Implicitly Restarted Generalized Second-order Arnoldi Type Algorithms for the Quadratic Eigenvalue Problem

We investigate the generalized second-order Arnoldi (GSOAR) method, a generalization of the SOAR method proposed by Bai and Su [{\em SIAM J. Matrix Anal. Appl.}, 26 (2005): 640--659.], and the Refined GSOAR (RGSOAR) method for the quadratic eigenvalue problem (QEP). The two methods use the GSOAR procedure to generate an orthonormal basis of a given generalized second-order Krylov subspace, and with such basis they project the QEP onto the subspace and compute the Ritz pairs and the refined Ritz pairs, respectively. We develop implicitly restarted GSOAR and RGSOAR algorithms, in which we propose certain exact and refined shifts for respective use within the two algorithms. Numerical experiments on real-world problems illustrate the efficiency of the restarted algorithms and the superiority of the restarted RGSOAR to the restarted GSOAR. The experiments also demonstrate that both IGSOAR and IRGSOAR generally perform much better than the implicitly restarted Arnoldi method applied to the corresponding linearization problems, in terms of the accuracy and the computational efficiency.Comment: 30 pages, 6 figure

Projected Urbanization Impacts on Surface Climate and Energy Budgets in the Pearl River Delta of China

The climate impacts of future urbanization in the Pearl River Delta (PRD) region in China were simulated with the Dynamics of Land Systems (DLS) model and the Weather Research and Forecasting (WRF) model in this study. The land use and land cover data in 2000 and 2020 were simulated with the DLS model based on the regional development planning. Then the spatial and temporal changes of surface air temperature, ground heat flux, and regional precipitation in 2020 were quantified and analyzed through comparing simulation results by WRF. Results show that the built-up land will become the dominant land use type in the PRD in 2020. Besides, the near-surface air temperature shows an increasing trend on the whole region in both summer and winter, but with some seasonal variation. The urban temperature rise is more apparent in summer than it is in winter. In addition, there is some difference between the spatial pattern of precipitation in summer and winter in 2020; the spatial variation of precipitation is a bit greater in summer than it is in winter. Results can provide significant reference for the land use management to alleviate the climate change

Electro-Chemo-Mechanical Failure of Solid Electrolytes Induced by Growth of Internal Lithium Filaments

Growth of lithium (Li) filaments within solid electrolytes, leading to mechanical degradation of the electrolyte and even short circuit of the cell under high current density, is a great barrier to commercialization of solid-state Li-metal batteries. Understanding of this electro-chemo-mechanical phenomenon is hindered by the challenge of tracking local fields inside the solid electrolyte. Here, a multiphysics simulation aiming to investigate evolution of the mechanical failure of the solid electrolyte induced by the internal growth of Li is reported. Visualization of local stress, damage, and crack propagation within the solid electrolyte enables examination of factors dominating the degradation process, including the geometry, number, and size of Li filaments and voids in the electrolyte. Relative damage induced by locally high stress is found to preferentially occur in the region of the electrolyte/Li interface having great fluctuations. A high number density of Li filaments or voids triggers integration of damage and crack networks by enhanced propagation. This model is built on coupling of mechanical and electrochemical processes for internal plating of Li, revealing evolution of multiphysical fields that can barely be captured by the state-of-the-art experimental techniques. Understanding mechanical degradation of solid electrolytes with the presence of Li filaments paves the way to design advanced solid electrolytes for future solid-state Li-metal batteries

Role of Li-Ion Depletion on Electrode Surface: Underlying Mechanism for Electrodeposition Behavior of Lithium Metal Anode

The application of lithium metal as an anode material for next generation high energy-density batteries has to overcome the major bottleneck that is the seemingly unavoidable growth of Li dendrites caused by non-uniform electrodeposition on the electrode surface. This problem must be addressed by clarifying the detailed mechanism. In this work the mass-transfer of Li-ions is investigated, a key process controlling the electrochemical reaction. By a phase field modeling approach, the Li-ion concentration and the electric fields are visualized to reveal the role of three key experimental parameters, operating temperature, Li-salt concentration in electrolyte, and applied current density, on the microstructure of deposited Li. It is shown that a rapid depletion of Li-ions on electrode surface, induced by, e.g., low operating temperature, diluted electrolyte and a high applied current density, is the underlying driving force for non-uniform electrodeposition of Li. Thus, a viable route to realize a dendrite-free Li plating process would be to mitigate the depletion of Li-ions on the electrode surface. The methodology and results in this work may boost the practical applicability of Li anodes in Li metal batteries and other battery systems using metal anodes

Biodiversity Impact Assessment Considering Land Use Intensities and Fragmentation

Land use is a major threat to terrestrial biodiversity. Life cycle assessment is a tool that can assess such threats and thereby support environmental decision-making. Within the Global Guidance for Life Cycle Impact Assessment (GLAM) project, the Life Cycle Initiative hosted by UN Environment aims to create a life cycle impact assessment method across multiple impact categories, including land use impacts on ecosystem quality represented by regional and global species richness. A working group of the GLAM project focused on such land use impacts and developed new characterization factors to combine the strengths of two separate recent advancements in the field: the consideration of land use intensities and land fragmentation. The data sets to parametrize the underlying model are also updated from previous models. The new characterization factors cover five species groups (plants, amphibians, birds, mammals, and reptiles) and five broad land use types (cropland, pasture, plantations, managed forests, and urban land) at three intensity levels (minimal, light, and intense). They are available at the level of terrestrial ecoregions and countries. This paper documents the development of the characterization factors, provides practical guidance for their use, and critically assesses the strengths and remaining shortcomings

Exploring the complex relationship between gut microbiota and risk of colorectal neoplasia using bidirectional Mendelian Randomization analysis

Background: Human gut microbiome has complex relation-ships with the host, contributing to metabolism, immunity, and carcinogenesis. Methods: Summary-level data for gut microbiota and metabo-lites were obtained from MiBioGen, FINRISK and human meta-bolome consortia. Summary-level data for colorectal cancer were derived from a genome-wide association study meta-analysis. In forward Mendelian randomization (MR), we employed genetic instrumental variables (IV) for 24 gut microbiota taxa and six bacterial metabolites to examine their causal relationship with colorectal cancer. We also used a lenient threshold for nine apriori gut microbiota taxa as secondary analyses. In reverse MR, we explored association between genetic liability to colorectal neoplasia and abundance of microbiota studied above using 95, 19, and 7 IVs for colorectal cancer, adenoma, and polyps, respectively. Results: Forward MR did not find evidence indicating causal relationship between any of the gut microbiota taxa or six bacterial metabolites tested and colorectal cancer risk. However, reverse MR supported genetic liability to colorectal adenomas was causally related with increased abundance of two taxa: Gammaproteobacteria (b = 0.027, which represents a 0.027 increase in log-transformed relative abundance values of Gam-maproteobacteria for per one-unit increase in log OR of adenoma risk; P = 7.06x10-8), Enterobacteriaceae (b = 0.023, P = 1.29x10-5). Conclusions: We find genetic liability to colorectal neoplasia may be associated with abundance of certain microbiota taxa. It is more likely that subset of colorectal cancer genetic liability variants changes gut biology by influencing both gut microbiota and colo-rectal cancer risk.Impact: This study highlights the need of future complemen-tary studies to explore causal mechanisms linking both host genetic variation with gut microbiome and colorectal cancer susceptibility

Fenofibrate Inhibits Cytochrome P450 Epoxygenase 2C Activity to Suppress Pathological Ocular Angiogenesis

Neovascular eye diseases including retinopathy of prematurity, diabetic retinopathy and age-related-macular-degeneration are major causes of blindness. Fenofibrate treatment in type 2 diabetes patients reduces progression of diabetic retinopathy independent of its peroxisome proliferator-activated receptor (PPAR)α agonist lipid lowering effect. The mechanism is unknown. Fenofibrate binds to and inhibits cytochrome P450 epoxygenase (CYP)2C with higher affinity than to PPARα. CYP2C metabolizes ω-3 long-chain polyunsaturated fatty acids (LCPUFAs). While ω-3 LCPUFA products from other metabolizing pathways decrease retinal and choroidal neovascularization, CYP2C products of both ω-3 and ω-6 LCPUFAs promote angiogenesis. We hypothesized that fenofibrate inhibits retinopathy by reducing CYP2C ω-3 LCPUFA (and ω-6 LCPUFA) pro-angiogenic metabolites. Fenofibrate reduced retinal and choroidal neovascularization in PPARα-/-mice and augmented ω-3 LCPUFA protection via CYP2C inhibition. Fenofibrate suppressed retinal and choroidal neovascularization in mice overexpressing human CYP2C8 in endothelial cells and reduced plasma levels of the pro-angiogenic ω-3 LCPUFA CYP2C8 product, 19,20-epoxydocosapentaenoic acid. 19,20-epoxydocosapentaenoic acid reversed fenofibrate-induced suppression of angiogenesis ex vivo and suppression of endothelial cell functions in vitro. In summary fenofibrate suppressed retinal and choroidal neovascularization via CYP2C inhibition as well as by acting as an agonist of PPARα. Fenofibrate augmented the overall protective effects of ω-3 LCPUFAs on neovascular eye diseases

Photoreceptor glucose metabolism determines normal retinal vascular growth

Abstract The neural cells and factors determining normal vascular growth are not well defined even though vision‐threatening neovessel growth, a major cause of blindness in retinopathy of prematurity (ROP) (and diabetic retinopathy), is driven by delayed normal vascular growth. We here examined whether hyperglycemia and low adiponectin (APN) levels delayed normal retinal vascularization, driven primarily by dysregulated photoreceptor metabolism. In premature infants, low APN levels correlated with hyperglycemia and delayed retinal vascular formation. Experimentally in a neonatal mouse model of postnatal hyperglycemia modeling early ROP, hyperglycemia caused photoreceptor dysfunction and delayed neurovascular maturation associated with changes in the APN pathway; recombinant mouse APN or APN receptor agonist AdipoRon treatment normalized vascular growth. APN deficiency decreased retinal mitochondrial metabolic enzyme levels particularly in photoreceptors, suppressed retinal vascular development, and decreased photoreceptor platelet‐derived growth factor (Pdgfb). APN pathway activation reversed these effects. Blockade of mitochondrial respiration abolished AdipoRon‐induced Pdgfb increase in photoreceptors. Photoreceptor knockdown of Pdgfb delayed retinal vascular formation. Stimulation of the APN pathway might prevent hyperglycemia‐associated retinal abnormalities and suppress phase I ROP in premature infants

Quantifying Earth system interactions for sustainable food production via expert elicitation

Several safe boundaries of critical Earth system processes have already been crossed due to human perturbations; not accounting for their interactions may further narrow the safe operating space for humanity. Using expert knowledge elicitation, we explored interactions among seven variables representing Earth system processes relevant to food production, identifying many interactions little explored in Earth system literature. We found that green water and land system change affect other Earth system processes strongly, while land, freshwater and ocean components of biosphere integrity are the most impacted by other Earth system processes, most notably blue water and biogeochemical flows. We also mapped a complex network of mechanisms mediating these interactions and created a future research prioritization scheme based on interaction strengths and existing knowledge gaps. Our study improves the understanding of Earth system interactions, with sustainability implications including improved Earth system modelling and more explicit biophysical limits for future food production

Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China

Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km2 and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants