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Background The Norwegian Knee Ligament Register was founded in 2004 to provide representative and reliable data on cruciate ligament surgery. The aim of this study was to evaluate the validity of key variables in the Norwegian Knee Ligament Register to reveal and prevent systematic errors or incompleteness, which can lead to biased reports and study conclusions. Method We included a stratified cluster sample of 83 patients that had undergone both primary and revision anterior cruciate ligament surgery. A total of 166 medical records were reviewed and compared with their corresponding data in the database of the Norwegian Knee Ligament Register. We assessed the validity of a selection of key variables using medical records as a reference standard to compute the positive predictive values of the register data for the variables. Results The positive predictive values for the variables of primary and revision surgery ranged from 92 to 100% and from 39 to 100% with a mean positive predictive value of 99% and 88% respectively. Data on intraoperative findings and surgical details had high positive predictive values, ranging from 91 to 100% for both primary and revision surgery. The positive predictive value for the variable “date of injury” was 92% for primary surgeries but only 39% for revision surgeries. The positive predictive value for “activity at the time of injury” was 99% for primary surgeries and 52% for revisions. Conclusion Overall, the data quality of the key variables examined in the Norwegian Knee Ligament Register was high, making the register a valid source for research.

Avalanches present substantial hazard risk in mountainous regions, particularly when avalanches obstruct roads, either hitting vehicles directly or leaving traffic exposed to subsequent avalanches during cycles. Traditional detection methods often are designed to cover only a limited section of a road stretch, hampering effective risk management. This research introduces a novel approach using Distributed Acoustic Sensing (DAS) for avalanche detection. The monitoring site in Northern Norway is known to be frequently impacted by avalanches. Between 2022–2024, we continuously monitored the road for avalanches blocking the traffic. The automated alert system identifies avalanches affecting the road and estimates accumulated snow. The system provides continuous, real-time monitoring with competitive sensitivity and accuracy over large areas (up to 170 km) and for multiple sites on parallel. DAS powered alert system can work unaffected by visual barriers or adverse weather conditions. The system successfully identified 10 road-impacting avalanches (100% detection rate). Our results via DAS align with the previous works and indicate that low frequency part of the signal (<20 Hz) is crucial for detection and size estimation of avalanche events. Alternative fiber installation methods are evaluated for optimal sensitivity to avalanches. Consequently, this study demonstrates its durability and lower maintenance requirements, especially when compared to the high setup costs and coverage limitations of radar systems, or the weather and lighting vulnerabilities of cameras. Furthermore the system can detect vehicles on the road as important supplemental information for search and rescue operations, and thus the authorities can be alerted, thereby playing a vital role in urgent rescue efforts.

Three-dimensional city models are increasingly being used for analyses and simulations. To enable such applications, it is necessary to standardise semantically richer city models and, in some cases, to connect the models with external data sources. In this study, we describe the development of a new Swedish specification for 3D city models, denoted as 3CIM, which is a joint effort between the three largest cities in Sweden—Stockholm, Gothenburg, and Malmö. Technically, 3CIM is an extension of the OGC standard CityGML 2.0, implemented as an application domain extension (ADE). The ADE is semantically thin, mainly extending CityGML 2.0 to harmonise with national standards; in contrast, 3CIM is mainly based on linkages to external databases, registers, and operational systems for the semantic part. The current version, 3CIM 1.0, includes various themes, including Bridge, Building, Utility, City Furniture, Transportation, Tunnel, Vegetation, and Water. Three test areas were created with 3CIM data, one in each city. These data were evaluated in several use-cases, including visualisation as well as daylight, noise, and flooding simulations. The conclusion from these use-cases is that the 3CIM data, together with the linked external data sources, allow for the inclusion of the necessary information for the visualisation and simulations, but extract, transform, and load (ETL) processes are required to tailor the input data. The next step is to implement 3CIM within the three cities, which will entail several challenges, as discussed at the end of the paper.

The performance of a new type of X-band weather radar (WR) for Sweden during a pilot run is studied. Compared to the conventional C-band WRs, the X-band WR covers a smaller area but with a higher spatiotemporal resolution, making it suitable for urban hydrological applications. Rainfall estimations from different elevation angles of the radar (levels) are compared at one-minute and single-event timescales with the observations of several rain gauges at different ranges using hyetographs. In general, the estimations aligned well with observations and the best match appeared for ranges as long as 5–10 km. Seemingly, radar estimations suffered from overshooting of lower lying showers by higher level scans in longer ranges (19–30 km) and from the reflectivity contamination due to moving objects in short ranges (<1 km). Also, the effective range of the radar dropped sharply for the moments when a cloudburst was located over the radar. Although various sources of error could affect the X-band WR rainfall estimates, higher resolution spatiotemporal rainfall monitoring for wider areas will benefit from an integration of data from a network of X-band WRs.

Climate change projections show that temperature and precipitation increases can alter the exchange of greenhouse gases between the atmosphere and high latitude landscapes, including their freshwaters. Dissolved organic carbon (DOC) plays an important role in greenhouse gas emissions, but the impact of catchment productivity on DOC release to subarctic waters remains poorly known, especially at regional scales. We test the hypothesis that increased terrestrial productivity, as indicated by the normalized difference vegetation index (NDVI), generates higher stream DOC concentrations in the Stordalen catchment in subarctic Sweden. Furthermore, we aimed to determine the degree to which other generic catchment properties (elevation, slope) explain DOC concentration, and whether or not land cover variables representing the local vegetation type (e.g., mire, forest) need to be included to obtain adequate predictive models for DOC delivered into rivers. We show that the land cover type, especially the proportion of mire, played a dominant role in the catchment's release of DOC, while NDVI, slope, and elevation were supporting predictor variables. The NDVI as a single predictor showed weak and inconsistent relationships to DOC concentrations in recipient waters, yet NDVI was a significant positive regulator of DOC in multiple regression models that included land cover variables. Our study illustrates that vegetation type exerts primary control in DOC regulation in Stordalen, while productivity (NDVI) is of secondary importance. Thus, predictive multiple linear regression models for DOC can be utilized combining these different types of explanatory variables.

The introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum) for improved feature matching of binary point-features. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.

BackgroundEarly orchidopexy is recommended for cryptorchidism and the surgery is increasingly centralised. The objectives were to determine the incidence, risk factors and if distance to treating hospital impacted on timely treatment of cryptorchidism.MethodsIn this observational study, all boys born in Sweden from 2001 to 2014 were followed in national registers to determine the incidence of cryptorchidism by levels of birth-related risk factors and social determinants. Travel time to hospital was used as the primary exposure in multivariable survival analysis, with age at surgery as main outcome.ResultsOf 748 678 boys at risk for cryptorchidism, 7351 were treated and evaluated for timing of surgery (cumulative childhood incidence 1.4%, 95% CI 1.3% to 1.5%). The incidence was clearly associated with prematurity and overdue pregnancy (HR for <32 weeks 2.77 (95% CI 2.39 to 3.21); 32–36 weeks HR 1.36 (95% CI 1.24 to 1.49); >41 weeks HR 1.19 (95% CI 1.10 to 1.29)), low birth weight (<1000 g HR 3.94 (95% CI 3.15 to 4.92); 1000–1499 g HR 3.70 (95% CI 3.07 to 4.46); 1500–2500 g HR 1.69 (95% CI 1.52 to 1.88)) and intrauterine growth restriction (small for gestational age HR 2.38 (95% CI 2.14 to 2.65); large for gestational age HR 1.26 (95% CI 1.13 to 1.42)), but not with smoking or maternal age. Each 30 min increase in travel time was associated with a reduced probability of timely treatment (HR for being treated by age 3 adjusted for risk factors and socioeconomic determinants: 0.91 (95% CI 0.88 to 0.95)). Lower income and financial support were also associated with treatment delays (adjusted HR for lowest income quintile 0.82 (95% CI 0.72 to 0.93) and for families with financial support 0.85 (95% CI 0.73 to 0.97)).ConclusionsTravel distance to treating hospital was associated with delayed treatment. ‘Not all those who wander are lost’, but these findings suggest a trade-off between centralisation benefits and barriers of geography also in elective paediatric surgery.

Digital elevation models (DEMs) can be captured and analysed by various methods. Elevation capturing with RTK-GPS and airborne laser scanning is presented and evaluated in terms of height accuracy of raw data and interpolated DEMs for study sites in Sweden and Germany. Applications for precision agriculture are based on the connection of land surface and the movement of water in the landscape. Three methods of deriving potential flow accumulation from DEMs are discussed. Results indicate that the Topographic Wetness Index can be used to assess the pattern of potential soil moisture on a field and changes in soil texture caused by erosion processes. The quality of the soil moisture assessment varies with both flow algorithm and terrain characteristics. Correlations up to r2 = 0.64 were found. Best results were obtained on undulating farmland using the DEMON algorithm and a formbased approach. However, in areas with low relief, the concept did not lead to valuable soil moisture maps.

Purpose - The purpose of this paper is to investigate how firms attempt to increase the profitability of specific groups of extant customers by achieving adjustments in customer behaviour that consequently lead to reduced costs associated with serving these customers.Design methodology approach - The paper takes the form of multiple case studies in the retail banking sector. Three specific initiatives launched by European retail banks for the stated (partial) purpose of modifying customer behaviour in order to reduce customer-related costs are evaluated.Findings - The findings from the three case studies show that strategies that aim to modify customer behaviour in a positive way can successfully decrease the costs of interacting with customers while at the same time maintaining and even increasing customer retention and customer-related revenues.Research limitations implications - This study is limited to an assessment of three initiatives carried out by three European banks. The findings bring the issue of costs into customer relationship management in a constructive manner, abandoning the view of cost reductions as a necessary evil or drastic measure to handle problematic customers.Practical implications - Marketing and customer relationship managers who consider strategies to change customer behaviour in a cost-reducing way as a complement to traditional revenue and loyalty enhancement strategies will expand their opportunities to achieve increases in customer lifetime value (CLV) and customer equity (CE).Originality value - The paper reclaims the management of costs as a key customer management activity, thereby answering calls for more attention to cost issues in marketing.

Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May–September, 2006–2010). Image derived estimates of water and peat moisture were sensitive (R2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes.