Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images

In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.Peer reviewe

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Urban vertical farming with a large wind power share and optimised electricity costs

Producing food in an environmentally sustainable way for the growing human population is a challenge to the global food system. Vertical farm (VF) as a part of the solution portfolio is attracting interest since it uses less water, pesticides, and land which are scarce in many parts of the globe. Despite these positive factors, the energy demand for vertical farms is high, and farms often remain separate and excluded from cities where most of the population lives. City-level energy system solutions exist to empower energy efficiency and increase the share of variable renewable energy sources, but their potential has not yet been estimated for an urban energy system that includes large vertical farms. Accordingly, in this study, we simulate an urban energy system that practices vertical farming with large-scale variable renewable energies and flexibility measures. For the first part of the study, we modelled a vertical farm's energy system with demand response control to maximize electricity cost savings. To evaluate the potential of demand response, the analysis is carried out for different crops (lettuce, wheat, and soybean), and different electricity price profiles. The result of demand response control can be a reduction of 5% to 30% in electricity consumption costs. Further, sensitivity analyses highlight the effect of electricity price variations and photoperiod on demand response outcomes. In the second part, the operation of an urban energy system (Helsinki, Finland) with vertical farms was analysed through two different scenarios. These scenarios represent the emission-free Helsinki energy system in 2050 with large-scale wind power implementation. As VFs can use electricity outside the peak demand hours, the inclusion of VF with the right energy system configuration can improve the power consumption within the system by up to 19%. Further, we show that connection to the exogenous power market is important to support vertical farming in the future energy systems. In this study, key points in the integration of VF in urban energy systems are highlighted, including the role of exogenous power markets, the potential for increasing local energy consumption with large wind power, and the importance of crop selection in reducing VF's energy costs through demand response. In a city-level solution with a high wind power share, we thus recommend including a vertical farm side by strong sectoral coupling as part of the future design to maximise local consumption

Current and Novel Emerging Medical Therapies for Peripheral Artery Disease: A Literature Review

Despite the improvements in endovascular techniques during the last decades, there is still an increase in the prevalence of peripheral artery disease (PAD) with limited practical treatment, which timeline impact of any intervention for critical limb ischemia (CLI) is poor. Most common treatments are not suitable for many patients due to their underlying diseases, including aging and diabetes. On the one hand, there are limitations for current therapies due to the contraindications of some individuals, and on the other hand, there are many side effects caused by common medications, for instance, anticoagulants. Therefore, novel treatment strategies like regenerative medicine, cell-based therapies, Nano-therapy, gene therapy, and targeted therapy, besides other traditional drugs combination therapy for PAD, are newly considered promising therapy. Genetic material encoding for specific proteins concludes with a potential future for developed treatments. Novel approaches for therapeutic angiogenesis directly used the angiogenetic factors originating from key biomolecules such as genes, proteins, or cell-based therapy to induce blood vessel formation in adult tissues to initiate the recovery process in the ischemic limb. As PAD is associated with high mortality and morbidity of patients causing disability, considering the limited treatment choices for these patients, developing new treatment strategies to prevent PAD progression and extending life expectancy, and preventing threatening complications is urgently needed. This review aims to introduce the current and the novel strategies for PAD treatment that lead to new challenges for relief the patient’s suffered from the disorder

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Modeling and optimization of urban energy systems for large-scale integration of variable renewable energy generation

Defense is held on 10.9.2021 12:00 – 16:00 (Zoom), https://aalto.zoom.us/j/69170835429To meet the future emissions goals, the energy systems need to be decarbonized. As much of the energy use originates from urban areas, their role will be of key importance in this context. One strategy for decarbonization is to use large-scale variable renewable electricity schemes, but these include several challenges, notably the issue of supply and demand mismatch. Therefore, a mix of technologies may be needed to achieve ambitious decarbonization targets in cities. The aim of this doctoral thesis is to develop solutions for city-level energy system transition. For this purpose, a dynamic energy system model for Helsinki city is used to ana-lyze a range of scenarios for a low-carbon future. Renewable energy, in particular wind power, was chosen here as the key supply technology. As northern cities are heat-dominated, the heat-ing sector was included in the analysis by using power-to-heat and heat pump schemes in par-allel to power production. To meet short peak heat demand conditions, separate bio-boilers were also considered. Such schemes provided deep decarbonization possibilities. In the Hel-sinki case, the use of fossil fuels could be reduced even up to 70% through the coupling of wind power with curtailment and heat pumps. Though the above type of sectoral coupling to heating helps to integrate large amounts of intermittent renewable power, the role of the exog-enous power market proved to be important for wind power integration. For Helsinki, for ex-ample, with a wind power capacity of 1500 MW corresponding to 62% of the annual electricity demand, 89% of the wind electricity could be used locally in the different sectors, but the rest needs to be coupled to the exogenous market due to the mismatch and plant limitations. In-corporating demand-side measures, e.g., building energy efficiency, could save 6%-13% in the annual system costs. Other alternatives such as sustainable gas were also investigated. The results of this thesis indicate that there are several decarbonization pathways of the urban energy systems of which some could even yield a zero-emission energy system

Building data model identification and predictive demand response control

Data-based models are conceptual tools for describing the real world entities and the connection among them. There are a lot of studies with usage of data-based structures for forecasting, optimization and fault detection in building energy systems. The main objectives of this thesis have been devoted to evaluate how data-based modelling can contribute to energy building system identification and demand response control. In overall view, this research work contains the separate sections for identifying energy system of buildings and establishing the optimal demand response control. In first stage of this research, multi stages prediction algorithm has been developed by means of a novel combination of signal processing technique (wavelet transformation) and dynamic neural network. Different prediction algorithms have been examined for forecasting the energy demand of building with considering random profiles in occupancy, lighting and equipment profiles. Also ability of model in forecasting energy demand has been investigated for different type of building structures and insulation levels. The model predictive control then has been organized for achieving the optimal energy trading schedule in energy building systems. The system includes building, Photovoltaic (PV) component, heat storage tank and ground source heat pump (GSHP). The heat demand of building contains space heating and domestic hot water demands. Active thermal storage is used for saving energy regarding to dynamic electricity price. Then based on dynamic coefficient of performance (COP) for GSHP, the required electricity of compressor has been achieved. On-site energy generated by PV system has been balanced with electricity demand of system to support trading with grid. The applied predictor structure improves the time of applying control algorithms for building energy systems signifi-cantly. The applied control methodology and on-site energy generation also save the energy cost as 28% and energy consumption as 17% for passive massive buildings. The results highlight the adaptability of proposed algorithm in prediction and identifying the energy system for different building structures and insulation levels. The comparative results also reveal the priority of the proposed method in aspect of prediction accuracy as compared to neural network. The integration of signal analysis and dynamic neural network is strong alternative for common simulation software in applying and evaluation of different control systems

Construction cost estimation of spherical storage tanks

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg–Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.Peer reviewe

Effect of heat demand on integration of urban large-scale renewable schemes-case of Helsinki City (60 °n)

Heat demand dominates the final energy use in northern cities. This study examines how changes in heat demand may affect solutions for zero-emission energy systems, energy system flexibility with variable renewable electricity production, and the use of existing energy systems for deep decarbonization. Helsinki city (60 °N) in the year 2050 is used as a case for the analysis. The future district heating demand is estimated considering activity-driven factors such as population increase, raising the ambient temperature, and building energy efficiency improvements. The effect of the heat demand on energy system transition is investigated through two scenarios. The BIO-GAS scenario employs emission-free gas technologies, bio-boilers and heat pumps. The WIND scenario is based on large-scale wind power with power-to-heat conversion, heat pumps, and bio-boilers. The BIO-GAS scenario combined with a low heat demand profile (-12% from 2018 level) yields 16% lower yearly costs compared to a business-as-usual higher heat demand. In the WIND-scenario, improving the lower heat demand in 2050 could save the annual system 6-13% in terms of cost, depending on the scale of wind power.Peer reviewe