Decomposition and decoupling analysis of carbon dioxide emissions in African countries during 1984‒2014.

The potential for mitigating climate change is growing worldwide, with an increasing emphasis on reducing CO2 emissions and minimising the impact on the environment. African continent is faced with the unique challenge of climate change whilst coping with extreme poverty, explosive population growth and economic difficulties. CO2 emission patterns in Africa are analysed in this study to understand primary CO2 sources and underlying driving forces further. Data are examined using gravity model, logarithmic mean divisia index and Tapio's decoupling indicator of CO2 emissions from economic development in 20 selected African countries during 1984-2014. Results reveal that CO2 emissions increased by 2.11% (453.73 million ton) over the research period. Gravity centre for African CO2 emissions had shifted towards the northeast direction. Population and economic growth were primary driving forces of CO2 emissions. Industrial structure and emission efficiency effects partially offset the growth of CO2 emissions. The economic growth effect was an offset factor in central African countries and Zimbabwe due to political instability and economic mismanagement. Industrial structure and emission efficiency were insufficient to decouple economic development from CO2 emissions and relieve the pressure of population explosion on CO2 emissions in Africa. Thus, future efforts in reducing CO2 emissions should focus on scale-up energy-efficient technologies, renewable energy update, emission pricing and long-term green development towards sustainable development goals by 2030.

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features.

Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative ℓ 0 -norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 × 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations.

Factor decomposition of carbon emissions in Chinese megacities.

In this article, per capita urban carbon emissions were decomposed into manufacturing, transportation, and construction sectors using logarithmic mean Divisia index (LMDI) method. This new decomposition method can provide information about specific drivers of carbon emissions, including urban growth and resident living standards, rather than general demographic and economic factors identified by traditional methods. Using four Chinese megacities (Beijing, Tianjin, Shanghai, and Chongqing) as case studies, we analyzed the factors that influenced per capita carbon emissions from 2010 to 2015. The results showed that per capita carbon emissions increased in Tianjin and Chongqing whereas decreased in Beijing and Shanghai, and that manufacturing was a key driving force. In these four megacities, energy conservation strategies were successfully implemented despite poor energy structure optimization during 2010-2015. Development of manufacturing and improvement of resident living standards in the cities led to an increase in carbon emissions. The unique dual-core urban form of Tianjin might mitigate the increased carbon emissions caused by the transportation sector. Reductions in carbon emissions could be achieved by further optimizing energy structures, limiting the number of private cars, and controlling per capita construction.

SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles.

UNLABELLED: A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods. AVAILABILITY AND IMPLEMENTATION: The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy CONTACT: lars.juhl.jensen@cpr.ku.dk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RANKS: a flexible tool for node label ranking and classification in biological networks.

UNLABELLED: RANKS is a flexible software package that can be easily applied to any bioinformatics task formalizable as ranking of nodes with respect to a property given as a label, such as automated protein function prediction, gene disease prioritization and drug repositioning. To this end RANKS provides an efficient and easy-to-use implementation of kernelized score functions, a semi-supervised algorithmic scheme embedding both local and global learning strategies for the analysis of biomolecular networks. To facilitate comparative assessment, baseline network-based methods, e.g. label propagation and random walk algorithms, have also been implemented. AVAILABILITY AND IMPLEMENTATION: The package is available from CRAN: https://cran.r-project.org/ The package is written in R, except for the most computationally intensive functionalities which are implemented in C. CONTACT: valentini@di.unimi.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Measuring Urban Carbon Footprint from Carbon Flows in the Global Supply Chain.

A global multiregional input-output (MRIO) model was built for eight Chinese cities to track their carbon flows. For in-depth understanding of urban carbon footprint from the perspectives of production, consumption, and trade balance, four kinds of footprints and four redefined measurement indicators were calculated. From the global supply chain, urban carbon inflows from Mainland China were larger than outflows, while the carbon outflows to European, principal North American countries and East Asia were much larger than inflows. With the rapid urbanization of China, Construction was the largest consumer and Utilities was the largest producer. Cities with higher consumption (such as Dalian, Tianjin, Shanghai, and Beijing) should change their consumption patterns, while cities with lower production efficiency (such as Dalian, Shanghai, Ningbo, and Chongqing) should improve their technology. The cities of net carbon consumption tended to transfer carbon emissions out of them by trading in carbon-intensive products, while the cities of net carbon production tended to produce carbon-intensive products for nonlocal consumers. Our results indicated that urban carbon abatement requires not only rational consumption and industrial symbiosis at the city level, but also tighter collaboration along all stages of the global supply chain.

Graphene quantum dots coated VO2 arrays for highly durable electrodes for Li and Na ion batteries.

Nanoscale surface engineering is playing important role in enhancing the performance of battery electrode. VO2 is one of high-capacity but less-stable materials and has been used mostly in the form of powders for Li-ion battery cathode with mediocre performance. In this work, we design a new type of binder-free cathode by bottom-up growth of biface VO2 arrays directly on a graphene network for both high-performance Li-ion and Na-ion battery cathodes. More importantly, graphene quantum dots (GQDs) are coated onto the VO2 surfaces as a highly efficient surface "sensitizer" and protection to further boost the electrochemical properties. The integrated electrodes deliver a Na storage capacity of 306 mAh/g at 100 mA/g, and a capacity of more than 110 mAh/g after 1500 cycles at 18 A/g. Our result on Na-ion battery may pave the way to next generation postlithium batteries.

STRING v9.1: protein-protein interaction networks, with increased coverage and integration.

Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.

A flexible alkaline rechargeable Ni/Fe battery based on graphene foam/carbon nanotubes hybrid film.

The development of portable and wearable electronics has promoted increasing demand for high-performance power sources with high energy/power density, low cost, lightweight, as well as ultrathin and flexible features. Here, a new type of flexible Ni/Fe cell is designed and fabricated by employing Ni(OH)2 nanosheets and porous Fe2O3 nanorods grown on lightweight graphene foam (GF)/carbon nanotubes (CNTs) hybrid films as electrodes. The assembled f-Ni/Fe cells are able to deliver high energy/power densities (100.7 Wh/kg at 287 W/kg and 70.9 Wh/kg at 1.4 kW/kg, based on the total mass of active materials) and outstanding cycling stabilities (retention 89.1% after 1000 charge/discharge cycles). Benefiting from the use of ultralight and thin GF/CNTs hybrid films as current collectors, our f-Ni/Fe cell can exhibit a volumetric energy density of 16.6 Wh/l (based on the total volume of full cell), which is comparable to that of thin film battery and better than that of typical commercial supercapacitors. Moreover, the f-Ni/Fe cells can retain the electrochemical performance with repeated bendings. These features endow our f-Ni/Fe cells a highly promising candidate for next generation flexible energy storage systems.

Three-dimensional graphene foam supported Fe3O4 lithium battery anodes with long cycle life and high rate capability.

Fe3O4 has long been regarded as a promising anode material for lithium ion battery due to its high theoretical capacity, earth abundance, low cost, and nontoxic properties. However, up to now no effective and scalable method has been realized to overcome the bottleneck of poor cyclability and low rate capability. In this article, we report a bottom-up strategy assisted by atomic layer deposition to graft bicontinuous mesoporous nanostructure Fe3O4 onto three-dimensional graphene foams and directly use the composite as the lithium ion battery anode. This electrode exhibits high reversible capacity and fast charging and discharging capability. A high capacity of 785 mAh/g is achieved at 1C rate and is maintained without decay up to 500 cycles. Moreover, the rate of up to 60C is also demonstrated, rendering a fast discharge potential. To our knowledge, this is the best reported rate performance for Fe3O4 in lithium ion battery to date.

PM2.5 air quality and health gains in the quest for carbon peaking: A case study of Fujian Province, China.

China faces a dual challenge of improving air quality and reducing greenhouse gas (GHG) emissions. Stringent clean air actions gradually narrow the end-of-pipe (EOP) pollution control potential. Meanwhile, pursuing carbon peaking will reduce air pollution and health risks. However, the impact on air quality and health gains in individual Chinese provinces has not been assessed with a specific focus on local policies. Here, typical shared socio-economic pathways (SSPs) and local policies (i.e., business as usual, BAU; end-of-pipe controls, EOP; co-control mitigation, CCM) are combined to set three scenarios (i.e., BAU-SSP3, EOP-SSP4, CCM-SSP1). Under these three scenarios, we couple the Low Emissions Analysis Platform (LEAP) model, an air quality model and health risk assessment methodology to evaluate the characteristics of carbon peaking in Fujian Province. PM2.5 air quality and impacts on public health are assessed, using the metric of the deaths attributable to PM2.5 pollution (DAPP). The results show that energy-related CO2 emissions will only peak before 2030 in the CCM-SSP1 scenario. In this context, air pollutant emission pathways reveal that mitigation is limited under the EOP-SSP4 scenario, necessitating further mitigation under the CCM-SSP1 scenario. The annual average PM2.5 level is projected to be 16.5 µg·m-3 in 2035 with a corresponding decrease in DAPP of 297 (95 % confidence intervals: 217-308) compared with that of 2020. Despite the significant improvements in PM2.5 air quality and health gains under the CCM-SSP1 scenario, reaching the 5 µg·m-3 target of the World Health Organization (WHO) remains difficult. Furthermore, population aging will require stronger PM2.5 mitigation to enhance health gains. This study provides a valuable reference for other developing regions to co-control air pollution and GHGs.

Pathway for the low-carbon consumption pattern transition of residents in six eastern coastal provinces of China: using fuzzy-set qualitative comparative analysis with panel data.

China has embarked on society-wide emission reduction initiatives to tackle the growing decarbonization pressures on national economic and social development. The low-carbon consumption pattern transition of the residential sector is viewed as a crucial impetus that will drive society toward sustainable development. However, how such a consumption transition can be facilitated remains unclear. By adopting 2015-2019 provincial panel data, this study assesses the carbon emissions associated with the consumption patterns of residents in Eastern China and further explores the causal relationships between policy and socioeconomic factors as well as the formation of a low-carbon consumption base using fuzzy-set qualitative comparative analysis method (fsQCA). This study explores five equivalent recipes that achieve the expected low-carbon consumption base and then classifies them into market-driven (M1) and tertiary-driven (M2) recipes. Longitudinal analysis based on the between consistency (BECONS) and the within consistency (WICONS) values reveals that the tertiary-driven pathways remain highly stable across time, whereas those pathways that heavily rely on market interventions may be more applicable in certain cases. Accelerating the low-carbon consumption transition via the explored pathways is expected to exert pressure on the production transition, and this study provides suggestions for regions with varied development levels to balance the trade-off between China's social development and its decarbonization targets.

Decoupling for a greener future: a spatio-temporal analysis of CO2 emissions and economic growth.

Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.

A Balance between Transmembrane-Mediated ER/Golgi Retention and Forward Trafficking Signals in Glycophorin-Anion Exchanger-1 Interaction.

Anion exchanger-1 (AE1) is the main erythroid Cl-/HCO3- transporter that supports CO2 transport. Glycophorin A (GPA), a component of the AE1 complexes, facilitates AE1 expression and anion transport, but Glycophorin B (GPB) does not. Here, we dissected the structural components of GPA/GPB involved in glycophorin-AE1 trafficking by comparing them with three GPB variants-GPBhead (lacking the transmembrane domain [TMD]), GPBtail (mainly the TMD), and GP.Mur (glycophorin B-A-B hybrid). GPB-derived GP.Mur bears an O-glycopeptide that encompasses the R18 epitope, which is present in GPA but not GPB. By flow cytometry, AE1 expression in the control erythrocytes increased with the GPA-R18 expression; GYP.Mur+/+ erythrocytes bearing both GP.Mur and GPA expressed more R18 epitopes and more AE1 proteins. In contrast, heterologously expressed GPBtail and GPB were predominantly localized in the Golgi apparatus of HEK-293 cells, whereas GBhead was diffuse throughout the cytosol, suggesting that glycophorin transmembrane encoded an ER/Golgi retention signal. AE1 coexpression could reduce the ER/Golgi retention of GPB, but not of GPBtail or GPBhead. Thus, there are forward-trafficking and transmembrane-driven ER/Golgi retention signals encoded in the glycophorin sequences. How the balance between these opposite trafficking signals could affect glycophorin sorting into AE1 complexes and influence erythroid anion transport remains to be explored.

Dynamic characteristics and synergistic effects of ecosystem services under climate change scenarios on the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau (QTP) supplies many ecosystem services (ESs) that maintain local and global pan-Asian populations and ecosystems. The effects of climate change on ES provision in the QTP will have far-reaching impacts on the region and the many downstream ecosystems and countries that depend on ESs from the "Third Pole". This study undertook a systematic assessment of ES provision, trade-offs and synergies between four ESs (raw material provision, water yield, soil retention, and carbon storage) under future climate scenarios (representative concentration pathway). The results show that: (1) the total amount of the four ESs on the QTP is predicted to increase from 1980 to 2100 for three climate change scenarios. (2) The spatial pattern of ESs on the QTP will not change significantly in the future, and the grassland and forest ESs in the central and southern regions are predicted to increase significantly. (3) The synergistic interactions among ESs were generally consistent at three spatial scales (10 km (pixel), county and watershed scales), but with more significant synergistic effects at the watershed scale. This demonstrates the necessity for the examination of scale-dependent ES dynamics and interactions. This study will supply a reference for further research on long-term ES assessments, especially the dynamic ES changes and the spatial scale dependency of the ES interactions, and provide evidence-based strategies for formulating ecosystem management on the QTP under climate change.

Drivers of the increasing water footprint in Africa: The food consumption perspective.

In Africa, water resources pervade multiple sustainable development goals (SDGs), which mainly focus on eliminating poverty (SDG 1) and hunger (SDG 2), promoting good health and well-being (SDG 3) and supporting clean water and sanitation (SDG 6). Africa's water scarcity problems have been worsened by population growth and climate change. Agriculture is the largest consumer of water in Africa, and a clear understanding of the water-food nexus is necessary to effectively alleviate water-related pressures on food security. Water footprint (WF) accounts and decompositions provide insights into water management planning for policy-makers. We investigated the WF of food consumption from 2000 to 2018 in 23 African countries and used the logarithmic mean Divisia index (LMDI) to decompose its driving forces into consumption structure, per capita food consumption, water intensity and population effect. The WF of food consumption increased from 609.8 km3 in 2000 to 1212.9 km3 in 2018, with an average annual growth rate of 3.7%. The population effect contributed most to this change (64.6%), followed by per capita food consumption (28.3%) and consumption structure (7.1%). Cereals (46.7%) and livestock (24.4%) were the major contributors to the increase in the total WF. Our findings highlight that controlling population growth and improving water efficiency are effective measures to relieve water-related pressures on food consumption. However, a healthy dietary structure must also be promoted because Africa's current dietary energy level is below the global average. Moreover, nine countries in the research area have an inadequate supply of dietary energy; this will inevitably drive the WF of food, as calories increase and diets change. This study is helpful for understanding the water-food nexus in Africa and provides strategies to conserve water and enhance food production.

Efficient growth of horizontally aligned single-walled carbon nanotubes by chemical vapor deposition over MgO-supported bimetallic co-based catalysts.

A series of supported cobalt-based catalysts (Co-X, where X = Mn, V, W, Gd, Mo) has been investigated for the growth of single-walled carbon nanotubes via catalytic decomposition of CH4. At 850 degrees C, Mn, W and Gd promoted Co-catalysts produce SWNTs while Mo and V do not yield any SWNTs. Furthermore, Co-Gd catalysts produce high quality SWNTs with very few defects, and also narrow diameter and chirality distributions (approximately 1 nm). The growth of horizontally aligned SWNTs (approximately 1 mm long) using the Co-Gd catalyst is also demonstrated.

Environmental Footprints of High-Speed Railway Construction in China: A Case Study of the Beijing-Tianjin Line.

The environmental footprints of China's high-speed railway (HSR) have attracted much attention nationally and internationally. Although there is some research focusing on CO2 emissions, a comprehensive environmental impacts assessment of HSR construction is still lacking. In this study, the emissions of the Beijing-Tianjin intercity HSR line was calculated using a hybrid input-output life cycle assessment method to quantify the environmental impacts of HSR throughout its construction. The environmental footprints during the construction stage were analyzed in terms of different subsystems and sectors. The results showed that bridges contribute the largest environmental footprints at approximately 60%, followed by rail and electric multiple unit (EMU) systems. The top three sectors that contribute to pollutant emissions are the metal smelting and rolling industry, transport equipment manufacturing, and non-metallic mineral production. CO2 and NOx are the major pollutants directly emitted by site equipment operation. More chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and petroleum are emitted in EMU production than in rail construction, while NH3-N is emitted more in rails instead. Cd, Pb, As, and Hg are the significant pollutants in the metal smelting and rolling industry, whereas Cr, Cu, and Zn are the main heavy metal emissions in the transport equipment manufacturing sector. Heavy metals are the main types of environmental footprints in bridges, stations, and electric systems. Water pollutants are the main environmental impacts for rail and EMU systems, and the emissions of air pollutants are significant in subgrades. The production efficiency of upstream materials, desulfurization and denitration in fossil combustion, and the length of the bridge construction should be considered for an HSR under construction, in order to become environmentally friendly and sustainable.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome.

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

First-principles study of carbon nanotubes with bamboo-shape and pentagon-pentagon fusion defects.

A first-principles study of single-walled carbon nanotubes with bamboo-shape (BS) and pentagon-pentagon fusion defects was conducted. Sharp resonances occur on the BS-nanotubes as strong density of electronic states (DOS) localized at carbon atoms adjacent to the partitions, while at the partition the localized DOS was greatly depleted. A strong defect state at -0.1 eV below the Fermi level was generated and the band gap was narrowed for BS-(10, 0) nanotube. Sharp resonant states are observed in the valence and conduction bands of BS-(12, 0) nanotube. The resonant states are attributed to the pentagon defects as exemplified by the study of a (5, 5) nanotube with pentagon-pentagon fusion ring. The high chemical reactivity of the topological defects of the BS-nanotubes is correlated to the presence of localized resonant states.