Modeling the dynamic impacts of maritime network blockage on global supply chains.

Recent phenomena such as pandemics, geopolitical tensions, and climate change-induced extreme weather events have caused transportation network interruptions, revealing vulnerabilities in the global supply chain. A salient example is the March 2021 Suez Canal blockage, which delayed 432 vessels carrying cargo valued at $92.7 billion, triggering widespread supply chain disruptions. Our ability to model the spatiotemporal ramifications of such incidents remains limited. To fill this gap, we develop an agent-based complex network model integrated with frequently updated maritime data. The Suez Canal blockage is taken as a case study. The results indicate that the effects of such blockages go beyond the directly affected countries and sectors. The Suez Canal blockage led to global losses of about $136.9 ($127.5-$147.3) billion, with India suffering 75% of these losses. Global losses show a nonlinear relationship with the duration of blockage and exhibit intricate trends post blockage. Our proposed model can be applied to diverse blockage scenarios, potentially acting as an early-alert system for the ensuing supply chain impacts. Furthermore, high-resolution daily data post blockage offer valuable insights that can help nations and industries enhance their resilience against similar future events.

Ammonium Tetrakis(pentafluorophenyl)borate: Preparation and Application in Olefin Coordination Polymerization as the Cocatalyst Compound.

Metallocene catalysts have attracted much attention from academia and industry for their excellent catalytic activity in the field of olefin polymerization. Cocatalysts play a key role in metallocene catalytic systems, which can not only affect the overall catalytic activity, but also have an obvious influence on the structure and properties of the polymer. Although methylaluminoxane (MAO) is currently the most widely used cocatalyst, its price increases the production cost of polyolefin materials. Ammonium tetrakis(pentafluorophenyl)borate has shown excellent performance in polymerization, being one of the best substitutes for the traditional cocatalyst MAO. Compared with the main catalyst, whose composition and structure are relatively complex, the research on cocatalyst is very limited. This review mainly introduces the research history, preparation methods, and application progress in polymerization of ammonium tetrakis(pentafluorophenyl)borate, deepening our understanding of the role of cocatalyst in polymerization, with the hope of inspiring brand-new thinking on improving and enhancing the overall performance of catalyst systems.

Optimum investment strategy for hydrogen-based steelmaking project coupled with multiple uncertainties.

The large-scale application of hydrogen steelmaking technology is expected to substantially accelerate the decarbonization process of the iron and steel industry. However, hydrogen steelmaking projects are still in the experimental or demonstration stage, and scientific investment decision-making methods are urgently needed to support the large-scale development of the technology. When assessing the investment value, existing studies usually only consider the intrinsic project value under a specific pathway, while ignoring the option value under realistic multiple uncertainties in terms of technology, market, and policy, leading to an underestimation of the investment value. To address this issue, this study constructs a real options model to explore the optimal investment timing and revenue of the hydrogen steelmaking project, by taking into account multi-dimensional uncertainties stemming from price fluctuations in the steel market, the development of the carbon market, and technological advances. Additionally, the impacts of various subsidy policies on the investment strategy are also investigated. Least Squares Monte Carlo method is applied to overcome computational challenges posed by dynamic programming under multi-dimensional uncertainties. The results show that: (i) Investment is not recommended based on current crude steel price and hydrogen price. (ii) When the annual reduction rate of hydrogen price reaches 5%, the optimal investment timing would advance to 2036. (iii) On this basis, with the introduction of a 20% green hydrogen subsidy policy, the optimal investment timing would be further brought forward to 2033. The implementation of tax incentives would significantly increase the investment value. The investment value would surge from 170 million CNY to 262 million CNY as the tax rate decreases from 20% to zero. The findings could provide reasonable suggestions for investment decisions under realistic volatile environments, as well as scientific references for policy design, thus facilitating the large-scale and high-level development of hydrogen-based steelmaking technology.

Identifying potential genetic epistasis implicated in Alzheimer's disease via detection of SNP-SNP interaction on quantitative trait CSF Aß42.

Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aß42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aß42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.

Evaluating rare-earth constraints on wind power development under China's carbon-neutral target.

China proposed a target to achieve carbon neutrality before 2060. Wind power is crucial for mitigating climate change and achieving carbon neutrality. However, its development depends on the potential constraints of rare-earth elements. Therefore, first projecting the rare-earth demand for wind power equipment in the context of achieving carbon neutrality and identifying potential obstacles are necessary. However, the carbon-neutral pathway for China's power sector is unclear, let alone the corresponding rare-earth demand. Consequently, this study explores a potential cost-effective carbon-neutral pathway for China's power sector and quantifies the demand for rare-earth elements used for producing wind power equipment under different pathways, by integrating dynamic material flow analysis and a national energy technology model. The results showed that the rare-earth supply may be inadequate for wind power development in terms of achieving carbon neutrality in China, especially for dysprosium and terbium. To neutralise the carbon emissions of China's power sector, the cumulative rare-earth demand during 2021-2060 would be 222-434 kt, of which at most 1/3 could potentially be obtained by circular usage from end-of-life wind turbines. However, the existing low secondary recovery rate of rare-earth elements makes the available circular amounts very small. Shifting to a wind power market dominated by direct-drive turbines may increase the cumulative rare-earth demand by up to 34 %. Without material intensity reduction for the wind power technologies, an additional 38 % demand for rare-earth elements will occur, exacerbating the risk of shortage.

Approaching national climate targets in China considering the challenge of regional inequality.

Achievement of national climate targets and the corresponding costs would entirely depend on regional actions within the country. However, because of substantial inequalities and heterogeneities among regions, especially in developing economies, aggressive or uniform actions may exacerbate inequity and induce huge economic losses, which in turn challenges the national climate pledges. Hence, this study extends prior research by proposing economically optimal strategies that can achieve national climate targets and ensure the greatest local and national benefits as well as regional equality. Focusing on the biggest developing country China, we find this strategy can avoid up to 1.54% of cumulative GDP losses for approaching carbon neutrality, and more than 90% of regions would obtain economic gains compared either with existing independently launched targets or with the uniform strategy that all regions achieve peak carbon emissions before 2030. We also provide optimal carbon mitigation pathways to regional peak carbon, carbon intensity and energy consumption.

The global mismatch between equitable carbon dioxide removal liability and capacity.

Limiting climate change to 1.5°C and achieving net-zero emissions would entail substantial carbon dioxide removal (CDR) from the atmosphere by the mid-century, but how much CDR is needed at country level over time is unclear. The purpose of this paper is to provide a detailed description of when and how much CDR is required at country level in order to achieve 1.5°C and how much CDR countries can carry out domestically. We allocate global CDR pathways among 170 countries according to 6 equity principles and assess these allocations with respect to countries' biophysical and geophysical capacity to deploy CDR. Allocating global CDR to countries based on these principles suggests that CDR will, on average, represent ∼4% of nations' total emissions in 2030, rising to ∼17% in 2040. Moreover, equitable allocations of CDR, in many cases, exceed implied land and carbon storage capacities. We estimate ∼15% of countries (25) would have insufficient land to contribute an equitable share of global CDR, and ∼40% of countries (71) would have insufficient geological storage capacity. Unless more diverse CDR technologies are developed, the mismatch between CDR liabilities and land-based CDR capacities will lead to global demand for six GtCO2 carbon credits from 2020 to 2050. This demonstrates an imperative demand for international carbon trading of CDR.

MR Image Harmonization with Transformer.

Many clinical applications require medical image harmonization to combine and normalize images from different scanners or protocols. This paper introduces a Transformer-based MR image harmonization method. Our proposed method leverages the self-attention mechanism of the Transformer to learn the complex relationships between image patches and effectively transfer the imaging characteristics from a source image domain to a target image domain. We evaluate our approach to state-of-the-art methods using a publicly available dataset of brain MRI scans and show that it provides superior quantitative metrics and visual quality. Furthermore, we demonstrate that the proposed approach is highly resistant to fluctuations in image modality, resolution, and noise. Overall, the experiment results indicate that our approach is a promising method for medical image harmonization that can improve the accuracy and reliability of automated analysis and diagnosis in clinical settings.

Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort.

Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.

Porphyromonas gingivalis exacerbates alcoholic liver disease by altering gut microbiota composition and host immune response in mice.

AIM: Porphyromonas gingivalis (P. gingivalis), a major periodontal pathogen, increases the risk of systemic diseases. P. gingivalis infection is closely associated with alcoholic liver disease (ALD), but the underlying mechanism remains unclear. We aimed to investigate the role of P. gingivalis in the pathogenesis of ALD. MATERIALS AND METHODS: An ALD mouse model was established using a Lieber-DeCarli liquid diet, and C57BL/6 mice were treated with P. gingivalis to detect the pathological indicators of ALD. RESULTS: Oral administration of P. gingivalis exacerbated alcohol-induced alterations in the gut microbiota, leading to gut barrier dysfunction and inflammatory response and disruption of the T-helper 17 cell/T-regulatory cell ratio in the colon of ALD mice. Furthermore, P. gingivalis worsened liver inflammation in ALD mice by increasing the protein expression of toll-like receptor 4 (TLR4) and p65, increasing the mRNA expression of interleukins-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) and up-regulating the transforming growth factor-beta 1 (TGF-ß1) and galectin-3 (Gal-3) production. CONCLUSIONS: These results indicate that P. gingivalis accelerates the pathogenesis of ALD via the oral-gut-liver axis, necessitating a new treatment strategy for patients with ALD complicated by periodontitis.

Allocating China's CO2 Emissions Based on Economic Welfare Gains from Environmental Externalities.

To achieve carbon neutrality (i.e., net zero carbon emissions) by 2060, China must make significant changes in its socioeconomic systems, including appropriately allocating emissions responsibility. Traditional methods of delineating responsibilities (such as production-based and consumption-based accounting) can lead to double counting when applied simultaneously and therefore difficulty in determining responsibilities of different agents. An alternative approach based on economic welfare gains from environmental externalities has been refined, ensuring that the responsibilities of consumers and producers add up to the total emissions. The application of this approach to 48 countries and 31 Chinese provinces reveals that regions with less elastic supply and demand, such as Hebei in China and Russia, have higher responsibilities. Furthermore, larger externalities associated with unitary product value shift the burden of obligations from producers to consumers. Regions with high levels of wealth and carbon-intensive imports, such as Zhejiang and Guangdong in China, as well as the United States, typically have higher consumer-based accounting (CBA) emissions than production-based accounting (PBA) emissions and, as a result, redistributed responsibilities between PBA and CBA emissions. The new distribution results vary significantly from PBA or CBA emissions, indicating opportunities for more comprehensive and accessible policy goals.

Fe-Catalyzed Difunctionalization of Aryl Titanates Enabled by Fe/Ti Synergism.

Fe-catalyzed difunctionalization of aryl titanates via double C-H activation has been developed, where aryl titanates were arylated via ortho C-H activation, followed by ipso electrophilic trapping of the C-Ti bond. The ortho C-H arylation should be promoted by a 1,2-Fe/Ti synergistic heterobimetallic arylene intermediate and represents an ortho C-H ferration directed by a readily transformable C-Ti group. Common benzamides, esters, and nitriles function as arylating reagents, which involves another ortho C-H activation directed by these functionalities.

Optimal modeling of anti-breast cancer candidate drugs screening based on multi-model ensemble learning with imbalanced data.

The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach.

Extreme temperatures, mortality, and adaptation: Evidence from the county level in China.

Extreme temperatures are known to cause adverse health outcomes. Yet knowledge on the magnitude of this effect in developing countries is limited due to data availability and reliability issues. Collecting data for 2872 counties in China, we estimate the effects of daily temperatures on the monthly mortality rate. The results indicate that an additional day for which the maximum temperature is 38°C or above on average increases the monthly mortality rate by about 1.7% relative to if that day's maximum temperature had been in the range 16-21°C. This is after deducting deaths harvested from the subsequent month. Higher gross domestic product per capita at the county level is associated with lower mortality effects of hot and cold days. Improved dwelling conditions are found to be associated with a lower mortality effect of hot days and improved local healthcare infrastructure to be associated with a lower mortality effect of cold days. In the absence of strong adaptation efforts, the estimates suggest net upward pressure on annual mortality rates over coming decades in many populous counties, especially under more extreme climate change scenarios.

Optimizing the rolling out plan of China's carbon market.

Although China has developed the world's largest carbon emissions trading scheme (ETS), there is no official documentation explaining how the current sectoral coverage plan was determined and what sectoral rollout plan is preferred. Here, we contribute to the policy development of the world's largest carbon market by suggesting a priority list of industries be covered in the ETS. We estimated marginal abatement cost curves using a database of more than two million firms covering over 500 four-digit industries that account for more than 97% of total industrial emissions, and simulating various carbon market scenarios including thermal power, 13 designated, and an additional 50 industries that have high emissions or are covered in other ETSs. Our analysis suggests that the cement industry should be the next sector to be included in China's ETS. In our revised list, the average abatement cost can be reduced by 39.5-78.3% compared with the business-as-usual scenario.

Synergism of Fe/Ti Enabled Regioselective Arene Difunctionalization.

Regioselective difunctionalization of arenes remains a long-standing challenge in organic chemistry. We report a novel and general Fe/Ti synergistic methodology for regioselective synthesis of various polysubstituted arenes through either E/E' or Nu/E ortho difunctionalizations of arenes. Preliminary results showed that an unprecedented 1,2-Fe/Ti heterobimetallic arylene intermediate bearing two distinct C-M bonds is essential to the regioselective difunctionalization.

Nexus between green financing, renewable energy generation, and energy efficiency: empirical insights through DEA technique.

The study aims to test the nexus of green financing with renewable electricity generation and energy efficiency. The study used data envelopment analysis (DEA) technique during the year of 2016 to 2020 in developed and developing countries. The findings show that there is a 24% possibility of worldwide rise in expenditures in renewable energy through energy efficiency projects and probably could fall around 17% much further in 2017 and 2018. This may jeopardize the Sustainable Development Goals (SDGs) and the Paris climate change agreement. Lack of access to private financing slows the development of green initiatives. Now that sustainable energy is not about science and technology, it is all about getting financing in developed and developing countries. As policy measure, the study suggested to value environmental initiatives, like other infrastructure initiatives, for greater electricity generation and energy efficiency in developed and developing countries. Such infrastructural projects need long-term financing and capital intensiveness. It is further suggested to sustain growth, development, and energy poverty reduction, and around $26 trillion would be required, in terms of green financing, in the developed and developing countries alone by the year 2030 to enhance energy efficiency. To achieve energy sustainability goals in developed and developing countries, recent research suggested some policy implication considering the post COVID-19 time. If such policy implications are implemented successfully, there are chances that green financing would make energy generation and energy efficiency effective.

The Multiple Influences of Natural Farming Environment on the Cultured Population Behavior of Kuruma Prawn, Penaeus japonicus.

Recent years have witnessed a tremendous development in shrimp farming around the world, which, however, has raised a variety of issues, possibly due to a lack of knowledge of shrimp behavior in farms. This study focused on the relationship between shrimp behavior and the various factors of natural farming environment through situ surveys, as distinguished from the majority of laboratory studies on shrimp behavior. In the survey, the behaviors of kuruma prawn (Penaeus japonicus) were investigated in the groups of swimming in the water, crawling on the sand, resting on the sand, and hiding in the sand, followed by the quantification of the sex ratio, water quality, density, and light intensity. The results showed the average proportions of resting, hiding, crawling, and swimming activities of 69.87%, 20.85%, 8.24%, and 1.04%, respectively, of P. japonicus. The behavior of hiding, resting, and crawling is significantly affected by the sex ratio of the shrimp (p < 0.05). The proportions of hiding behavior exhibited a negative connection with density and a positive connection with light intensity, while the proportions of resting behavior showed the opposite according to both Pearson correlation analysis and multiple linear regression analysis. The light intensity was the only factor that significantly influenced the swimming behavior, in which the probability of the swimming behavior was reduced from 48% to 5% when light intensity varied from 0 to 10 lx, as determined by the generalized linear model. It could be speculated that P. japonicus prefers a tranquil environment. Female shrimp might exhibit less aggression and more adventure compared to male shrimp. The findings suggested light intensity, followed by density, as the most crucial element influencing the behavior of P. japonicus in the culture environment. These findings will contribute to the comprehension of the behavior of P. japonicus and provide a novel perspective for the formulation of its culture management strategy.