Ультразвуковое исследование при дегенеративно-дистрофических и воспалительных заболеваниях коленного сустава

Рассмотрены актуальные вопросы диагностики заболеваний коленного сустава − деформирующего остеоартроза, ревматоидного и псориатического артритов. Предложены критерии дифференциальной диагностики этих заболеваний.Urgent issues of diagnosis of knee joint diseases (osteoarthrosis deformans, rheumatoid and psoriatic arthritis) are discussed. The criteria of differential diagnosis are suggested

Selection of climate models for developing representative climate projections for the Hindu Kush Himalayan Region

This series is based on the work of the Himalayan Adaptation, Water and Resilience (HI-AWARE) consortium under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Ottawa, Canada. CARIAA aims to build the resilience of vulnerable populations and their livelihoods in three climate change hot spots in Africa and Asia. The programme supports collaborative research to inform adaptation policy and practice.In HI-AWARE, both statistical and dynamical downscaling techniques will be used to downscale and bias correct climate model data to higher spatial resolutions. For both approaches, General Circulation Models (GCMs) and Regional Climate Models (RCMs) must be selected to either be statistically downscaled or used as boundary and forcing for dynamical downscaling. This report discusses the statistical downscaling component. There are two fundamentally different methods for selecting appropriate GCMs/RCMs. The first approach aims to cover the full envelope of possible futures ranging from dry and cold projections to wet and warm projections, while the second approach selects GCMs/RCMs on the basis of indicators of past performance. Both approaches have their pros and cons, but in the case of the Hindu Kush Himalayas (HKH) the first approach may be preferable as climate models have considerable difficulty in simulating past climate (Turner and Annamalai 2012). In this study, we develop a new method that combines the two existing methods. We aim to select a set of climate models that both cover a wide range of possible futures, but are also able to reproduce the most important processes in the region

Development and evaluation of digital twins for district-level heating energy demand simulation.

To achieve the aim of a CO2 neutral built environment in 2050, a large part of the existing housing stock will have to be energetically retrofitted. It has been noted that a neighbourhood-oriented approach will be necessary for the feasibility, affordability and timeliness of this aim. Considering that many different stakeholders are involved in renovations at the neighbourhood level, and that multiple neighbourhoods will have to be retrofitted at the same time, efficient working methods are imperative. To facilitate the design, construction and operation of the new energy infrastructure, a prototype for a digital environment (digital twin) is developed for four Dutch pilot neighbourhoods. In this contribution, the authors will describe a procedure to convert publicly available geo-information to a CityGML model, which is used to simulate the monthly and annual space heating energy demand using SimStadt. To assess model fidelity, the simulation results are compared with publicly available aggregated energy use data. A procedure will be described to split the measured natural gas use into gas usage for space heating, domestic hot water and cooking. It is found that the simulation tends to overestimate the energy demand for space heating by 4 - 125%. This difference is largely explained by the manner in which the thermal properties of the buildings are estimated. In addition, the homogeneity of the neighbourhood in terms of the different building functions present has an impact on the accuracy of the simulation. Finally, possible invalid assumptions concerning setpoint temperatures and internal heating loads are of interest. It is concluded that more accurate simulation results will be obtained through the use of current input data. Most importantly: (i) reliable information on the buildings’ current thermal properties through e.g. energy audits, and (ii) reliable information on the buildings’ setpoint temperatures and internal heating loads through on-board monitoring systems

Modeling Cost Impacts and Adaptation of Freeze-Thaw Climate Change on a Porous Asphalt Road Network

Changes in weather patterns pose a threat to the serviceability and long-term performance of roads, and porous asphalt (PA) roads are particularly sensitive to the freezing-thawing (FT) phenomenon. The main objective of this research is to assess the impact of climate change, particularly freezing and thawing cycles, on PA. Climate models predict changes in air temperature, not pavement temperature. To predict the climate change impact on pavements performance, this requires first establishing a relationship between air and road temperature and a correlation between pavement performance and FT cycles. This project focuses on the Netherlands, where PA pavement use has become mandatory, and recent severe winters have increased the discussion about the cold weather performance of porous asphalt and the potential challenges of changing winter weather patterns. When considering long-term changes in climate, the cost impacts of freeze-thaw on PA pavement are predicted to vary regionally and in most areas reach a point in the middle of the century when a reactive wait-and-see approach is more advantageous than proactive adaptation. Further research is suggested to refine the relationship between observed damage and freeze-thaw impacts on PA pavement.</p

South Asian river basins in a 1.5 °C warmer world

In 2015, with the signing of the “Paris Agreement”, 195 countries committed to limiting the increase in global temperature to less than 2 °C with respect to pre-industrial levels and to aim at limiting the increase to 1.5 °C by 2100. The regional ramifications of those thresholds remain however largely unknown and variability in the magnitude of change and the associated impacts are yet to be quantified. We provide a regional quantitative assessment of the impacts of a 1.5 versus a 2 °C global warming for a major global climate change hotspot: the Indus, Ganges, and Brahmaputra river basins (IGB) in South Asia, by analyzing changes in climate change indicators based on 1.5 and 2 °C global warming scenarios. In the analyzed ensemble of general circulation models, a global temperature increase of 1.5 °C implies a temperature increase of 1.4–2.6 (μ = 2.1) °C for the IGB. For the 2.0 °C scenario, the increase would be 2.0–3.4 (μ = 2.7) °C. We show that climate change impacts are more adverse under 2 °C versus 1.5 °C warming and that changes in the indicators’ values are in general linearly correlated to average temperature increase. We also show that for climate projections following Representative Concentration Pathways 4.5 and 8.5, which may be more realistic, the regional temperature increases and changes in climate change indicators are much stronger than for the 1.5 and 2 °C scenarios

South Asian river basins in a 1.5 °C warmer world

In 2015, with the signing of the “Paris Agreement”, 195 countries committed to limiting the increase in global temperature to less than 2 °C with respect to pre-industrial levels and to aim at limiting the increase to 1.5 °C by 2100. The regional ramifications of those thresholds remain however largely unknown and variability in the magnitude of change and the associated impacts are yet to be quantified. We provide a regional quantitative assessment of the impacts of a 1.5 versus a 2 °C global warming for a major global climate change hotspot: the Indus, Ganges, and Brahmaputra river basins (IGB) in South Asia, by analyzing changes in climate change indicators based on 1.5 and 2 °C global warming scenarios. In the analyzed ensemble of general circulation models, a global temperature increase of 1.5 °C implies a temperature increase of 1.4–2.6 (μ = 2.1) °C for the IGB. For the 2.0 °C scenario, the increase would be 2.0–3.4 (μ = 2.7) °C. We show that climate change impacts are more adverse under 2 °C versus 1.5 °C warming and that changes in the indicators’ values are in general linearly correlated to average temperature increase. We also show that for climate projections following Representative Concentration Pathways 4.5 and 8.5, which may be more realistic, the regional temperature increases and changes in climate change indicators are much stronger than for the 1.5 and 2 °C scenarios

Assessing risk and adaptation options to fires and windstorms in European forestry

Adaptation, Climate change, Forest fire, Forestry, Natural disturbance, Windstorm, EFISCEN,