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We employ data that match the population of Danish workers to the universe of private-sector Danish firms, with product-level trade flows by origin-and destination-countries. We document new stylized facts about offshoring and instrument for offshoring and exporting. Within job spells, offshoring increases (decreases) the high-skilled (low-skilled) wage; exporting increases the wages of all skill-types; the net wage-effect of trade varies substantially within the same skill-type; conditional on skill, the wage-effect of offshoring varies across task characteristics. We estimate the overall effects of offshoring on workers' present and future income streams by constructing pre-offshoring-shock worker-cohorts and tracking them over time.

Purpose This paper aims to study a circular economy business model that offers services with embedded information exchange capabilities to extend product life through maintenance and repair. Information exchange has been identified as a critical factor in advancing the principles of a circular economy, and this research was conducted to illustrate how information exchange can facilitate maintenance and repair. Design/methodology/approach The study has a case study approach of collecting data through semi-structured interviews and questionnaires. Findings Information exchange on what and when to do something engages end-users in maintenance and facilitates learning. For repair, the problem description and possible solutions are information that must be exchanged. Both types of information exchange are facilitated by simple tech solutions relying on known and inexpensive technology (e.g. e-mail service, video call and text messaging). Research limitations/implications The study contributes to the organisational development and knowledge management fields with novel insights on how information exchange and circular economy are related and can be facilitated. Practical implications The study provides insights for companies looking for solutions on how to generate revenue from services and reduce resource consumption. The findings of the study suggest that the development of circular business models does not always require expensive high-tech solutions. Originality/value To the best of the authors’ knowledge, this study is unique as it is empirically based on insights into how information exchange can extend product life through the use of simple digital tools.

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

Background Placing a peripheral vein catheter can be challenging due to several factors, but using ultrasound as guidance increases the success rate. The purpose of this review is to investigate the knowledge already existing within the field of education in ultrasound-guided peripheral vein catheter placement and explore the efficacy and clinical impact of different types of education. Methods In accordance with PRISMA-guidelines, a systematic search was performed using three databases (PubMed, EMBASE, CINAHL). Two reviewers screened titles and abstracts, subsequently full-text of the relevant articles. The risk of bias was assessed using the Cochrane Collaboration risk of bias assessment tool and the New Ottawa scale. Results Of 3409 identified publications, 64 were included. The studies were different in target learners, study design, assessment tools, and outcome measures, which made direct comparison difficult. The studies addressed a possible effect of mastery learning and found e-learning and didactic classroom teaching to be equally effective. Conclusion Current studies suggest a potential benefit of ultrasound guided USG-PVC training on success rate, procedure time, cannulation attempts, and reducing the need for subsequent CVC or PICC in adult patients. An assessment tool with proven validity of evidence to ensure competence exists and education strategies like mastery learning, e-learning, and the usage of color Doppler show promising results, but an evidence-based USG-PVC-placement training program using these strategies combined is still warranted.

Purpose Enterprise social media (ESM) platforms are rapidly diffusing in the business context because they can bring substantial benefits to companies by enhancing their knowledge management (KM) processes. However, such benefits materialize only if active employee participation is ensured. Therefore, it is crucial to understand how individual employees use an ESM platform to assist their knowledge-related activities. This paper contributes to this topic by proposing a classification of ESM users based on two dimensions: frequency and type (active or passive) of use. Design/methodology/approach The paper presents the results of a survey of 262 employees of an international engineering service company that has adopted an ESM platform to support its KM processes. Statistical methods (e.g. ANOVA, Tukey’s b) were applied to verify the usefulness of the proposed typology and identify the main aspects that characterize the different user groups. Findings The survey results confirm the existence of different types of ESM users and provide the empirical basis for developing a bi-dimensional classification from which four user groups were derived and characterized: frequent contributors, sporadic contributors, frequent lurkers and sporadic lurkers. Research limitations/implications The main limitation is that only one company in one sector with specific knowledge needs and capabilities was investigated. Practical implications The study provides useful suggestions for how to promote the use of an ESM and particularly for how to encourage less frequent and less active users to increase their participation in a platform. Originality/value The paper contributes to a better understanding of how employees approach ESM by identifying factors that characterize different user groups.

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

Purpose This paper aims to study the impact of intentionally developed communities of practice (CoPs) on knowledge sharing and practice improvement in an administrative public sector organisation (PSO). Design/methodology/approach A case study approach was used to analyse the impact of the CoPs intentionally developed by four different teams at a Danish PSO. The study applied a CoP development framework suggested by the literature to develop the CoPs. Findings Three out of the four CoPs were successfully developed, and they positively affected knowledge sharing and practice improvement. CoP participants engaged in conversations to explore individual ways of working, share knowledge and ultimately improve practice. Standardisation and boundary spanning were identified as contextual factors influencing the CoP activities. Research limitations/implications The findings verify the framework and contribute to a better understanding of the factors affecting the development of CoPs that positively impact knowledge sharing and practice improvements in a PSO context. Practical implications The study provides operations managers in PSOs with a framework for developing CoPs to improve work performance through better knowledge sharing among employees. Originality/value The paper provides case study evidence for the relevance of CoPs in PSO settings and highlights the necessity of investing resources in employee knowledge-sharing interactions.

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3–10 m and an accuracy of 75.2% for an altitude range of 10–20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3–10 of meters and 77.7% in an altitude range of 10–20 m

In rodent models of type 2 diabetes (T2D), sustained remission of hyperglycemia can be induced by a single intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1), and the mediobasal hypothalamus (MBH) was recently implicated as the brain area responsible for this effect. To better understand the cellular response to FGF1 in the MBH, we sequenced >79,000 single-cell transcriptomes from the hypothalamus of diabetic Lep ob/ob mice obtained on Days 1 and 5 after icv injection of either FGF1 or vehicle. A wide range of transcriptional responses to FGF1 was observed across diverse hypothalamic cell types, with glial cell types responding much more robustly than neurons at both time points. Tanycytes and ependymal cells were the most FGF1-responsive cell type at Day 1, but astrocytes and oligodendrocyte lineage cells subsequently became more responsive. Based on histochemical and ultrastructural evidence of enhanced cell-cell interactions between astrocytes and Agrp neurons (key components of the melanocortin system), we performed a series of studies showing that intact melanocortin signaling is required for the sustained antidiabetic action of FGF1. These data collectively suggest that hypothalamic glial cells are leading targets for the effects of FGF1 and that sustained diabetes remission is dependent on intact melanocortin signaling. In rodent models of type 2 diabetes, sustained remission of hyperglycemia can be induced by FGF1 action in the mediobasal hypothalamus. Here, the authors show that FGF1-injection is followed by marked changes in glial cell populations and that the sustained glycemic response is dependent on intact melanocortin signaling.

Rapid climate warming has been linked to increasing shrub dominance in the Arctic tundra. Research now shows that climate–shrub growth relationships vary spatially and according to site characteristics such as soil moisture and shrub height. Rapid climate warming in the tundra biome has been linked to increasing shrub dominance 1 , 2 , 3 , 4 . Shrub expansion can modify climate by altering surface albedo, energy and water balance, and permafrost 2 , 5 , 6 , 7 , 8 , yet the drivers of shrub growth remain poorly understood. Dendroecological data consisting of multi-decadal time series of annual shrub growth provide an underused resource to explore climate–growth relationships. Here, we analyse circumpolar data from 37 Arctic and alpine sites in 9 countries, including 25 species, and ∼42,000 annual growth records from 1,821 individuals. Our analyses demonstrate that the sensitivity of shrub growth to climate was: (1) heterogeneous, with European sites showing greater summer temperature sensitivity than North American sites, and (2) higher at sites with greater soil moisture and for taller shrubs (for example, alders and willows) growing at their northern or upper elevational range edges. Across latitude, climate sensitivity of growth was greatest at the boundary between the Low and High Arctic, where permafrost is thawing 4 and most of the global permafrost soil carbon pool is stored 9 . The observed variation in climate–shrub growth relationships should be incorporated into Earth system models to improve future projections of climate change impacts across the tundra biome.