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Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain af

A new genus in the diverse Andean Pedaliodes complex uncovered using target enrichment (Lepidoptera, Nymphalidae)

A new genus of Neotropical Satyrinae butterflies, Viloriodes Pyrcz & Espeland gen. n. is described in the Pedaliodes Butler complex comprising 11–13 genera and more than 400 species. Support for the new genus is provided by a phylogenetic analysis based on target enrichment (TE) data including 618 nuclear loci with a total of 248,940 nucleotides, and the mitochondrial gene cytochrome oxidase s

Characterizing Uncertainty in the Visual Text Analysis Pipeline

Current visual text analysis approaches rely on sophisticated processing pipelines. Each step of such a pipeline potentially amplifies any uncertainties from the previous step. To ensure the comprehensibil- ity and interoperability of the results, it is of paramount importance to clearly communicate the uncertainty not only of the output but also within the pipeline. In this paper, we characterize

Sparse optimization for nonlinear group delay mode estimation

Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes

Association of PET index quantifying skeletal uptake in NaF PET/CT images with overall survival in prostate cancer patients

Background: Bone Scan Index (BSI) derived from 2D whole-body bone scans is considered an imaging biomarker of bone metastases burden carrying prognostic information. Sodium fluoride (NaF) PET/CT is more sensitive than bone scan in detecting bone changes due to metastases. We aimed to develop a semi-quantitative PET index similar to the BSI for NaF PET/CT imaging and to study its relationship to BS

ECG Markers of Acute Melatonin Treatment in a Porcine Model of Acute Myocardial Ischemia

In myocardial ischemia, melatonin confers antiarrhythmic action, but its electrocardiographic expression is unclear. We aimed to evaluate the effects of melatonin treatment on electrocardiogram (ECG) parameters reflecting major arrhythmogenic factors and to test the association of these parameters with ventricular fibrillation (VF) incidence. Myocardial ischemia was induced by 40 min coronary arte

Molecular and Morphological Characteristics of the De-Obstructed Rat Urinary Bladder—An Update

Many patients with outlet obstruction secondary to prostatic enlargement have lower urinary tract symptoms (LUTSs) and an increased frequency of micturition. The standard treatment is transurethral resection of the prostate (TURP), which alleviates obstruction and symptoms. However, after TURP, 20–40 percent of patients continue to experience LUTSs. The aim of the present study in rats was to iden

Acceptance of COVID-19 Vaccine Booster Doses Using the Health Belief Model : A Cross-Sectional Study in Low-Middle- and High-Income Countries of the East Mediterranean Region

Coronavirus disease (COVID-19) booster doses decrease infection transmission and disease severity. This study aimed to assess the acceptance of COVID-19 vaccine booster doses in low, middle, and high-income countries of the East Mediterranean Region (EMR) and its determinants using the health belief model (HBM). In addition, we aimed to identify the causes of booster dose rejection and the main so

Automated evaluation of normal uptake in different skeletal parts in 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method

Introduction: Understanding normal skeletal uptake of 18F-sodium fluoride (18F-NaF) in positron emission tomography/computed tomography (PET/CT) is important for clinical interpretation. Quantification of tracer uptake in PET/CT is often performed by placing a volume of interest (VOI) to measure standard uptake values (SUVs). Manual placement of this VOI requires a subjective decision and can only

Barnens tid och framtid : En studie av den svenska asylbyråkratin efter 2015

Syftet med denna studie är att analysera asylbyråkratins tidssyn, tidsanvändning och tidsstyrning samt peka på konsekvenserna av detta för skyddssökande familjer och rätten till asyl: Hur används tid och vilka perspektiv på tid tonar fram i asylprövningen på Migrationsverket, den myndighet som genomför svensk asylpolitik? Hur fördelas makten över tiden, mellan myndigheter och domstolar som fattar Syftet med denna studie är att analysera asylbyråkratins tidssyn, tidsanvändning och tidsstyrning samt peka på konsekvenserna av detta för skyddssökande familjer och rätten till asyl: Hur används tid och vilka perspektiv på tid tonar fram i asylprövningen på Migrationsverket, den myndighet som genomför svensk asylpolitik? Hur fördelas makten över tiden, mellan myndigheter och domstolar som fattar

A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the t

Motion Prediction Based on Multiple Futures for Dynamic Obstacle Avoidance of Mobile Robots

The ability to decide and adjust actions according to motion prediction of dynamic obstacles offers a flexible planning scheme and ampler reaction time to avoid potential impact. Prediction-based collision avoidance implies a two-stage decision-making process from motion prediction to action planning. One of the challenges in motion prediction is the movements of objects are usually non-determinis

Modelling and Learning Dynamics for Robotic Food-Cutting

Interaction dynamics are difficult to model analytically, making data-driven controllers preferable for contact-rich manipulation tasks. In this work, we approximate the intricate dynamics of food-cutting with a Long Short-Term Memory (LSTM) model to apply a Model Predictive Controller (MPC). We propose a problem formulation that allows velocity-controlled robots to learn the interaction dynamics

Monte Carlo Filtering Objectives

Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models. It can be addressed by surrogate objectives for optimization. We propose Monte Carlo filtering objectives (MCFOs), a family of variational objectives for jointly learning parametric generative models and amortized adapt

Jakt på papperslösa gör oss till en polisstat

Regeringen föreslår nio åtgärder för att hitta och utvisa papperslösa. Det kommer att slå hårt och främst gå ut över redan svaga och jagade människor. Vi uppmanar därför regeringen att ta tillbaka åtgärderna, skriver 43 forskare.

Human-robot collaborative object transfer using human motion prediction based on Cartesian pose Dynamic Movement Primitives

In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and

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Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need fo