Classifying the Ice Seasons 1982-2016 Using the Weighted Ice Days Number as a New Winter Severity Characteristic

Sea ice is a key climate factor and it restricts considerably the winter navigation in severe seasons on the Baltic Sea. So determining ice conditions severity and describing ice cover behavior at severe seasons are necessary. The ice seasons severity degree is studied at the years 1982 to 2016. A new integrative characteristic named the weighted ice days number of the season is introduced to determine the ice season severity. The ice concentration data on the Baltic Sea published in the European Copernicus Programme are used to calculate the maximal ice extent and the weighted ice days number of the seasons. Both the ice season severity characteristics are used to classify the winters with respect of severity. The ice seasons 1981/82, 1984/85, 1985/86, 1986/87, 1995/96 and 2002/03 are classified as severe by the weighted ice days number. Only three seasons of this list are severe by both the criteria. We interpret this coincidence as the evidence of enough-during extensive ice cover in these three seasons. In the winter 2010/11 ice cover extended widely for some time, but did not last longer. At 2002/03 and a few other ice seasons the Baltic Sea was ice-covered in moderate extent, but the ice cover stayed long time. For 11 winters (32 % of the period) the relational weighted ice days number differs considerably (> 10 %) from the relational maximal ice extent. These winters yield one third of the studied ice seasons. Statistically every 6th winter is severe by the weighted ice days number whereas only statistically every 8th winter is severe by the maximal ice extent on the Baltic. Hence there are more intrinsically severe seasons than the maximal ice extent gives. The maximal ice extent fails to account with the ice cover durability. The weighted ice days number enables to describe the ice cover behavior more representatively. Using the weighted ice days number adds the temporal dimension to the ice season severity study

Primary age-related tauopathy in a Finnish population-based study of the oldest old (Vantaa 85+)

Abstract Aims Few studies have investigated primary age-related tauopathy (PART) in a population-based setting. Here, we assessed its prevalence, genetic background, comorbidities and features of cognitive decline in an unselected elderly population. Methods The population-based Vantaa 85+ study includes all 601 inhabitants of Vantaa aged ≥ 85 years in 1991. Neuropathological assessment was possible in 301. Dementia (DSM IIIR criteria) and Mini-Mental State Examination (MMSE) scores were assessed at the baseline of the study and follow-ups. PART subjects were identified according to the criteria by Crary et al and were compared with subjects with mild and severe Alzheimer's disease (AD) neuropathological changes. The effects of other neuropathologies were taken into account using multivariate and sensitivity assays. Genetic analyses included APOE genotypes and 29 polymorphisms of the MAPT 3′ untranslated region (3′UTR region). Results The frequency of PART was 20n = 61/301, definite PART 5. When PART subjects were compared with those with severe AD pathology, dementia was less common, its age at onset was higher and duration shorter. No such differences were seen when compared with those with milder AD pathology. However, both AD groups showed a steeper decline in MMSE scores in follow-ups compared with PART. APOE ε4 frequency was lower, and APOE ε2 frequency higher in the PART group compared with each AD group. The detected nominally significant associations between PART and two MAPT 3′UTR polymorphisms and haplotypes did not survive Bonferroni correction. Conclusions PART is common among very elderly. PART subjects differ from individuals with AD-type changes in the pattern of cognitive decline, associated genetic and neuropathological features.Peer reviewe

Long-term dynamics of soil, tree stem and ecosystem methane fluxes in a riparian forest

Funding Information: This study was supported by the Ministry of Education and Science of Estonia (SF0180127s08 grant), the Estonian Research Council (IUT2-16, PRG-352, and MOBERC20), the Czech Science Foundation (17-18112Y), SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797), the Ministry of Education, Youth and Sports of Czech Republic within the National Sustainability Program I (NPU I, grant number LO1415), the EU through the European Regional Development Fund (ENVIRON and EcolChange Centres of Excellence, Estonia, and MOBTP101 returning researcher grant by the Mobilitas Pluss programme), the European Social Fund (Doctoral School of Earth Sciences and Ecology). This work was also supported by Academy of Finland (294088, 288494), from the European Research Council (ERC) under the European Union?s Horizon 2020 research and innovation programme under grant agreement No [757695], and a Department of Energy (DOE) grant to JPM (DE-SC0008165). Funding Information: This study was supported by the Ministry of Education and Science of Estonia ( SF0180127s08 grant), the Estonian Research Council ( IUT2-16 , PRG-352 , and MOBERC20 ), the Czech Science Foundation ( 17-18112Y ), SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions ( CZ.02.1.01/0.0/0.0/16_019/0000797 ), the Ministry of Education, Youth and Sports of Czech Republic within the National Sustainability Program I (NPU I, grant number LO1415 ), the EU through the European Regional Development Fund (ENVIRON and EcolChange Centres of Excellence, Estonia, and MOBTP101 returning researcher grant by the Mobilitas Pluss programme), the European Social Fund (Doctoral School of Earth Sciences and Ecology). This work was also supported by Academy of Finland ( 294088 , 288494 ), from the European Research Council (ERC) under the European Union‘s Horizon 2020 research and innovation programme under grant agreement No [ 757695 ], and a Department of Energy (DOE) grant to JPM ( DE-SC0008165 ). Publisher Copyright: © 2021 Elsevier B.V.The carbon (C) budgets of riparian forests are sensitive to climatic variability. Therefore, riparian forests are hot spots of C cycling in landscapes. Only a limited number of studies on continuous measurements of methane (CH4) fluxes from riparian forests is available. Here, we report continuous high-frequency soil and ecosystem (eddy-covariance; EC) measurements of CH4 fluxes with a quantum cascade laser absorption spectrometer for a 2.5-year period and measurements of CH4 fluxes from tree stems using manual chambers for a 1.5 year period from a temperate riparian Alnus incana forest. The results demonstrate that the riparian forest is a minor net annual sink of CH4 consuming 0.24 kg CH4-C ha−1 y−1. Soil water content is the most important determinant of soil, stem, and EC fluxes, followed by soil temperature. There were significant differences in CH4 fluxes between the wet and dry periods. During the wet period, 83% of CH4 was emitted from the tree stems while the ecosystem-level emission was equal to the sum of soil and stem emissions. During the dry period, CH4 was substantially consumed in the soil whereas stem emissions were very low. A significant difference between the EC fluxes and the sum of soil and stem fluxes during the dry period is most likely caused by emission from the canopy whereas at the ecosystem level the forest was a clear CH4 sink. Our results together with past measurements of CH4 fluxes in other riparian forests suggest that temperate riparian forests can be long-term CH4 sinks.Peer reviewe

Forest canopy mitigates soil N2O emission during hot moments

Funding Information: This study was supported by the Ministry of Education and Science of Estonia (SF0180127s08 grant), the Estonian Research Council (IUT2-16, PRG-352, and MOBERC20), the Czech Science Foundation (17-18112Y) and project SustES— Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797), the EU through the European Regional Development Fund (Centres of Excellence ENVIRON, grant number TK-107, EcolChange, grant number TK-131, and the MOBTP101 returning researcher grant by the Mobilitas Pluss program) and the European Social Fund (Doctoral School of Earth Sciences and Ecology). This work was also supported by the Academy of Finland (294088, 288494), and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No [757695]. We would like to thank Marek Jakubík for his technical support. Publisher Copyright: © 2021, The Author(s).Peer reviewedPublisher PD

From BIM towards digital twin: Strategy and future development for smart asset management

With the rising adoption of Building Information Model (BIM) for as-set management within architecture, engineering, construction and owner-operated (AECO) sector, BIM-enabled asset management has been increasingly attracting more attentions in both research and practice. This study provides a comprehensive review and analysis of the state-of-the-art latest research and industry standards development that impact upon BIM and asset management within the operations and maintenance (O&M) phase. However, BIM is not always enough in whole-life cycle asset management, especially in the O&M phase. Therefore, a framework for future development of smart asset management are proposed, integrating the concept of Digital Twin (DT). DT integrates artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. The findings will contribute to inspiring novel research ideas and promote wide-spread adoption of smart DT-enabled asset management within the O&M phaseCentre for Digital Built Britain, Innovate U

Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation.

Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70-117 t CO2 eq ha-1 yr-1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions

Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

Automated Planning of Concrete Joint Layouts with 4D-BIM

Concrete pouring represents a major critical path activity that is often affected by design limitations, structural considerations and on-site operational constraints. As such, meticulous planning is required to ensure that both the aesthetic and structural integrity of joints between cast in-situ components is achieved. Failure to adequately plan concrete pouring could lead to structural defects, construction rework or structural instability, all having major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual planning’ of concrete pouring is a challenging task and prone to human errors. Against this backdrop, this study developed 4D Building Information Management (BIM) approach to facilitate automated concrete joint positioning solution (as a proof of concept) for design professionals and contractors. The study first developed structural model in Revit, then extracted spatial information regarding all construction joints and linked them to dynamic Microsoft (MS) Excel and Matlab spreadsheets using integration facilitated by Dynamo software. Midspan points of each beam as well as floor perimeter information were gathered via codes developed in MS Excel macros. Based on the Excel outputs, Matlab programming was used to determine best concreating starting points and directions, and daily allowed concrete volume, considering limitations due to cold joints. These information were then pushed back to Revit via Dynamo in order to develop daily concrete scheduling. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of designing construction joints and planning pours. This framework extends the body of knowledge by introducing innovative solutions to integrate structural design considerations, constructional procedures and operational aspects for mitigating human error, and providing a novel, yet technically sound, basis for further application of BIM in structural engineering

Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel

Genetic imputation is a cost-efficient way to improve the power and resolution of genome-wide association (GWA) studies. Current publicly accessible imputation reference panels accurately predict genotypes for common variants with minor allele frequency (MAF) >= 5% and low-frequency variants (0.5Peer reviewe