On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

Landscape trajectory of natural boreal forest loss as an impediment to green infrastructure

Loss of natural forests by forest clearcutting has been identified as a critical conservation challenge worldwide. This study addressed forest fragmentation and loss in the context of the establishment of a functional green infrastructure as a spatiotemporally connected landscape-scale network of habitats enhancing biodiversity, favorable conservation status, and ecosystem services. Through retrospective analysis of satellite images, we assessed a 50- to 60-year spatiotemporal clearcutting impact trajectory on natural and near-natural boreal forests across a sizable and representative region from the Gulf of Bothnia to the Scandinavian Mountain Range in northern Fennoscandia. This period broadly covers the whole forest clearcutting period; thus, our approach and results can be applied to comprehensive impact assessment of industrial forest management. The entire study region covers close to 46,000 km² of forest-dominated landscape in a late phase of transition from a natural or near-natural to a land-use modified state. We found a substantial loss of intact forest, in particular of large, contiguous areas, a spatial polarization of remaining forest on regional scale where the inland has been more severely affected than the mountain and coastal zones, and a pronounced impact on interior forest core areas. Salient results were a decrease in area of the largest intact forest patch from 225,853 to 68,714 ha in the mountain zone and from 257,715 to 38,668 ha in the foothills zone, a decrease from 75% to 38% intact forest in the inland zones, a decrease in largest patch core area (assessed by considering 100-m patch edge disturbance) from 6114 to 351 ha in the coastal zone, and a geographic imbalance in protected forest with an evident predominance in the mountain zone. These results demonstrate profound disturbance of configuration of the natural forest landscape and disrupted connectivity, which challenges the establishment of functional green infrastructure. Our approach supports the identification of forests for expanded protection and conservation-oriented forest landscape restoration.     

Correction to: Keeping pace with forestry: Multi-scale conservation in a changing production forest matrix

Ambio - In the original published article, the sentence “Nevertheless, semi-natural forest remnants continue to be harvested and fragmented (Svensson et al. 2018; Jonsson et al. 2019), and...     

The city as a refuge for insect pollinators

Research on urban insect pollinators is changing views on the biological value and ecological importance of cities. The abundance and diversity of native bee species in urban landscapes that are absent in nearby rural lands evidence the biological value and ecological importance of cities and have implications for biodiversity conservation. Lagging behind this revised image of the city are urban conservation programs that historically have invested in education and outreach rather than programs designed to achieve high‐priority species conservation results. We synthesized research on urban bee species diversity and abundance to determine how urban conservation could be repositioned to better align with new views on the ecological importance of urban landscapes. Due to insect pollinators’ relatively small functional requirements—habitat range, life cycle, and nesting behavior—relative to larger mammals, we argue that pollinators put high‐priority and high‐impact urban conservation within reach. In a rapidly urbanizing world, transforming how environmental managers view the city can improve citizen engagement and contribute to the development of more sustainable urbanization.     

Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea

Dense blooms of diazotrophic filamentous cyanobacteria are formed every summer in the Baltic Sea. We estimated their contribution to nitrogen fixation by combining two decades of cyanobacterial biovolume monitoring data with recently measured genera-specific nitrogen fixation rates. In the Bothnian Sea, estimated nitrogen fixation rates were 80 kt N year⁻¹, which has doubled during recent decades and now exceeds external loading from rivers and atmospheric deposition of 69 kt year⁻¹. The estimated contribution to the Baltic Proper was 399 kt N year⁻¹, which agrees well with previous estimates using other approaches and is greater than the external input of 374 kt N year⁻¹. Our approach can potentially be applied to continuously estimate nitrogen loads via nitrogen fixation. Those estimates are crucial for ecosystem adaptive management since internal nitrogen loading may counteract the positive effects of decreased external nutrient loading.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

Exposure to radiation from global system for mobile communications at 1,800 MHz significantly changes gene expression in rat hippocampus and cortex

We have earlier shown that radio frequency electromagnetic fields can cause significant leakage of albumin through the blood–brain barrier of exposed rats as compared to non-exposed rats, and also significant neuronal damage in rat brains several weeks after a 2 h exposure to a mobile phone, at 915 MHz with a global system for mobile communications (GSM) frequency modulation, at whole-body specific absorption rate values (SAR) of 200, 20, 2, and 0.2 mW/kg. We have now studied whether 6 h of exposure to the radiation from a GSM mobile test phone at 1,800 MHz (at a whole-body SAR-value of 13 mW/kg, corresponding to a brain SAR-value of 30 mW/kg) has an effect upon the gene expression pattern in rat brain cortex and hippocampus—areas where we have observed albumin leakage from capillaries into neurons and neuronal damage. Microarray analysis of 31,099 rat genes, including splicing variants, was performed in cortex and hippocampus of 8 Fischer 344 rats, 4 animals exposed to global system for mobile communications electromagnetic fields for 6 h in an anechoic chamber, one rat at a time, and 4 controls kept as long in the same anechoic chamber without exposure, also in this case one rat at a time. Gene ontology analysis (using the gene ontology categories biological processes, molecular functions, and cell components) of the differentially expressed genes of the exposed animals versus the control group revealed the following highly significant altered gene categories in both cortex and hippocampus: extracellular region, signal transducer activity, intrinsic to membrane, and integral to membrane. The fact that most of these categories are connected with membrane functions may have a relation to our earlier observation of albumin transport through brain capillaries.     

A description of a test plant for combustion on a pilot scale

A plant for combustion on a pilot scale is described.The plant is comprised of two shaft furnaces connected to a common afterburning chamber. Sawmill chips and LP-gas are used as supporting fuel. The plant is provided with equipment for continuous feeding-in of both solid and liquid wastes.The products of decomposition and residual concentrations resulting from combustion of the wastes are analysed with respect to different organic compounds. These analyses are made on ingoing product, slag, dust and fuel gases. In addition, temperature, transit time, gas composition (CO, CO₂, O₂), fuelgas flow and - in some cases - parameters such as total hydrocarbon and nitrogen oxides are investigated.     

Modelling Change in Ground Vegetation Response to Acid and Nitrogen Pollution, Climate Change and Forest Management at in Sweden 1500–2100 a.d.

The ForSAFE model, designed for modelling biogeochemical cycles (water, acidity, base cation, nitrogen and carbon) in terrestrial ecosystems, was modified with a vegetation response module (VEG), incorporating the effects of: nitrogen pollution, acidification, soil moisture, temperature, wind chill exposure, light and shading by trees, grazing by animals, competition between plants, above ground for light and below ground for water and nutrients. The model calculates the response of number ground vegetation plant groups. The integrated model was tested and validated at integrated level II forest monitoring sites across Sweden, four have been shown here, and used to assess the effect of acidification and nitrogen pollution in relation to factors such as climate change, forest management and changing grazing pressure. The response functions have been derived from single-factor experiments and integrated through the model structure for use on whole systems. The tests with the model suggest that the ground vegetation composition is reasonably well predicted, that much research remains before the model is fully tested and operational, and that the model may serve as a tool for assessing impacts of climate change, acid rain and forest management on plant biodiversity in forested areas.     

Using digital image projections to visualize forest landscape changes due to management activities and forest growth

A pedagogic problem in forestry and landscape management is to visualize future landscape effects of forest growth and current management activities in the forest. This paper presents a method for forecasting digital image projections of forest landscape dynamics. Static nonlinear regression functions estimate the digital numbers in a Landsat Thematic Mapper image. Regressors used are forest stand variables. By estimating the future forest stand data, based on intermediate treatment and growth, future satellite digital images are created. In a case study example, the future landscape of a forest block in the province of Västernorrland, Sweden, is projected to demonstrate the application of this visualization technique.