Severe undernutrition among under-5 children is usually assessed using single or conventional indicators (i.e., severe stunting, severe wasting, and/or severe underweight). But these conventional ...indicators partly overlap, thus not providing a comprehensive estimate of the proportion of malnourished children in the population. Incorporating all these conventional nutritional indicators, the Composite Index of Severe Anthropometric Failure (CSIAF) provides six different undernutrition measurements and estimates the overall burden of severe undernutrition with a more comprehensive view. This study applied the CISAF indicators to investigate the prevalence of severe under-5 child undernutrition in Bangladesh and its associated socioeconomic factors in the rural-urban context.
This study extracted the children dataset from the 2017-18 Bangladesh Demographic Health Survey (BDHS), and the data of 7661 children aged under-5 were used for further analyses. CISAF was used to define severe undernutrition by aggregating conventional nutritional indicators. Bivariate analysis was applied to examine the proportional differences of variables between non-severe undernutrition and severe undernutrition group. The potential associated socioeconomic factors for severe undernutrition were identified using the adjusted model of logistic regression analysis.
The overall prevalence of severe undernutrition measured by CISAF among the children under-5 was 11.0% in Bangladesh (rural 11.5% vs urban 9.6%). The significant associated socioeconomic factors of severe undernutrition in rural areas were children born with small birth weight (AOR: 2.84), children from poorest households (AOR: 2.44), and children aged < 36 months, and children of uneducated mothers (AOR: 2.15). Similarly, in urban areas, factors like- children with small birth weight (AOR: 3.99), children of uneducated parents (AOR: 2.34), poorest households (APR: 2.40), underweight mothers (AOR: 1.58), mothers without postnatal care (AOR: 2.13), and children's birth order ≥4 (AOR: 1.75), showed positive and significant association with severe under-5 undernutrition.
Severe undernutrition among the under-5 children dominates in Bangladesh, especially in rural areas and the poorest urban families. More research should be conducted using such composite indices (like- CISAF) to depict the comprehensive scenario of severe undernutrition among the under-5 children and to address multi-sectoral intervening programs for eradicating severe child undernutrition.
•Novel optimization algorithm to optimize both urban morphology and energy system.•Building form and urban density will increase in the energy demand by 10% and 27%.•The influence of urban morphology ...on energy system cost can be up to 50%.•Co-optimization of both urban morphology and energy system is vital maintain climate resilience.
Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment.
We have undertaken a deep (σ∼ 1.1 mJy) 1.1-mm survey of the z= 0.54 cluster MS 0451.6−0305 using the AzTEC camera on the James Clerk Maxwell Telescope. We detect 36 sources with signal-to-noise ratio ...(S/N) ≥ 3.5 in the central 0.10 deg2 and present the AzTEC map, catalogue and number counts. We identify counterparts to 18 sources (50 per cent) using radio, mid-infrared, Spitzer InfraRed Array Camera (IRAC) and Submillimetre Array data. Optical, near- and mid-infrared spectral energy distributions are compiled for the 14 of these galaxies with detectable counterparts, which are expected to contain all likely cluster members. We then use photometric redshifts and colour selection to separate background galaxies from potential cluster members and test the reliability of this technique using archival observations of submillimetre galaxies. We find two potential MS 0451−03 members, which, if they are both cluster galaxies, have a total star formation rate (SFR) of ∼100 M⊙ yr−1– a significant fraction of the combined SFR of all the other galaxies in MS 0451−03. We also examine the stacked rest-frame mid-infrared, millimetre and radio emission of cluster members below our AzTEC detection limit, and find that the SFRs of mid-IR-selected galaxies in the cluster and redshift-matched field populations are comparable. In contrast, the average SFR of the morphologically classified late-type cluster population is nearly three times less than the corresponding redshift-matched field galaxies. This suggests that these galaxies may be in the process of being transformed on the red sequence by the cluster environment. Our survey demonstrates that although the environment of MS 0451−03 appears to suppress star formation in late-type galaxies, it can support active, dust-obscured mid-IR galaxies and potentially millimetre-detected LIRGs.
Abstract
Climate change and increased urban population are two major concerns for society. Moving towards more sustainable energy solutions in the urban context by integrating renewable energy ...technologies supports decarbonizing the energy sector and climate change mitigation. A successful transition also needs adequate consideration of climate change including extreme events to ensure the reliable performance of energy systems in the long run. This review provides an overview of and insight into the progress achieved in the energy sector to adapt to climate change, focusing on the climate resilience of urban energy systems. The state-of-the-art methodology to assess impacts of climate change including extreme events and uncertainties on the design and performance of energy systems is described and discussed. Climate resilience is an emerging concept that is increasingly used to represent the durability and stable performance of energy systems against extreme climate events. However, it has not yet been adequately explored and widely used, as its definition has not been clearly articulated and assessment is mostly based on qualitative aspects. This study reveals that a major limitation in the state-of-the-art is the inadequacy of climate change adaptation approaches in designing and preparing urban energy systems to satisfactorily address plausible extreme climate events. Furthermore, the complexity of the climate and energy models and the mismatch between their temporal and spatial resolutions are the major limitations in linking these models. Therefore, few studies have focused on the design and operation of urban energy infrastructure in terms of climate resilience. Considering the occurrence of extreme climate events and increasing demand for implementing climate adaptation strategies, the study highlights the importance of improving energy system models to consider future climate variations including extreme events to identify climate resilient energy transition pathways.
Steps followed to evaluate system flexibility.
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•Redefining flexibility using fuzzy logic and multi criterion decision making.•Combining flexibility and stochastic optimization.•Novel ...optimization algorithm based on GPU computing.•Pareto optimization considering flexibility, NPV and GI.
A novel method is introduced in this study to consider flexibility taking into account both system design and operation strategy by using fuzzy logic. A stochastic optimization algorithm is introduced to optimize the system design and operation strategy of the energy system while considering the flexibility. GPU (Graphics Processing Unit)-accelerated computing is introduced to speed up the computation process when computing the expected values of the objective functions considering a pool up to 5832 scenarios. Subsequently, a Pareto optimization is conducted considering Net Present Value (NPV), Grid Integration (GI) level (which represents the autonomy level of the energy system) and system flexibility. The case study assesses an energy system design problem for the city of Lund in Sweden. According to the obtained NPV and GI Pareto front, a renewable energy penetration level covering more than 45% of the annual demand of the energy hub (an integrated energy system consisting of wind turbines, solar PV panels, internal combustion generator and a battery bank) can be achieved. However, the flexibility of the system notably decreases when the renewable energy penetration level exceeds above 30%. Furthermore, the results show that poor system flexibility notably increases the risk of higher-loss of load probability and operation cost. It is also shown that the utility grid acts as a virtual storage when integrating renewable energy sources. In this context, a grid dependency level of 25–30% (of the annual energy demand) is sufficient while reaching a renewable energy penetration level of 30% and maintaining the system flexibility.
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•Combining urban climate, building simulation and energy system optimization.•Urban climate influence the peak and annual demand.•Urban climate results in more fluctuations in demand ...profile.•Performance gap (energy system) is up to 50% due to neglecting the urban climate.•Neglecting urban climate may results in drop of power supply reliability.
Rapid growth of cities, concerns on global warming and depletion of fossil fuel resources call for sustainable energy solutions for cities. Distributed energy systems such as energy hubs offer promising solutions in this context. Evaluating the energy demand at urban scale is vital to support the design of energy hubs. However, most of the recent studies are based on bottom-up models and do not consider the energy demand in detail. More specifically, the influence of the urban climate on urban energy demand has not been considered so far in the energy system design process. In order to address this research gap, a novel computational platform is developed in the first part of this study, combining an urban climate model with a building simulation tool and an energy system optimization model. The second part of the manuscript is devoted to quantifying the impact of urban climate on energy system design and assessing the consequences of neglecting this specific aspect on energy system performance. Three case studies are conducted considering three building densities for the city of Nablus (building density at the periphery, center and future center of the city) in Palestine. Three scenarios representing (1) standalone buildings (present practice) (2) shadowing and longwave reflection (radiation heat transfer from the walls and the roofs of the buildings to the urban climate and to the sky) of neighboring buildings and (3) urban climate are considered for each case study when computing the energy demand. Subsequently, the energy system is optimized considering Net Present Value (NPV) and system autonomy level as the objective functions (Pareto optimization). The results of the study reveal that the urban climate has a notable impact on the energy demand and energy system design. More importantly, it is shown that the influence of urban climate results in higher fluctuations in the energy demand, which in turn results in a notable increase in the NPV (by up to 40%). This further magnifies the increase in annual or peak demand. The study reveals that neglecting the influence of urban climate in the energy system design process can result in a performance gap in NPV, grid integration level, and greenhouse gas emissions and can impose reliability issues. The design tool introduced in this study can be used for urban planning to mitigate the aforementioned adverse effects.
Immune evasion is a major obstacle for cancer treatment. Common mechanisms of evasion include impaired antigen presentation caused by mutations or loss of heterozygosity of the major ...histocompatibility complex class I (MHC-I), which has been implicated in resistance to immune checkpoint blockade (ICB) therapy
. However, in pancreatic ductal adenocarcinoma (PDAC), which is resistant to most therapies including ICB
, mutations that cause loss of MHC-I are rarely found
despite the frequent downregulation of MHC-I expression
. Here we show that, in PDAC, MHC-I molecules are selectively targeted for lysosomal degradation by an autophagy-dependent mechanism that involves the autophagy cargo receptor NBR1. PDAC cells display reduced expression of MHC-I at the cell surface and instead demonstrate predominant localization within autophagosomes and lysosomes. Notably, inhibition of autophagy restores surface levels of MHC-I and leads to improved antigen presentation, enhanced anti-tumour T cell responses and reduced tumour growth in syngeneic host mice. Accordingly, the anti-tumour effects of autophagy inhibition are reversed by depleting CD8
T cells or reducing surface expression of MHC-I. Inhibition of autophagy, either genetically or pharmacologically with chloroquine, synergizes with dual ICB therapy (anti-PD1 and anti-CTLA4 antibodies), and leads to an enhanced anti-tumour immune response. Our findings demonstrate a role for enhanced autophagy or lysosome function in immune evasion by selective targeting of MHC-I molecules for degradation, and provide a rationale for the combination of autophagy inhibition and dual ICB therapy as a therapeutic strategy against PDAC.
We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the ...increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. The transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.
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•An exponential increase in publications covering the vulnerability and resilience of the energy infrastructure.•COVID analogy is introduced to understanding the propagation of disruption within and beyond the energy ecosystem.•Transformation of the energy landscape into an ecosystem could notably increase the vulnerability during extreme events.•There is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure.
An integrated approach is presented in this study to design electrical hubs combining optimization, multi-criterion assessment and decision making. Levelized Energy Cost (LEC), Initial Capital Cost ...(ICC), Grid Integration Level (GI), Levelized CO2 emission (LCO2), utilization of renewable energy, flexibility of the system, loss of load probability (LOLP) are considered as criteria used to assess the design. The novel approach consists of several steps. Pareto analysis is conducted initially using 2D Pareto fronts to reduce the dimensions of the optimization problem. Subsequently, Pareto multi objective optimization is conducted considering LEC, GI and ICC which were identified as the best set of objective functions to represent the design requirements. Next, fuzzy TOPSIS and level diagrams are used for multi-criterion decision making (MCDM) considering the set of criteria and the boundary matrix that represents the design requirements of the application. Pareto analysis shows that 5D optimization problem can be reduced to a 3D optimization problem when considering LEC, ICC and GI as the objective functions. Finally, results obtained from the case study shows that the novel method can be used design distributed energy systems considering a set of criteria which is beyond the reach of Pareto optimization with different priority levels.
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•A novel integrated method to design distributed energy system.•Determine most suitable objective functions for Pareto optimization.•MCDM considering set of criterions with different priorities.•Combine optimization, Pareto analysis and MCDM.
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