Filtyp
Purpose – This study aims to enable robotic friction stir welding (FSW) in practice. The use of robots has hitherto been limited, because of the large contact forces necessary for FSW. These forces are detrimental for the position accuracy of the robot. In this context, it is not sufficient to rely on the robot’s internal sensors for positioning. This paper describes and evaluates a new method for
Purpose: Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa. Methods: A total of 168 patients from three centers were divided into training, validation, and test groups. Manual an
Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through
Agricultural soils are the most important anthropogenic source of nitrous oxide (N2O) emissions. This occurs via two main pathways: (1) from microbial-mediated oxidation of ammonium to nitrite and nitrate; and (2) denitrification. Most agro-ecological models explicitly deal with these two pathways albeit with different degrees of process understanding and empiricism. Models that integrate the impa
Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next-generation CO2-observing satellites, providing both an opportunity and a challenge for inversions. This study i
Machine learning, a sub-field of artificial intelligence, is the study of computer algorithms that improve automatically through experience. Although the term was coined in 1959, machine learning builds on questions/methods that were developed earlier in linear algebra, mathematical analysis, optimization, and mathematical statistics. In this chapter we will give a brief overview of machine learni
One of the biggest challenges in urban densifications is securing adequate daylight access. This study examines the potential of using semantic 3D city models as input to daylight simulations. It is focusing on investigating input data requirements to these simulations from a geodata, 3D city model specification and measuring guideline perspective. To achieve this, geodata simulation input require
The double quantum coherence (DQC) signal in nonlinear spectroscopy gives information about the many-body correlation effects not easily available by other methods. The signal is short-lived, consequently, a significant part of it is generated during the pulse overlap. Since the signal is at two times the laser frequency, one may intuitively expect that the pulse overlap-related artifacts are filt
BACKGROUND: Most cerebral palsy (CP) cases have an unexplained etiology, but a role for environmental exposures has been suggested. One purported environmental risk factor is exposure to endocrine-disrupting pollutants specifically per- and polyfluoroalkyl substances (PFAS).OBJECTIVES: We investigated the association between prenatal PFAS exposures and CP in Swedish children.METHODS: In this case-
BACKGROUND: Retrospective studies have shown promising results using artificial intelligence (AI) to improve mammography screening accuracy and reduce screen-reading workload; however, to our knowledge, a randomised trial has not yet been conducted. We aimed to assess the clinical safety of an AI-supported screen-reading protocol compared with standard screen reading by radiologists following mamm
Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertai
Baltic Earth is an independent research network of scientists from all Baltic Sea countries that promotes regional Earth system research. Within the framework of this network, the Baltic Earth Assessment Reports (BEARs) were produced in the period 2019-2022. These are a collection of 10 review articles summarising current knowledge on the environmental and climatic state of the Earth system in the
Markers of downstream events are a key component of clinical trials of disease-modifying therapies for Alzheimer's disease. Morphological metrics like cortical thickness are established measures of atrophy but are not sensitive enough to detect amyloid-beta (Aβ)- related changes that occur before overt atrophy become visible. We aimed to investigate to what extent diffusion MRI can provide sensiti
Aim: Identifying how climate change, habitat loss, and corridors interact to influence species survival or extinction is critical to understanding macro-scale biodiversity dynamics under changing environments. In North America, the ice-free corridor was the only major pathway for northward migration by megafaunal species during the last deglaciation. However, the timing and interplay among the lat
Since 1750, land-use change and fossil fuel combustion has led to a 46% increase in the atmospheric carbon dioxide (CO2) concentrations, causing global warming with substantial societal consequences. The Paris Agreement aims to limit global temperature increases to well below 2C above preindustrial levels. Increasing levels of CO2 and other greenhouse gases (GHGs), such as methane (CH4) and nitrou
The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of
Biological nitrogen fixation (BNF) from grain legumes is of significant importance in global agricultural ecosystems. Crops with BNF capability are expected to support the need to increase food production while reducing nitrogen (N) fertilizer input for agricultural sustainability, but quantification of N fixing rates and BNF crop yields remains inadequate on a global scale. Here we incorporate tw
Enhanced warming in the Arctic (Arctic amplification, AA) in the last decades has been linked to several factors including sea ice and the Atlantic Multidecadal Oscillation (AMO). However, how these factors contributed to AA variations in a long-term perspective remains unclear. By reconstructing a millennial AA index combining climate model simulations with recently available proxy data, this wor
The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient constraints at large scales, whereby satellite data offer great potential for reducing the uncertainty. Here, we present two carbon sink estimates for China constrained either by satellite CO2 column concentrations (XCO2) within the Global Carbon Assimilation System or by remotely sensed soil moi
Purpose: The aim of this study was to develop and validate an artificial intelligence (AI)-based method using convolutional neural networks (CNNs) for the detection of pelvic lymph node metastases in scans obtained using [18F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) from patients with high-risk prostate cancer. The second goal was to make the AI-based method available to
