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To predict the final geometry in thermo-mechanical processes, the use of modeling tools is of great importance. One important part of the modeling process is to describe the response correctly. A previously published mechanism-based flow stress model has been further developed and adapted for the nickel-based superalloys, alloy 625, and alloy 718. The updates include the implementation of a solid
With electrification of vehicles comes an increasing demand on low noise, which for gears translates to transmission error. In this paper, static transmission error, STE, is found for a large number of manufacturing error combinations belonging to different tolerance classes, with focus on those typically used in the automotive industry. There is a lack of studies found in the scientific literatur
This paper proposes distributed event-triggered schemes for achieving state consensus for multi-agent linear systems. For each agent modeled by a linear control system in Rn, a positive signal is embedded in its event function, with the aim of guaranteeing an asymptotic convergence to state consensus for networked linear systems interacted in an undirected and connected graph, and with Zeno trigge
We report thermometry and new Raman linewidth data using a novel method for time-domain rotational coherent anti-Stokes Raman spectroscopy (RCARS) on single-shot basis. The coherences are generated by two fs pulses and probed by a ns pulse. The resulting signal is detected using a combined spectrograph/streak camera setup with high spectral and temporal resolution. Rotational CARS spectrograms of
The normal approach to digital control is to sample periodically in time. Using an analog of integration theory we can call this Riemann sampling. Lebesgue sampling or event based sampling, is an alternative to Riemann sampling. It means that signals are sampled only when measurements pass certain limits. In this paper it is shown that Lebesgue sampling gives better performance for some simple sys
Objective:The relationship between fatigue and cognition has not been fully elucidated in children and adolescent survivors of brain tumours. The aim of the present study was to investigate the potential relationship between fatigue and cognitive impairments in these survivors, as this group is at risk for both types of deficits.Methods:Survivors of paediatric brain tumours (n = 45) underwent a ne
Simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI) offer the potential to combine anatomic information from DBT with functional information from MI. This makes it possible to associate tissue stiffness with specific anatomic structures in the breast, a combination that can reduce false-positive findings by using the MI data to discriminate between ambiguous lesions in DBT.
Inferring the cell-type composition of bulk samples can provide biological insight. While bulk transcriptomics data has been extensively used for this purpose, the use of proteomics data has remained unexplored until recently. This study evaluates computational approaches for estimating immune cell composition using bulk sample proteomics data. Leveraging defined immune cell populations and simula
A radiation detection system combining a single-sided silicon strip detector (SSSD) and a high-purity germanium (HPGe) COMPEX detector module together with its anti-Compton shield (ACS) was set up at Lund University for environmental sample measurements. This system enables high sensitivity ––-coincidence analysis, enhancing the identification level of radionuclides in environmental samples. A com
This ESMO CPG provides key recommendations and algorithms for managing DLBCL, FL, HL, MCL, MZL, PCNSL and PTCL. •It covers epidemiology, diagnosis, staging, risk assessment, treatment of primary and refractory disease and follow-up. •Treatment algorithms for each lymphoma type are provided. •The author group encompasses a multidisciplinary group of experts from different institutions and countries
Surface tension is crucial for nanowire vapor liquid solid (VLS) growth models and simulations of growth under external stimuli. A promising method involving imaging droplet deformation under an external electric field has previously been used to report the surface tension of the AuSi eutectic catalyst on Si-nanowires. However, simplified assumptions in this approach led to a significantly lower v
Context: AI technologies are increasingly embedded in products and software engineering processes of industrial IoT, autonomous systems, and cyber-physical systems. It is therefore essential to ensure alignment with safety, reliability, and ethical standards. However, practical software engineering methods for managing misalignment risks remain underdeveloped. Objective: This study aims to explore
Ensuring functional safety is crucial for the deployment of autonomous systems in real-life dynamic environments, as they must operate reliably and safely among humans. However, existing safety systems are designed with a closed-world assumption and can over-constrain the system by shutting down the robot at every safety violation, limiting the robot’s ability to complete its tasks. To address thi
Objective: The aim of this study was to explore experiences and challenges in handling feedback and criticism among early career surgeons. Design: This study is part of a prospective, exploratory, longitudinal study evaluating surgical residents throughout residency. Semi-structured interviews were conducted with medical doctors applying to a locum or residency position in a surgical specialty. An
The rapid growth of smart logistics, driven by IoT devices and data-intensive applications, necessitates secure, scalable, and efficient computing frameworks. As the edge-fog-cloud (EFC) paradigm supports real-time data processing, it faces significant security threats and attacks, including privacy risks, data breaches, and unauthorized access. To address these security threats and attacks, block
Improving lignocellulosic biorefineries is a crucial step in transitioning from a fossil-based economy to a bio-based one. These biorefineries convert lignocellulosic biomass feedstocks into value-added products such as biofuels, biopower, biochemicals, and biomaterials, while also recovering valuable components from underutilized process streams. Pulp and paper mills are lignocellulosic biorefine
The advent of AlphaFold and consumer large language models have elicited unprecedented development of artificial intelligence (AI). AI has had substantial impact in every area of research, including in molecular biology. This is principally in thanks to contributions to the Protein Data Bank and various genome sequence databases, providing an astronomical amount of data for model training. These d
