Search

In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

The purpose of this research is to demystify literature on aerotropolis using systematic review. Literature on aerial life and aeronautical studies suggests that airports are frequently cited outside urban centres. However, recent events surrounding the growth of aerotropolis contradicts existing realities. In fact, the pull and push factors constitute the life cycle of aerotropolis in urban enclaves. In generating data for this study, Dimensions, an artificial intelligence databank, was adopted, and a hybrid method which combines both VOSviewer and Citespace software was the preferred analytical tool for analysis. Key findings were imperative in establishing certain parameters regarding aerial life, including but not limited to knowledge about the technologies adopted, quality of stakeholders, in addition to existing relationships of urban space, urbanisation, and geography. Furthermore, two recurrent themes were identified, such as the development in ICT, and smart technologies, which corresponds with the multiple potentials that exist for developing sustainable airports, such as eco-innovation, greenovation, and social innovation. This study contributes to the concept of transit-bound tourism, a concept we coined to depict the role tourism can play in transit philosophy and economics.

Pseudomonas aeruginosa is a Gram-negative bacterium that is able to survive and adapt in a multitude of niches as well as thrive within many different hosts. This versatility lies within its large genome of ca. 6 Mbp and a tight control in the expression of thousands of genes. Among the regulatory mechanisms widespread in bacteria, cyclic-di-GMP signaling is one which influences all levels of control. c-di-GMP is made by diguanylate cyclases and degraded by phosphodiesterases, while the intracellular level of this molecule drives phenotypic responses. Signaling involves the modification of enzymes’ or proteins’ function upon c-di-GMP binding, including modifying the activity of regulators which in turn will impact the transcriptome. In P. aeruginosa, there are ca. 40 genes encoding putative DGCs or PDEs. The combined activity of those enzymes should reflect the overall c-di-GMP concentration, while specific phenotypic outputs could be correlated to a given set of dgc/pde. This notion of specificity has been addressed in several studies and different strains of P. aeruginosa. Here, we engineered a mutant library for the 41 individual dgc/pde genes in P. aeruginosa PAO1. In most cases, we observed a significant to slight variation in the global c-di-GMP pool of cells grown planktonically, while several mutants display a phenotypic impact on biofilm including initial attachment and maturation. If this observation of minor changes in c-di-GMP level correlating with significant phenotypic impact appears to be true, it further supports the idea of a local vs global c-di-GMP pool. In contrast, there was little to no effect on motility, which differs from previous studies. Our RNA-seq analysis indicated that all PAO1 dgc/pde genes were expressed in both planktonic and biofilm growth conditions and our work suggests that c-di-GMP networks need to be reconstructed for each strain separately and cannot be extrapolated from one to another., Frontiers in Microbiology, 13, ISSN:1664-302X

The COVID-19 pandemic is a real shock to society and business and financial markets. The government bond market is an essential part of financial markets, especially in difficult times, because it is a source of government funding. The majority of existing ESG studies report positive impacts on corporate financial performance regarding environmental, social, and governance. Thus, understanding governments’ financial practices and their relevant ESG implications is insufficient. This research aims to value the impact of the COVID-19 pandemic on different government bond curve sectors. We try to identify the reactions to the COVID-19 pandemic in the government bond market and analyze separate tenors of government bond yields in different regions. We have chosen Germany and the United States government bond yields of 10, 5, and 3 years tenor for the analysis. As independent variables, we have chosen daily cases of COVID-19 and daily deaths from COVID-19 at the country and global levels. We used daily data from 02 January 2020–19 March 2021, and divided this period into three stages depending on the COVID-19 pandemic data. We employed the methods of correlation-regression analysis (ordinary least squares and least squares with breakpoints) and VAR-based impulse response functions to evaluate the effect of the COVID-19 pandemic on government bond yields both in the long and short run. Our analysis revealed the impact of the spread of the COVID-19 pandemic on government bond yields differs depending on the country and the assessment period. The short-term responses vary in direction, strength, and duration; the long-term response of Germany’s yields appeared to be more negative (indicating the decrease of the yields), while the response of the United States yields appeared to be more positive (i.e., increase of yields).

Changes in rainfall onset and cessation dates are critical for improving decision making and adaptation strategies in numerous socio-economic sectors. An objective method of determining onset and cessation date is employed over Southern West Africa (SWA) in this study. The method is applied over 34 years of the quasi-global rainfall dataset from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and five High Resolution Model Intercomparison Project (HighResMIP) model datasets under the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment. Generally, a strong agreement exists between CHIRPS and the HighResMIP models in capturing the behaviour of seasonal rainfall over SWA, with models able to capture the bimodal rainfall season. The ability of models in capturing onset and cessation dates as observed in CHIRPS shows the strength of these models in representing the short break between the two wet seasons that is otherwise known as the ‘Little Dry Season’. Patterns observed in the onset and cessation dates over the SWA region are consistent with the northward and southward displacement of the Intertropical Convergence Zone (ITCZ). The seasonal timing of the models shows good agreement with observations such that most mean onset/cessation dates agree within 26 days. While IPSL-CM6A-ATM-HR, a model among the five HighResMIPs used in the study, best agrees with CHIRPS in representing onset and cessation dates during the unimodal rainfall season, no one model best agrees with CHIRPS during the bimodal season, with models outperforming each other in representing onset/cessation dates with little variation.

The recently developed polyoxometalate (POM)–ionosolv concept offers an interesting strategy to generate two valuable product streams from lignocellulosic biomass, a solid cellulose-rich pulp and short-chain carboxylic acids like formic acid and acetic acid in a simple and cost-efficient manner. This study aimed to find optimum parameters for the two steps of the transformation by performing a sensitivity analysis on the initial ionosolv fractionation step as well as kinetic investigations of the following POM-catalyzed oxidation step. The results were transferred to the POM–ionosolv concept to find the overall process optimum. Beech wood was used as an industrially relevant substrate for ionosolv fractionation with the low-cost ionic liquid triethylammonium sulfate, [TEA][HSO4], and the HPA-5 [H8PV5Mo7O40] POM catalyst for the oxidation of the dissolved components in an oxygen atmosphere. As the most seminal finding, we defined optimum conditions of 125 °C, 1200 rpm, 30 bar oxygen, and 24 h reaction time in ionic liquid containing 70% water, achieving 72% xylose extraction from beech wood, which resulted in a 39% formic acid yield. We suggest that the fractionation and catalytic conversion are carried out at different water contents for maximum conversion efficiency for each step.

Negative emission technologies (NETs) could play a key role in ensuring net-zero and longer-term net negative emission ambitions are met. However, greenhouse gas emissions (and other pollutants) will occur over the life cycle of a NET and will need to be taken into consideration when developing schemes to roll out their use. We compare five NETs: afforestation/reforestation (AR), enhanced weathering (EW), mangrove restoration (MR), bioenergy and direct air capture with carbon storage (BECCS and DAC), using life cycle assessment to determine their environmental impacts (global warming, freshwater, toxicity etc.). We find that there is a wide range in the environmental impacts estimated across the NETs and the context in which they are used will directly impact which NET has low or high environmental impacts. This is an important aspect to consider when deciding which NET to prioritise in strategies to roll out their use on large scales. If consistent removal of CO2 from the atmosphere is the goal, then AR and MR have the lowest environmental impacts. However, if large and quick CO2 removal is the goal then EW, DAC and BECCS have similar, if not lower, environmental impacts.

Currently, conventional carbon dioxide (CO2) mitigation solutions may be insufficient to achieve the stringent environmental targets set for the coming decades. CO2 removal (CDR) technologies, such as direct air capture (DAC), capturing CO2 from the ambient air, are required. In this research, an independent life cycle assessment (LCA) of DAC adsorption systems based on three physisorbents (metal organic frameworks) and two chemisorbents (amine functionalized sorbents) is presented. These capture processes have been optimised by us in previous work. Results show that for the overall capture process, negative CO2 emissions are ensured by using a cellulose-based amine sorbent (cradle-to-gate) ensuring even the net removal of CO2 from the atmosphere (cradle-to-grave). Processes using physisorbents have poorer performances. Chemisorbents yield operating conditions allowing lower impacts on the environment. In 2050, these processes could reduce climate change but can generate other environmental impacts. With the aim to have better environmental performances of DAC systems, future research should be focused on improving the physical properties of sorbents such as the silica gel based amine sorbent to increase their capture capacities. If metal organic frameworks are to be used, it is necessary to drop their energy consumption (by increasing the loading) and the required mass of sorbent.

The risks of Aedes aegypti and Aedes albopictus nuisance and vector-borne diseases are rising and the adverse effects of broad-spectrum insecticide application have promoted species-specific techniques, such as sterile insect technique (SIT) and other genetic strategies, as contenders in their control operations. When specific vector suppression is proposed, potential effects on predators and wider ecosystem are some of the first stakeholder questions. These are not the only Aedes vectors of human diseases, but are those for which SIT and genetic strategies are of most interest. They vary ecologically and in habitat origin, but both have behaviorally human-adapted forms with expanding ranges. The aquatic life stages are where predation is strongest due to greater resource predictability and limited escape opportunity. These vectors' anthropic forms usually use ephemeral water bodies and man-made containers as larval habitats; predators that occur in these are mobile, opportunistic and generalist. No literature indicates that any predator depends on larvae of either species. As adults, foraging theory predicts these mosquitoes are of low profitability to predators. Energy expended hunting and consuming will mostly outweigh their energetic benefit. Moreover, as adult biomass is mobile and largely disaggregated, any predator is likely to be a generalist and opportunist. This work, which summarizes much of the literature currently available on the predators of Ae. aegypti and Ae. albopictus, indicates it is highly unlikely that any predator species depends on them. Species-specific vector control to reduce nuisance and disease is thus likely to be of negligible or limited impact on nontarget predators. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Central Zagros region in Iran is a major hotspot of carbon storage and sequestration which has experienced severe land cover change in recent decades that has led to carbon emission. In this research, using temporal Landsat images, land cover maps were produced and used in Land Change Modeler to predict land cover changes in 2020, 2030, 2040 and 2050 using Multilayer Perceptron Neural Network and Markov Chain techniques. Next, resultant maps were used as inputs to Ecosystem Services Modeler. The Intergovernmental Panel on Climate Change (IPCC) report data was used to extract carbon data. Results show that between 1989–2013 about half of forests have been destroyed. Prediction results show that by 2050 about 75% of existing forests will be lost and between 2013–2020 about 157,000 Mg carbon and by 2050 about 565,000 Mg carbon will be lost with more than US$1.9 million to 2020 and AU$3.2 million by 2050 economic compensation.

Urban green spaces (UGSs) may present economic contributions through increases in proximate property values, encapsulated in the proximity principle (PP). More data on the PP is required from the Global South, where the quality and equitable distribution of UGSs are important considerations. This paper investigates the PP in Potchefstroom, South Africa following a quantitative approach, by statistically analyzing municipal property valuations in three districts differentiated according to their socioeconomic status (SES). Districts are divided into sample areas where three zones are demarcated according to their proximity to a UGS. The results show that property valuations are generally higher for properties in closer proximity to UGSs in lower-and higher-income samples, but are lower in middle-income areas. Neighborhood characteristics and SES, UGS amenity and maintenance, ecosystem services and disservices, domestic garden area and residential property size may be connected to the confirmation or rejection of the PP. The rejection of the PP in middle-income areas indicates a need to improve public UGSs as amenity destinations. The results confirming the PP in low-income areas could incentivize expenditures to improve UGS area and quality to increase the willingness to pay for proximity to such spaces and, reciprocally, increase revenue from municipal property taxes.

Brood diseases and pesticides can reduce the survival of bee larvae, reduce bee populations, and negatively influence ecosystem biodiversity. However, major gaps persist in our knowledge regarding the routes and implications of co-exposure to these stressors in managed and wild bee brood. In this review, we evaluate the likelihood for co-exposure to brood pathogen and pesticide stressors by examining the routes of potential co-exposure and the possibility for pollen and nectar contaminated with pathogens and pesticides to become integrated into brood food. Furthermore, we highlight ways in which pesticides may increase brood disease morbidity directly, through manipulating host immunity, and indirectly through disrupting microbial communities in the guts of larvae, or compromising brood care provided by adult bees. Lastly, we quantify the brood research bias towards Apis species and discuss the implications the bias has on brood disease and pesticide risk assessment in wild bee communities. We advise that future studies should place a higher emphasis on evaluating bee brood afflictions and their interactions with commonly encountered stressors, especially in wild bee species.

Following the BBC’s Blue Planet II nature documentary series on marine ecosystems, plastic packaging has come under public fire, with consumers demanding greener alternatives. The biodegradable properties of some bioplastics have offered a potential solution to the global challenge of plastic pollution, while enabling the capture of food waste through anaerobic digestion as a circular and energy-positive waste treatment strategy. However, despite their increasing popularity, currently bioplastics are being tested in environments that do not reflect real-life waste management scenarios. Bioplastics find their most useful, meaningful and environmentally-sound application in food packaging—why is there so little interest in addressing their anaerobic co-digestion with food waste? Here, we provide a set of recommendations to ensure future studies on bioplastic end-of-life are fit for purpose. This perspective makes the link between the environmental sustainability of bioplastics and the role of food waste anaerobic digestion as we move towards an integrated food–energy–water–waste nexus. It shines light on a novel outlook in the field of bioplastic waste management while uncovering the complexity of a successful path forward. Ultimately, this research strives to ensure that the promotion of bioplastics within a circular economy framework is supported across waste collection and treatment stages.

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited., Atmosphere, 13 (5), ISSN:2073-4433

Purpose This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector. Design/methodology/approach The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed. Findings This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation. Originality/value Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Mobile genetic elements (MGEs) and antimicrobial resistance (AMR) drive important ecological relationships in microbial communities and pathogen-host interaction. In this study, we investigated the resistome-associated mobilome in 345 publicly available Pasteurellaceae genomes, a large family of Gram-negative bacteria including major human and animal pathogens. We generated a comprehensive dataset of the mobilome integrated into genomes, including 10,820 insertion sequences, 2,939 prophages, and 43 integrative and conjugative elements. Also, we assessed plasmid sequences of Pasteurellaceae. Our findings greatly expand the diversity of MGEs for the family, including a description of novel elements. We discovered that MGEs are comparable and dispersed across species and that they also co-occur in genomes, contributing to the family’s ecology via gene transfer. In addition, we investigated the impact of these elements in the dissemination and shaping of AMR genes. A total of 55 different AMR genes were mapped to 721 locations in the dataset. MGEs are linked with 77.6% of AMR genes discovered, indicating their important involvement in the acquisition and transmission of such genes. This study provides an uncharted view of the Pasteurellaceae by demonstrating the global distribution of resistance genes linked with MGEs.

The aim of the study is to evaluate fluids circulation through the Chicxulub crater, and to determine the composition of hydrothermal fluids after the impact. Rock-Eval pyrolysis and fluid inclusion micro-thermometry analyses were performed. The technique has been routinely used for about fifteen years and has become a standard tool for hydrocarbon exploration. Rock-Eval pyrolysis reveals the distribution of organic and mineral carbon affected by the impact and later affected by hydrothermal activity. All measured inclusions are primary and were found in basement samples only. Both the fluid inclusions data and Rock-Eval pyrolysis show that composition and temperature of the fluids changed as the fluids migrated though crater rocks. An evolution of temperatures occurs (vertical, horizontal, or both), from the surface and from the center of the crater; this spatial evolution is consistent with model of Abramov and Kring, showing a thermal evolution of temperature with depth in the crater as well as its influence on the hydrothermal system. Post-impact fluid circulation modifies the temperature distribution.

In this paper, we discuss a novel approach to chemical storage based on the physical properties of the chemicals (reactivity, solid or liquid, and volume) and the intrinsic hazards associated with a chemical as identified by the hazard statements. These are indicated on a substance’s safety data sheet (SDS), for example, oxidizing, flammability, toxicity, and compatibility with other chemicals. We introduce a flowchart to identify appropriate storage locations for each chemical in the laboratory and describe what amendments could be needed to our system if it is replicated at another institution. We describe the advantages of using a chemical inventory system and how it was integrated with our storage guidance. Finally, we provide insights and share lessons learnt on maintaining good chemical management.

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Technological breakthroughs and policy measures targeting energy efficiency and clean energy alone will not suffice to deliver Paris Agreement-compliant greenhouse gas emissions trajectories in the next decades. Strong cases have recently been made for acknowledging the decarbonisation potential lying in transforming linear economic models into closed-loop industrial ecosystems and in shifting lifestyle patterns towards this direction. This perspective highlights the research capacity needed to inform on the role and potential of the circular economy for climate change mitigation and to enhance the scientific capabilities to quantitatively explore their synergies and trade-offs. This begins with establishing conceptual and methodological bridges amongst the relevant and currently fragmented research communities, thereby allowing an interdisciplinary integration and assessment of circularity, decarbonisation, and sustainable development. Following similar calls for science in support of climate action, a transdisciplinary scientific agenda is needed to co-create the goals and scientific processes underpinning the transition pathways towards a circular, net-zero economy with representatives from policy, industry, and civil society. Here, it is argued that such integration of disciplines, methods, and communities can then lead to new and/or structurally enhanced quantitative systems models that better represent critical industrial value chains, consumption patterns, and mitigation technologies. This will be a crucial advancement towards assessing the material implications of, and the contribution of enhanced circularity performance to, mitigation pathways that are compatible with the temperature goals of the Paris Agreement and the transition to a circular economy. © 2021 The Authors This work was supported by the H2020 European Commis- sion projects “PARIS REINFORCE”(Grant Agreement No. 820846), “LOCOMOTION”(Grant Agreement No. 821105), “NDC ASPECTS”(Grant Agreement No. 101003866), and “newTRENDS”(Grant Agreement No. 893311); the European Research Council (ERC) project “FINEPRINT”(Grant Agreement No. 725525); the Hel- lenic Foundation for Research and Innovation (HFRI) and General Secretariat for Research and Technology (GSRT) project “ATOM”(Grant Agreement No. HFRI-FM17–2566); the Spanish Ministry of Science, Innovation, and Universities projects RTI2018–099858- A-I00 and RTI2018–093352-B-I00; the María de Maeztu excel- lence accreditation 2018–2022 (Ref. MDM-2017–0714), funded by MCIN/AEI/10.13039/50110 0 011033; and by the Basque Government through the BERC 2018–2021 program and BIDERATU project (KK- 2021/0 0 050, ELKARTEK programme 2021). The sole responsibility for the content of this paper lies with the authors; the paper does not necessarily reflect the opinions of the European Commission, the Basque Government, or the Spanish Government.

Road freight transport is one of the major contributors to greenhouse gas and air pollutant emissions. Hence, it is increasingly regulated in urban areas to reduce its impact on the environment and human health. The rich data available from telematics has the potential to provide high-resolution information, yet research has not been conducted to understand, evaluate, and ultimately improve the operation and impacts of urban road freight. This paper demonstrates the role of vehicle telematics data in enabling quantitative assessment of the impacts of urban freight transport for the effective management of relevant policies. We present a comprehensive data-driven approach that provides a robust quantitative evaluation and applies it to a case study of the London Lorry Control Scheme (LLCS) policy in UK. We show that, for the studied freight operator, the LLCS policy affects their drivers’ route choice both inside and outside the restricted hours. The spatio-temporal distributions of different parameters including traffic speeds, fuel economy and emissions at different times of the day are compared and analyzed. The results indicate that the unintended consequences of urban freight transport policies can include an extra 15% vehicle-km traveled per trip and 12% liter of fuel consumed per trip

Immediate and short-term changes in diet composition can support individualized, real-time interventions in precision dairy production systems, and might increase feed efficiency (FE) of dairy cattle in the short-term. The objective of this study was to determine immediate and short-term effects of changes in diet composition on production parameters of dairy cattle fed varying amounts of top dressed commodities. A 4 × 4 replicated Latin square design was used to evaluate responses of twenty-four Holstein cows fed either no top dress (Control) or increasing amounts of: corn grain (CG), soybean meal (SBM), or chopped mixed grass hay (GH) top dressed on a total mixed ration (TMR) over four, 9-day periods. Throughout each period, top dressed commodities were incrementally increased, providing 0% to 20% of calculated net energy of lactation (NEL ) intake. Measured production responses were analyzed for each 9-d period using a mixed-effects model considering two different time ranges. Samples collected from d 3 and 4 and from d 7 and 8 of each period were averaged and used to reflect “immediate” vs. “short-term” responses, respectively. In the immediate response time frame, control fed cows had lower milk yield, milk fat yield, and milk true protein yield than CG and SBM supplemented animals but similar responses to GH supplemented animals. Milk fat and protein percentages were not affected by top dress type in the immediate term. In the short-term response time-frame, GH supplemented animals had lower DMI and milk fat yield than all other groups. Control and GH supplemented cows had lower milk yield than CG and SBM fed cows. In the immediate response time frame, FE of SBM supplemented cows was superior to other groups. In the short-term time frame, FE of GH and SBM groups was improved over the control group. Results suggest that lactating dairy cows show rapid performance responses to small (

The COVID-19 pandemic has spurred significant changes in the fields of economic development, social issues, everyday life, etc. Activities that used to depend on face-to-face communication were firstly suspended and then shifted to new forms of communication. This includes the public participation process in urban and spatial planning. Therefore, this study explores the new domain developed in urban and spatial planning with regard to public participation and surmises future realms in the post-pandemic era. On the occasion of the virtual collaboration platform Cyber Agora organized by the ISOCARP (International Society of City and Regional Planners), chosen participants got together virtually to share, discuss, and compare their practical knowledge in public participation before and during COVID-19. In addition, they addressed the potential benefits of shifting from traditional to virtual participation and potential benefits in the post-COVID-19 era. Considering the collected data and understanding them in the light of the available literature, this study concludes that the application of a combined approach (using both traditional and virtual modes of participation) is recommended because it would enable a larger number and higher diversity of participants. The study also elaborates particular modes of virtual participation with the pros and cons of their use in a particular context.

The changes in the concentration and composition of biomass-burning organic aerosol (OA) downwind of a major wildfire are simulated using the one-dimensional Lagrangian chemical transport model PMCAMx-Trj. A base case scenario is developed based on realistic fire-plume conditions and a series of sensitivity tests are performed to quantify the effects of different conditions and processes. Temperature, oxidant concentration and dilution rate all affect the evolution of biomass burning OA after its emission. The most important process though is the multi-stage oxidation of both the originally emitted organic vapors (volatile and intermediate volatility organic compounds) and those resulting from the evaporation of the OA as it is getting diluted. The emission rates of the intermediate volatility organic compounds (IVOCs) and their chemical fate have a large impact on the formed secondary OA within the plume. The assumption that these IVOCs undergo only functionalization leads to an overestimation of the produced SOA suggesting that fragmentation is also occurring. Assuming a fragmentation probability of 0.2 resulted in predictions that are more consistent with available observations. Dilution leads to OA evaporation and therefore reduction of the OA levels downwind of the fire. However, the evaporated material can return to the particulate phase later on after it gets oxidized and recondenses. The sensitivity of the OA levels and total mass balance on the dilution rate depends on the modeling assumptions. The high variability of OA mass enhancement observed in past field studies downwind of fires may be partially due to the variability of the dilution rates of the plumes.

In South Africa, the Coronavirus Disease 2019 (COVID-19) pandemic is occurring against the backdrop of high Human Immunodeficiency Virus (HIV), tuberculosis and non-communicable disease burdens as well as prevalent herpesviruses infections such as Epstein-Barr virus (EBV) and Kaposi’s sarcoma-associated herpesvirus (KSHV). As part of an observational study of adults admitted to Groote Schuur Hospital, Cape Town, South Africa during the period June–August 2020 and assessed for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we measured KSHV serology and KSHV and EBV viral load (VL) in peripheral blood in relation to COVID-19 severity and outcome. A total of 104 patients with PCR-confirmed SARS-CoV-2 infection were included in this study. 61% were men and 39% women with a median age of 53 years (range 21–86). 29.8% (95% CI: 21.7–39.1%) of the cohort was HIV positive and 41.1% (95% CI: 31.6–51.1%) were KSHV seropositive. EBV VL was detectable in 84.4% (95% CI: 76.1–84.4%) of the cohort while KSHV DNA was detected in 20.6% (95% CI: 13.6–29.2%), with dual EBV/KSHV infection in 17.7% (95% CI: 11.1–26.2%). On enrollment, 48 [46.2% (95% CI: 36.8–55.7%)] COVID-19 patients were classified as severe on the WHO ordinal scale reflecting oxygen therapy and supportive care requirements and 30 of these patients [28.8% (95% CI: 20.8–38.0%)] later died. In COVID-19 patients, detectable KSHV VL was associated with death after adjusting for age, sex, HIV status and detectable EBV VL [p = 0.036, adjusted OR = 3.17 (95% CI: 1.08–9.32)]. Furthermore, in HIV negative COVID-19 patients, there was a trend indicating that KSHV VL may be related to COVID-19 disease severity [p = 0.054, unstandardized co-efficient 0.86 (95% CI: –0.015–1.74)] in addition to death [p = 0.008, adjusted OR = 7.34 (95% CI: 1.69–31.49)]. While the design of our study cannot distinguish if disease synergy exists between COVID-19 and KSHV nor if either viral infection is indeed fueling the other, these data point to a potential contribution of KSHV infection to COVID-19 outcome, or SARS-CoV-2 infection to KSHV reactivation, particularly in the South African context of high disease burden, that warrants further investigation.

Does a unilateral climate change policy cause companies to shift the location of production, thereby creating carbon leakage? In this paper, we analyze the effect of the European Union Emissions Trading System (EU ETS) on the geographic distribution of carbon emissions by multinational companies. The empirical evidence is based on unique data for the period 2007–2014 from the Carbon Disclosure Project, which tracks the emissions of multinational businesses by geographic region within each company. Because they already operate from multiple locations, multinational firms should be the most prone to carbon leakage. Our data includes the regional emissions of 1,122 companies, of which 261 are subject to EU ETS regulation. We find no evidence that the EU ETS has led to a displacement of carbon emissions from Europe toward the rest of the world, including to countries with lax climate policies and within energy-intensive companies. A large number of robustness checks confirm this finding. Overall, the paper suggests that modest differences in carbon prices between countries do not induce carbon leakage.

Copyright © 2021 The Author(s). The pace to achieving a sustainable plastics economy remains noticeably slow due to a lack of understanding on the role and importance of stakeholder dynamics in the plastic packaging system. This study aims to unpack and assess the role of stakeholders in improving plastics recycling rates and circularity in the UK, using polyethylene terephthalate (PET) drinks bottles value chain as a case study. Via the theoretical lens of stakeholder theory we identify and group the stakeholders involved in the PET drinks bottles value chain, and integrate this in the Complex Value Optimisation for Resource Recovery (CVORR) systems thinking approach to make sense of, and analyse, stakeholders complex interactions. Results highlight that even though, external stakeholders (e.g. NGOs, trade associations) engage on different levels and scales in promoting circularity in the PET bottles value chain, there is strong drive in incentivising production and consumption processes driven by the significant lobbying power of internal stakeholders operating upstream of the PET bottles value chain (i.e. producers and brand owners). This lobbying power, which arises from the well-established market of PET bottles in the UK, and its support by financial institutions, it strongly influences national and local government policies and decision-making processes. Meanwhile, the waste management processes are short-sighted, being unable to gain improved momentum and increasing the PET bottles recycling rates. This dynamic conceals, and somewhat retains, the prevailing resistance in removing the infrastructural, regulatory and technological lock-ins. The development of an interdisciplinary collaboration between internal and external stakeholders is paramount to sustainably managing PET drinks bottles in the UK and achieving a transition to a sustainable circular plastics economy. Fostering closer collaboration between all stakeholders involved in the system, can aid the development of new value networks and support new policy interventions that can improve circularity in the plastic packaging sector. Brunel University London as part of the Brunel Research Initiative & Enterprise Fund (BRIEF) award No.11683100, in the context of ‘Closing the Plastic Food Packaging Loop’ project, and generously supported by the Oxford Martin School, University of Oxford.

Currently, many cities in Europe are affected by concentrations of PM2.5 and NO2 above the WHO guidelines on the protection of human health. This is a global problem in which the growth of road transport constitutes a major factor. Looking to the future, electric vehicles (EVs) are considered to be the choice technology for reducing road transport greenhouse gas emissions, but their impact on air quality needs to be considered. Taking the UK as a case study, this paper begins by understanding the trajectory of a future scenario without the introduction of EVs, reflecting on the latest emission control improvements in internal combustion engine vehicles (ICEVs). This is then compared to a 2050 scenario in which the introduction of EVs, based on the UK government’s Transport Decarbonisation Plan, is reviewed. This plan includes a ban on the sale of ICEV cars and LGVs, beginning in 2030, with the subsequent electrification of heavier vehicles. By 2030, population exposure to NOx was found to be significantly reduced in the ICEV scenario, with a marginal further reduction found for the EV scenario. The EV scenario further reduced NOx exposure by 2050, with most of the benefits being realized before 2040. For the ICEV and EV scenario, PM2.5 emissions were largely unchanged due to the primary contribution of non-exhaust emissions, suggesting that EVs are likely to yield relatively smaller changes in exposure to PM2.5 than for NOx.

Bioactive aminooxime ligands based on optically pure (R)-limonene have been synthesized in two steps. Their ruthenium (II) cationic water-soluble complex was prepared by a reaction between dichloro (para-cymene) ruthenium (II) dimers and aminooxime ligands in a 1:2 molar ratio. Antibacterial and antibiofilm activities of the synthetized complex were assessed against Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, and Enterococcus faecalis. The results revealed that the ruthenium (II) complex has higher antibacterial and antibiofilm activities in comparison with free ligands or the enantiopure (R)-limonene. Moreover, microencapsulation of this complex reduced its cytotoxicity and improved their minimum inhibitory concentration and antibiofilm activity toward the considered bacteria. The ruthenium (II) complex targets the bacterial cell membrane, which leads to rapid leakage of intracellular potassium. Our study suggests that the developed ruthenium (II) complexes could be useful as an alternative to conventional disinfectants.

Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen’s seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples the market price of a house into price components that are attributable to housing characteristics. Despite the potential contribution of this theory in a planning context, three main limitations exist in the conventional applications: (1) variable measurement issues, (2) model misspecification, and (3) the problematic common use of global regression. These flaws problematically skew our understanding of the urban structure and spatial distribution of amenities, leading to misinformed policy interventions and poor amenity planning decisions. In this article, we propose a coherent conceptual framework that addresses measurement, specification, and scale challenges to generate consistent economic estimates of local amenities. Finally, we argue that, by paying greater attention to the spatial equity of amenity values, governments can provide greater equality of opportunities in cities.

Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.

Habitat fragmentation has become one of the largest areas of research in conservation biology. Empirical studies into habitat fragmentation impacts typically measure ecological responses to metrics describing fragmentation processes, for example ‘distance to the nearest forest edge’, ‘forest fragment area’ and ‘landscape habitat amount’. However, these studies often fail to sample across representative ranges of fragmentation metrics characterising the study region. They therefore lack the data to account for correlation among multiple fragmentation metrics and for spatial autocorrelation among sample sites, which reduces the strength of derived predictive models. Here, we draw on approaches used in the mining and soil science industry to develop standardised and repeatable protocols for designing optimised sampling schemes of biodiversity in fragmented landscapes that meet three criteria: the distance between sample sites is maximised to reduce spatial autocorrelation, the full range of values of the metrics of interest are sampled and the confounding effects of correlated metrics are minimised. We show that our computational methods can optimise the placement of sample sites in fragmented landscapes to minimise, and in some cases to entirely avoid, over- or under-sampling of fragmentation metrics. Our method is flexible enough to cater for any continuous (e.g. maps of percentage tree cover) or categorical (e.g. habitat and land use types) fragmentation metric, and to simultaneously handle combinations of multiple fragmentation metrics and habitat types. We implement our methods as open-source code which includes options to mask invalid or inaccessible regions, update designs to adapt to unforeseen constraints in the field and suggest optimal numbers of sample sites for given design criteria. Using a case study landscape, we demonstrate how the approach improves on manually generated sampling designs. We also show that the methods are flexible enough to be applied to landscape studies beyond the field of habitat fragmentation. We introduce our package as a novel research tool that is able to streamline the experimental design process for biodiversity sampling and monitoring at landscape scales, leading to improved data quality and representativeness.

Emissions of carbon dioxide from fossil fuel combustion are reducing the ratio 13C/12C, δ13C, in atmospheric urn:x-wiley:23284277:media:eft2900:eft2900-math-0001 and in the carbon in the ocean and terrestrial biosphere that exchanges with the atmosphere on timescales of decades to centuries. Future changes to fossil fuel emissions vary across different scenarios and may cause decreases of more than 6‰ in atmospheric δ13urn:x-wiley:23284277:media:eft2900:eft2900-math-0002 between 1850 and 2100. The effects of these potential changes on the three-dimensional distribution of δ13C in the ocean has not yet been investigated. Here, we use an ocean biogeochemical-circulation model forced with a range of Shared Socioeconomic Pathway (SSP)-based scenarios to simulate δ13C in ocean dissolved inorganic carbon from 1850 to 2100. In the future, vertical δ13C gradients characteristic of the biological pump are reduced or reversed, relative to the preindustrial period, with the reversal occurring in higher emission scenarios. For the highest emission scenario, SSP5-8.5, surface δ13C in the centre of Pacific subtropical gyres falls from 2.2‰ in 1850 to -3.5‰ by 2100. In lower emission scenarios, δ13C in the surface ocean decreases but then rebounds. The relationship between anthropogenic carbon (Cant) and δ13C in the ocean shows a larger scatter in all scenarios, suggesting that errors in δ13C-based estimates of Cant may increase in the future. These simulations were run with fixed physical forcing and ocean circulation, providing a baseline of predicted δ13C. Further work is needed to investigate the impact of climate-carbon cycle feedbacks on ocean δ13C changes.

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

Housing market dynamics have primarily shifted from consumption- to investment-driven in many countries, including Australia. Building on investment theory, we investigated market dynamics by placing investment demand at the center using the error correction model (ECM). We found that house prices, rents, and interest rates are cointegrated in the long run under the present value investment framework. Other economic factors such as population growth, unemployment, migration, construction activities, and bank lending were also important determinants of the housing market dynamics. Our forecasting results show that the Sydney housing market will continue to grow with no significant price decline in the foreseeable future.

The Paris Agreement seeks to combine international efforts to keep global temperature increase to well‐below 2°C. Whilst current ambitions in many signatories are insufficient to achieve this goal, optimism prevailed in the second half of 2020. Not only did several major emitters announce net‐zero mitigation targets around mid‐century, but the new Biden Administration immediately announced the U.S.’s re‐entry into Paris and a net‐zero goal for 2050. U.S. federal re‐engagement in climate action could have a considerable impact on its national greenhouse gas emissions pathway, by significantly augmenting existing state‐level actions. Combined with U.S. re‐entry in the Paris Agreement, this could also serve as a stimulus to enhance ambitions in other countries. A critical question then becomes what such U.S. re‐engagement, through both national and international channels, would have on the global picture. This commentary explores precisely this question, by using an integrated assessment model to assess U.S. national emissions, global emissions, and end‐of‐century temperatures in five scenarios combining different climate ambition levels in both the U.S. and the rest of the world. Our analyses finds that ambitious climate leadership by the Biden Administration on top of enhanced climate commitments by other the major economies could potentially be the trigger for the world to fulfill the temperature goal of the Paris Agreement.

Comminution is an essential step in processing itabirite ores, given the need to liberate silica and other contaminants from the iron minerals for downstream concentration and then pellet feed production. In general, these ores in Brazil are not particularly hard to crush and grind, but both capital (CAPEx) and operating (OPEx) expenditures in this stage of preparation can be critical for the project, in particular due to uncertainties in iron ore prices. Several circuits have been designed and are in operation for this type of ore in Brazil, however, it is not yet clear which technologies are more cost-effective and in which configuration they should be applied. This work critically analyzes four comminution circuits for an undisclosed case study. For these circuits, CAPEx, OPEx, and some environmental sustainability indices, as well as qualitative technical criteria, were used in the comparisons. This work concludes that two of these process routes, especially those based on more energy-efficient technologies (and one of these still rarely explored even at bench-scale), have demonstrated to be very attractive from multiple standpoints.

Colistin is frequently a last resort treatment for Pseudomonas aeruginosa infections caused by multidrug-resistant (MDR) and extensively drug resistant (XDR) strains, and detection of colistin resistance is essential for the management of infected patients. Therefore, we evaluated the recently developed MALDIxin test for the detection of colistin resistance in P. aeruginosa clinical strains using the routine matrix-assisted laser desorption ionization (MALDI) Biotyper Sirius system. The test is based on the detection by mass spectrometry of modified lipid A by the addition of 4-amino-l-arabinose (l-ara4N) molecules on one or two phosphate groups, in strains resistant to colistin. Overproduction of l-Ara4N molecules is mainly due to the constitutive activation of the histidine kinase (PmrB) or the response regulator (PmrA) following an amino-acid substitution in clinical strains. The performance of the test was determined on a panel of 14 colistin-susceptible and 14 colistin-resistant P. aeruginosa clinical strains, the reference strain PAO1 and positive control mutants PmrB (V28G), PmrB (D172), PhoQ (D240–247), and ParR (M59I). In comparison with the broth microdilution (BMD) method, all the susceptible strains (n=14) and 8/14 colistin-resistant strains were detected in less than 1h, directly on whole bacteria. The remaining resistant strains (n=6) were all detected after a short pre-exposure (4h) to colistin before sample preparation. Validation of the method on a larger panel of strains will be the next step before its use in diagnostics laboratories. Our data showed that the MALDIxin test offers rapid and efficient detection of colistin resistant P. aeruginosa and is thus a valuable diagnostics tool to control the spread of these emerging resistant strains.

An integrated understanding of factors influencing the occurrence, distribution, and fate of antibiotic resistance genes (ARGs) in vegetable production systems is needed to inform the design and development of strategies for mitigating the potential for antibiotic resistance propagation in the food chain. The goal of the present study was to holistically track antibiotic resistance and associated microbiomes at three distinct pre-harvest control points in an agroecosystem in order to identify the potential impacts of key agricultural management strategies. Samples were collected over the course of a single growing season (67 days) from field-scale plots amended with various organic and inorganic amendments at agronomic rates. Dairy-derived manure and compost amendment samples (n = 14), soil samples (n = 27), and lettuce samples (n = 12) were analyzed via shotgun metagenomics to assess multiple pre-harvest factors as hypothetical control points that shape lettuce resistomes. Pre-harvest factors of interest included manure collection during/post antibiotic use, manure composting, and soil amended with organic (stockpiled manure/compost) versus chemical fertilizer. Microbial community resistome and taxonomic compositions were unique from amendment to soil to lettuce surface according to dissimilarity analysis. The highest resistome alpha diversity (i.e., unique ARGs, n = 642) was detected in amendment samples prior to soil application, while the composted manure had the lowest total ARG relative abundance (i.e., 16S rRNA gene-normalized). Regardless of amendment type, soils acted as an apparent ecological buffer, i.e., soil resistome and taxonomic profiles returned to background conditions 67 d-post amendment application. Effects of amendment conditions surprisingly re-emerged in lettuce phyllosphere resistomes, with the highest total ARG relative abundances recovered on the surface of lettuce plants grown in organically-fertilized soils (i.e., compost- and manure-amended soils). Co-occurrence analysis identified 55 unique ARGs found both in the soil amendments and on lettuce surfaces. Among these, arnA and pmrF were the most abundant ARGs co-occurring with mobile genetic elements (MGE). Other prominent ARG-MGE co-occurrences throughout this pre-harvest lettuce production chain included: TetM to transposon (Clostridiodies difficile) in the manure amendment and TriC to plasmid (Ralstonia solanacearum) on the lettuce surfaces. This suggests that, even with imposing manure management and post-amendment wait periods in agricultural systems, ARGs originating from manure can still be found on crop surfaces. This study demonstrates a comprehensive approach to identifying key control points for the propagation of ARGs in vegetable production systems, identifying potential ARG-MGE combinations that could inform future surveillance. The findings suggest that additional pre-harvest and potentially post-harvest interventions may be warranted to minimize risk of propagating antibiotic resistance in the food chain. Published version

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images., This project received funding from the ATTRACT project, funded by the European Commission (EC) under Grant Agreement 777,222, and from the Instituto de Salud Carlos III, under the project DTS19/00059, FEDER.

Spatial design at interior, site, city and regional scales is increasingly complex, and will continue to be so with the uncertainty of the climate crisis and the growing place-based intricacies of pluralist societies. In response to this complexity, professional design practice has pursued new ways of working. More design projects are becoming more interdisciplinary and less hierarchically structured, involving more collaborative project teams with a variety of backgrounds in architecture, urban design, landscape and interior architecture, engineering, ecological sciences and art. At universities, the design-learning studio which pedagogically champions the authentic replication of design practice projects, has also bifurcated. While teaching design through the traditional disciplinary-based problem-solving processes of an individual project is still understandably commonplace, a new type of studio has emerged, led by group work and interdisciplinary collaborations, and framed by the complexity of a seemingly irreconcilable problematic subject. This emergent domain warrants more research into pedagogical structures, teaching techniques and learning activities, and this paper explains such investigations undertaken through the live educational practice of two interdisciplinary studios in two years, drawing conclusions from student feedback gathered via questionnaires and focus group interviews. The findings suggest that teaching formats in this type of studio need to facilitate a balance between trusting relationships and immersive experiences, and that effective teaching techniques entail the development of more accessible communication techniques in conceptual diagramming and linguistic idiom.

Ventilation flows generated by a localised heat source within a room containing two openings to the ambient environment are examined. The openings are representative of a doorway and a high-level vent, and a steady two-layer stratification of well-mixed fluid is established within the room for all cases considered. Classical displacement ventilation flow is observed when the interface between the two layers is above the height of the doorway. Displacement ventilation flow can persist when the interface within the room is below the height of the doorway; in general, however, an unbalanced exchange flow forms across the doorway and the ventilating flow is found to be dependent on the doorway aspect ratio, the doorway height relative to the room height, the effective area of the high-level vent (relative to the square of the room height) and the rate of entrainment into the plume generated by the localised heat source. An analytical model is presented which predicts the ventilation rates and temperature structures within the room for these flows. Results from analogue experiments demonstrate good agreement with the model for a wide range of the parameter space relevant to the full-scale application.

The gut microbiome plays an important role in a host’s development and adaption to its dietary niche. In this study, a group of bamboo-feeding insects are used to explore the potential role of the gut microbiota in the convergent adaptation to extreme diet specialization. Specifically, using a 16S rRNA marker and an Illumina sequencing platform, we profiled the microbial communities of 76 gut samples collected from nine bamboo-feeding insects, including both hemimetabolous (Orthoptera and Hemiptera) and holometabolous (Coleoptera and Lepidoptera) species, which are specialized in three distinct dietary niches: bamboo leaf, shoot, and sap. The gut microbiota of these insects were dominated by Proteobacteria, Firmicutes, and Bacteroidetes and were clustered into solid (leaf and shoot) and liquid (sap) dietary niches. The gut bacterial communities of insects feeding on solid diet overlapped significantly, even though these insects belong to phylogenetically distant lineages representing different orders. In addition, the presence of cellulolytic bacterial communities within the gut microbiota allows bamboo-feeding insects to adapt to a highly specialized, fiber-rich diet. Although both phylogeny and diet can impact the structure and composition of gut microbiomes, phylogeny is the primary driving force underlying the convergent adaptation to a highly specialized diet, especially when the related insect species harbor similar gut microbiomes and share the same dietary niche over evolutionary timescales. These combined findings lay the foundation for future research on how convergent feeding strategies impact the interplays between hosts and their gut microbiomes and how the gut microbiota may facilitate convergent evolution in phylogenetically distant species in adaptation to the shared diet.

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated.

Making informed estimates of future energy technology costs is central to understanding the cost of the low-carbon transition. A number of methods have been used to make such estimates: extrapolating empirically derived learning rates; use of expert elicitations; and engineering assessments which analyse future developments for technology components' cost and performance parameters. In addition, there is a rich literature on different energy technology innovation systems analysis frameworks, which identify and analyse the many processes that drive technologies' development, including those that make them increasingly cost-competitive and commercially ready. However, there is a surprising lack of linkage between the fields of technology cost projections and technology innovation systems analysis. There is a clear opportunity to better relate these two fields, such that the detailed processes included in technology innovation systems frameworks can be fully considered when estimating future energy technology costs. Here we demonstrate how this can be done. We identify that learning curve, expert elicitation and engineering assessment methods already either implicitly or explicitly incorporate some elements of technology innovation systems frameworks, most commonly those relating to R&D and deployment-related drivers. Yet they could more explicitly encompass a broader range of innovation processes. For example, future cost developments could be considered in light of the extent to which there is a well-functioning energy technological innovation system (TIS), including support for the direction of technology research, industry experimentation and development, market formation including by demand-pull policies and technology legitimation. We suggest that failure to fully encompass such processes may have contributed to over-estimates of nuclear cost reductions and under-estimates of offshore wind cost reductions in the last decade.

Lawsuits concerning the impacts of climate change make causal claims about the effect of defendants’ greenhouse gas (GHG) emissions on plaintiffs and have proliferated around the world. Plaintiffs have sought, inter alia, compensation for climate-related losses and to compel governments to reduce their GHG emissions. So far, most of these claims have been unsuccessful. Here we assess the scientific and legal bases for establishing causation and evaluate judicial treatment of scientific evidence in 73 lawsuits. We find that the evidence submitted and referenced in these cases lags considerably behind the state of the art in climate science, impeding causation claims. We conclude that greater appreciation and exploitation of existing methodologies in attribution science could address obstacles to causation and improve the prospects of litigation as a route to compensation for losses, regulatory action and emission reductions by defendants seeking to limit legal liability. Legal cases to force governments to reduce emissions or to pursue compensation for climate change-related losses are increasing. The scientific evidence used in such cases is found to be lagging behind state-of-the-art climate science; using up-to-date methodologies could improve causation claims.

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antimicrobials, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens and thus are a key point of interest for antibiotic resistance surveillance. WWTP monitoring has the potential to inform with respect to the antibiotic resistance status of the community served as well as the potential for ARGs to escape treatment. However, there is lack of agreement regarding suitable sampling frequencies and monitoring targets to facilitate comparison within and among individual WWTPs. The objective of this study was to comprehensively evaluate patterns in metagenomic-derived indicators of antibiotic resistance through various stages of treatment at a conventional WWTP for the purpose of informing local monitoring approaches that are also informative for global comparison. Relative abundance of total ARGs decreased by ∼50% from the influent to the effluent, with each sampling location defined by a unique resistome (i.e., total ARG) composition. However, 90% of the ARGs found in the effluent were also detected in the influent, while the effluent ARG-pathogen taxonomic linkage patterns identified in assembled metagenomes were more similar to patterns in regional clinical surveillance data than the patterns identified in the influent. Analysis of core and discriminatory resistomes and general ARG trends across the eight sampling events (i.e., tendency to be removed, increase, decrease, or be found in the effluent only), along with quantification of ARGs of clinical concern, aided in identifying candidate ARGs for surveillance. Relative resistome risk characterization further provided a comprehensive metric for predicting the relative mobility of ARGs and likelihood of being carried in pathogens and can help to prioritize where to focus future monitoring and mitigation. Most antibiotics that were subject to regional resistance testing were also found in the WWTP, with the total antibiotic load decreasing by ∼40–50%, but no strong correlations were found between antibiotics and corresponding ARGs. Overall, this study provides insight into how metagenomic data can be collected and analyzed for surveillance of antibiotic resistance at WWTPs, suggesting that effluent is a beneficial monitoring point with relevance both to the local clinical condition and for assessing efficacy of wastewater treatment in reducing risk of disseminating antibiotic resistance. Published version