation angle by distributing the driving torques to the four wheels considering the current actuator limits. Both the simulation and the vehicle tests show that the control method meets the control objectives even when a sudden actuator limit occurs during critical driving scenarios like cornering. Thus, the vehicle can keep its maneuverability in the event of a detected failure of a drive motor. Together with reliable failure detection, the proposed approach provides a basis for further development towards innovative fault-tolerant electric articulated vehicles that meet the requirements of functional safety.

Abstract We investigate laccase-mediated detoxification of aflatoxins, fungal carcinogenic food contaminants. Our experimental comparison between two aflatoxins with similar structures (AFB1 and AFG2) shows significant differences in laccase-mediated detoxification. A multi-scale modeling approach (Docking, Molecular Dynamics, and Density Functional Theory) identifies the highly substrate-specific changes required to improve laccase detoxifying performance. We employ a large-scale density functional theory-based approach, involving more than 7000 atoms, to identify the amino acid residues that determine the affinity of laccase for aflatoxins. From this study we conclude: (1) AFB1 is more challenging to degrade, to the point of complete degradation stalling; (2) AFG2 is easier to degrade by laccase due to its lack of side products and favorable binding dynamics; and (3) ample opportunities to optimize laccase for aflatoxin degradation exist, especially via mutations leading to π–π stacking. This study identifies a way to optimize laccase for aflatoxin bioremediation and, more generally, contributes to the research efforts aimed at rational enzyme optimization.

Introduction Privatisation through the expansion of private payment and investor-owned corporate healthcare delivery in Canada raises potential conflicts with equity principles on which Medicare (Canadian public health insurance) is founded. Some cases of privatisation are widely recognised, while others are evolving and more hidden, and their extent differs across provinces and territories likely due in part to variability in policies governing private payment (out-of-pocket payments and private insurance) and delivery.Methods and analysis This pan-Canadian knowledge mobilisation project will collect, classify, analyse and interpret data about investor-owned privatisation of healthcare financing and delivery systems in Canada. Learnings from the project will be used to develop, test and refine a new conceptual framework that will describe public-private interfaces operating within Canada’s healthcare system. In Phase I, we will conduct an environmental scan to: (1) document core policies that underpin public-private interfaces; and (2) describe new or emerging forms of investor-owned privatisation (‘cases’). We will analyse data from the scan and use inductive content analysis with a pragmatic approach. In Phase II, we will convene a virtual policy workshop with subject matter experts to refine the findings from the environmental scan and, using an adapted James Lind Alliance Delphi process, prioritise health system sectors and/or services in need of in-depth research on the impacts of private financing and investor-owned delivery.Ethics and dissemination We have obtained approval from the research ethics boards at Simon Fraser University, University of British Columbia and University of Victoria through Research Ethics British Columbia (H23-00612). Participants will provide written informed consent. In addition to traditional academic publications, study results will be summarised in a policy report and a series of targeted policy briefs distributed to workshop participants and decision/policymaking organisations across Canada. The prioritised list of cases will form the basis for future research projects that will investigate the impacts of investor-owned privatisation.

A systematic simulation model is proposed in this research paper to estimate the energy consumption of electric vehicles. The main advantage of this model is that it is made in a generic and simplified way in order to be adaptable to different electric vehicles. The overall electrical power corresponding to the performed maneuver is estimated considering: a tabular form of electric motor efficiency, mechanical power losses, a generalized efficiency map of the power electronics, the auxiliary power losses, and an electro-thermal Lithium-Ion battery pack model. The battery model was developed in a previous work, which simulates the open circuit voltage curves at different temperatures and the alteration in the internal resistance of the battery cells. The proposed model is validated with experimental data from the maneuver tests. The battery model proved high accuracy in estimating the voltage values relevant to the WLTP2 driving cycle on the chassis roller test bench. Furthermore, the mechanical and electrical power were estimated with excellent matching compared to actual test field driving test measurements, giving only the measured vehicle speed and auxiliary power losses. Finally, the state of charge change is predicted accurately along the performed test field dynamic maneuver.

The regeneration of highly dynamic driving maneuvers on vehicle test benches is challenging due to several influences, such as power losses, vibrations in the overall system that involves the vehicle with the test bench, uncertainties in the model parameterization, and time delays from both the test bench and the measurement systems. In order to improve the dynamic response of the vehicle test bench and to overcome system disturbances, we employed different types of control algorithms for a mechanical multi-mass model. First, those controllers are extensively investigated in the frequency domain to analyze their stability and evaluate the noise rejection quality. Then, the expectations from the frequency analysis are confirmed in a time-domain simulation. Furthermore, sensitivity analysis tests were conducted to evaluate each controller’s robustness against the modeling parameters’ uncertainty. The linear quadratic controller with integral action demonstrated the best compromise between performance and robustness.

The objective of this study was to experimentally determine the mathematical correlations between the loading of the tire, being longitudinal and lateral forces, and the emission of particulate matter (PM) from the tire–road contact. Existing emission factors (EF, emission per vehicle and distance traveled) are the result of long-term measurements, which means that no conclusion can be drawn about the exact driving condition. To determine meaningful emission factors, extensive driving tests were conducted on an internal drum test bench while measuring PM emissions from the tire–road contact in real-time. This showed that the increases in emission over longitudinal and lateral forces can be approximated with fourth-order functions, with lateral forces leading to significantly higher emissions than longitudinal forces for the summer tire investigated. Using the emission functions obtained, a three-dimensional map was created that assigns an EF to each load condition consisting of different longitudinal and lateral forces for one vertical load. With known driving data, the map can be used for future simulation models to predict the total emission of real driving cycles. Furthermore, the results show that the average particle size increases with increasing horizontal force. The particles collected during the tests were analyzed to determine the proportions of tire and road material. According to the results, the tire contributes only about 20% of the particle mass, while 80% is attributable to the road surface. In terms of volume, these shares are 32% and 68%, respectively.

The energy consumption of the electric test vehicle on the roller dynamometer test bench is thoroughly investigated in this work. Initially, we extensively examined the relevant testing approaches on chassis dynamometer test benches. Then, we presented the main issues and limitations of this type of testing facility. After that, an experimental analysis was performed by recording the NEDC and the WLTP-class 2 standard driving cycle test data. It was found that the power consumption associated with specific test maneuvers could be accurately simulated via the roller dynamometer system. However, the experimental test results showed some disparities compared to the expected results from the simulation models. These findings supported identifying the dynamic behavior resulting from the tire and roller contact. The LuGre friction model provided the solution, incorporating friction-related physical models such as stick-slip and the Stribeck effect. In addition, adjustments were made to the LuGre model to consider the dynamic influences of the roller on the tire. Finally, we created a systematic estimation model for energy consumption by integrating the boundary conditions of the test bench, a generic model for energy estimation of electric vehicles, and the proposed tire–roller contact model. The overall model requires only the measured traction speed of the test vehicle and the auxiliary energy losses as physical inputs. The results proved that the proposed model enabled the estimation of the total power consumption with high accuracy.

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.

In order to improve the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. This article presents the extension of a generic prediction approach already proposed in a previous paper, which allows a robust forecasting of all traction torque-relevant variables for such strategies. The extension primarily includes the proper utilization of map data in the case of an a priori known route. Approaches from Artificial Intelligence (AI) have proven to be effective for such proposals. With regard to this, Recurrent Neural Networks (RNN) are to be preferred over Feed-Forward Neural Networks (FNN). First, preprocessing is described in detail including a wide overview of both calculating the relevant quantities from global navigation satellite system (GNSS) data in several steps and matching these with data from the chosen map provider. Next, an RNN including Long Short-Term Memory (LSTM) cells in an Encoder–Decoder configuration and a regular FNN are trained and applied. The models are used to forecast real driving profiles over different time horizons, both including and excluding map data in the model. Afterwards, a comparison is presented, including a quantitative and a qualitative analysis. The accuracy of the predictions is therefore assessed using Root Mean Square Error (RMSE) computations and analyses in the time domain. The results show a significant improvement in velocity prediction with LSTMs including map data.

En el libro de texto básico se reconoce el doble carácter en su condición de fuente del conocimiento histórico y de medio de enseñanza para su empleo como componente fundamental en la clase. Desde esta perspectiva, persigue la búsqueda de las ideas esenciales, el establecimiento de las relaciones conceptuales, causales y espacio-temporales y el desarrollo de las habilidades comunicativas. Favorece, además, la independencia cognoscitiva y los modos de actuación profesional integral, con un enfoque dialéctico – materialista, y desde una perspectiva histórico–cultural.

Shorter development times, increased standards for vehicle emissions and a greater number of vehicle variants result in a higher level of complexity in the vehicle development process. Efficient development of powertrain and driver assistance functions under comparable and reproducible operating conditions is possible on vehicle test benches. Yet, the realistic simulation of real driving environments on test benches is a challenge. Current test procedures and new technologies, such as Real Driving Emission tests and Autonomous Driving, require a reproducible and even more detailed simulation of the driving environment. Due to this, the simulation of curve driving in particular is gaining in importance. This results from its significant influence on energy consumption and Autonomous Driving functions with lateral guidance, such as lane departure and evasion assistance. Reproducibility can be additionally increased by using a driving robot. At today’s vehicle test benches, pedal and shift robots are predominantly used for longitudinal dynamic tests in the performed test procedures. In order to meet these new test automation requirements for vehicle test benches, the cooperative operation of pedal and steering robots is needed on a test bench setup suitable for this purpose. In this publication, the authors present the setup of a vehicle test bench to be used in automated and reproducible vehicle-in-the-loop tests during steering events. The focus is on the test-bench-specific setup with steerable front wheels, the actuators for simulating the wheel steering torque around the steering axle and the robots used for pedals and steering wheel. Results from various test series are presented and the potential of the novel test environment is shown. The results are reproducible in various test series due to the closed-loop operation without human driving influences at the test bench.

Various energy management systems (driving strategies) have been developed to improve the efficiency of electrified vehicle drives. These include strategies from the field of offline optimization to determine the theoretical optimum for a given system, as well as online strategies designed for an on-board application in the vehicle. In this paper, investigations are performed on an SUV electrified by a 48 V hybrid system in P14 topology regarding both offline and online strategies. To calculate the global optimum, the performance of Dynamic Programming (DP) compared to an Equivalent Consumption Minimization Strategy (ECMS) with an iteratively determined equivalence factor is shown. Furthermore, with regard to online energy management strategies (EMS), it is presented how a predictive Online ECMS achieves additional fuel savings compared to a robust, non-predictive implementation. The simulation-based vehicle development allows detailed investigations regarding interactions between battery requirements and EMS. In this context, it is shown how various battery capacities are exploited by the discussed EMS.

In order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. The subsequent direct comparison of the two approaches is performed based on the described methodology, which includes both quantitative and qualitative analyses. Hereby, the quality of the predictions is evaluated using Root Mean Squared Error (RMSE) calculations as well as analyses in time domain. Based on the presented results, an appropriate approach is finally recommended.

One of the major challenges in cancer research is finding models that closely resemble tumors within patients. Human tissue slice cultures are a promising approach to provide a model of the patient's tumor biology ex vivo. Recently, it was shown that these slices can be successfully analyzed by whole transcriptome sequencing as well as automated histochemistry, increasing their usability as preclinical model. Glioblastoma multiforme (GBM) is a highly malignant brain tumor with poor prognosis and little is known about its genetic background and heterogeneity regarding therapy success. In this study, tissue from the tumors of 25 patients with primary GBM was processed into slice cultures and treated with standard therapy (irradiation and temozolomide). Total RNA sequencing and automated histochemistry were performed to enable analysis of treatment effects at a transcriptional and histological level. Slice cultures from long‐term survivors (overall survival [OS] > 24 months) exhibited more apoptosis than cultures from patients with shorter OS. Proliferation within these slices was slightly increased in contrast to other groups, but not significantly. Among all samples, 58 protein‐coding genes were upregulated and 32 downregulated in treated vs. untreated slice cultures. In general, an upregulation of DNA damage‐related and cell cycle checkpoint genes as well as enrichment of genotoxicity pathways and p53‐dependent signaling was found after treatment. Overall, the current study reproduces knowledge from former studies regarding the feasibility of transcriptomic analyses and automated histology in tissue slice cultures. We further demonstrate that the experimental data merge with the clinical follow‐up of the patients, which improves the applicability of our model system.

Vehicle drive systems are often oversized for common customer operation in order to cover the high demands of rare driving events such as towing a trailer, high acceleration or steep inclines. This high torque and power requirement affects the efficiency map and the highest efficiency is around the area of increased torque and speed. However, in everyday use, drive systems are mostly driven by customers at low speed and load, and therefore are not operating in the most efficient area. Designing a drive system that only covers the area of highest customer operation can increase efficiency by moving the sweet spot of efficiency to the relevant area, and thus reduce energy consumption. Therefore, customer data need to be analyzed in order to identify customer requirements and to localize the area of greatest operation. The method presented in this paper analyzes customer data in order to identify design-relevant parameters for a customer-specific drive system design. The available customer data results from event-based counts and are submitted as a statistical frequency distribution. These statistics are compared with discrete time series recorded during test drives in order to derive representative time series that correspond to customer behavior. By applying the time frame-based load analysis to these relevant time series, the desired design-relevant parameters are pointed out.

The energy consumption caused by battery thermal management of electric vehicles can be reduced using predictive control. A predictive controller needs a prediction model of the battery temperature, for example for different battery cooling and heating thresholds. In the proposed method, cross-domain data from simulation, vehicle fleet and weather stations were analyzed and processed as training data for a Convolutional Neural Network (CNN). The CNN took data from previous road segments and predictions for following road segments as input and predicted the change in battery temperature as quantile sequences over a prediction horizon. Properties of the collected cross-domain data sets were analyzed and considered during preprocessing, before 150 models were trained, of which the best performing model was further analyzed. Point-forecast metrics and quantile-related metrics were used for model comparison and evaluation. For example, the median prediction achieved a mean absolute error (MAE) of 0.27 °C and the true values were below the median prediction in 47% of the test data. Possible improvements of the method such as increasing data size, using more complex architectures as well as optimizing the horizon sizes were discussed. In conclusion, the method was able to well predict battery temperatures for different battery cooling thresholds.

This study aimed to evaluate the pharmacological trends in patients with COVID-19 from a hospital in Peru during the first wave of the pandemic. Retrospective study conducted between April and September 2020. The medical records of patients hospitalized for COVID-19 pneumonia at the Dos de Mayo National Hospital (Lima, Peru) were reviewed. Of a total of 3103 patients, 381 medical records were included (77.4% male, median age: 44 years). The use of four prehospital drugs increased (azithromycin, ivermectin, corticosteroids, and ceftriaxone), while the in-hospital use of six drugs (ceftriaxone, azithromycin, hydroxychloroquine, ivermectin, corticosteroid pulse, and prophylactic anticoagulation) decreased and in-hospital anticoagulation use decreased. These findings suggest that the management of COVID-19 has varied during the first wave of the pandemic, typically increasing prehospital drug use and decreasing inpatient use.