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This paper provides evidence of informed trading by individual investors around earnings announcements using a unique data set of NYSE stocks. We show that intense aggregate individual investor buying (selling) predicts large positive (negative) abnormal returns on and after earnings announcement dates. We decompose abnormal returns following the event into information and liquidity provision components, and show that about half of the returns can be attributed to private information. We also find that individuals trade in both return-contrarian and news-contrarian manners after earnings announcements. The latter behavior has the potential to slow the adjustment of prices to earnings news.

Well-compatible with current industrial manufacturing and diverse applications, interfacial polymerization (IP) and IP-based polyamide nanofiltration membranes have evoked extensive research. Wherein, aside from tailoring pore sizes, regulating charged features of membranes remains challenging. Re-visiting principles of interfacial amide condensation, HCl byproducts are found exerting disparate effects in interfacial diffusion and ensuing amidation reaction. Exemplified by classic aqueous monomers–piperazine, H + byproducts or equivalents, backflowing from IP zone and associating with amine groups of up-migrating piperazine or surrounding oligomers to alleviate the amidation, unexpectedly escort more amines to terminate polyamide chains for enhanced positive moieties. Resulting NF membranes synergize steric and charge effects for high Li + /Mg 2+ selectivity of ~68 concurrent with water permeance of ~9 L m − 2 h −1 bar −1 , superior in polyamide membranes. Hence, backflow H + from the organic phase, typically cleared away in routine IP, is developed as the novel strategy to configure spatial charges of resulting membranes, further expanding IP’s versatility in fabricating functional membranes. Interfacial polymerization is widely used to design nanofiltration membranes, though it is challenging to tailor pore size and charged features of the membrane. Here, the authors use PCl3 and equivalents to control interfacial polymerization to optimize performance.

In recent years, a growing body of research has focused on the environment and economic growth nexus. However, there has not been much research done on how environmentally friendly technologies and institutional quality affect pollution levels. It is found that in developed countries, the rate of environmental deterioration has slowed thanks to more sustainable environmental regulations, advances in technology, and improvements in the quality of institutions. In contrast, limited modern technology in developing nations has resulted in havens of high carbon emissions. Therefore, the current research tried to analyze the environmental quality by using green technologies (GT), institutional quality (IQ), and energy efficiency (EE) as independent variables. In this study, we utilized data from 1995 to 2019 from BRICS countries to estimate long-term and short-term relationships. Used second-generation econometric techniques indicated that IQ, GT, and EE repair reduced environmental damage. The EKC does not exist, which means pollution in selected countries will improve with an expansion in economic activities. In the long term, a reform in institutions and more spending are required on green technologies to secure a sustainable future in BRICS countries. Results hold up when it comes to policy implications.

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performance on a single GPU that rivals the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that OpenABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates.

This review seeks to provide a better understanding of energy used in the urban water system in China. Electricity is a major contributor to the environmental impact of water supply and wastewater treatment, particularly in countries like China where electricity is largely generated using coal and has a significant impact on greenhouse gas emissions. Electricity use can also constitute one of the main costs for water and wastewater companies. China is an important country for the study of energy for water, particularly in urban areas where population is rapidly increasing. China's daily wastewater treatment capacity has increased dramatically over the last decade and a half, and energy use for both wastewater treatment and potable water supply has grown significantly. This paper deals with the challenge of energy for water in China. It reviews the growing body of work on energy for conventional water supply and wastewater treatment in urban China. The review covers energy for all parts of conventional water supply and wastewater treatment, including energy for sourcing, treating and distributing groundwater and surface water, and energy for primary and secondary treatment and sludge treatment and disposal. This review seeks to provide a better understanding of energy used in the urban water system in China. The review covers energy for sourcing, treating and distributing conventional water sources, and energy for treatment of wastewater and sludge. It draws conclusions on which areas should be the focus of efforts to reduce or recover energy.

Heavy metals (HMs) contamination and vegetation destruction in the mining area caused by mining activities are severely increasing. It is urgent to restore vegetation and stabilize HMs. In this study, we compared the ability of HMs phytoextraction/phytostabilization of three dominant plants, including Artemisia argyi (LA) , Miscanthus floridulus (LM), and Boehmeria nivea (LZ) in a lead-zinc mining area in Huayuan County (China). We also explored the role of the rhizosphere bacterial community in assisting phytoremediation using 16S rRNA sequencing technology. Bioconcentration factor (BCF) and translocation factor (TF) analysis showed that LA preferred accumulating Cd, LZ preferred accumulating Cr and Sb, and LM preferred accumulating Cr and Ni. Significant ( p < 0.05) differences were found among the rhizosphere soil microbial communities of these three plants. The key genera of LA were Truepera and Anderseniella , that of LM were Paracoccus and Erythrobacter , and of LZ was Novosphingobium . Correlation analysis showed some rhizosphere bacterial taxa (e.g., Actinomarinicola , Bacillariophyta and Oscillochloris ) affected some soil physicochemical parameters (e.g., organic matter and pH) of the rhizosphere soil and enhanced the TF of metals. Functional prediction analysis of soil bacterial community showed that the relative abundances of genes related to the synthesis of some proteins (e.g., manganese/zinc-transporting P-type ATPase C, nickel transport protein and 1-aminocyclopropane-1-carboxylate deaminase) was positively correlated with the phytoextraction/phytostabilization capacity of plants for heavy metals. This study provided theoretical guidance on selecting appropriate plants for different metal remediation applications. We also found some rhizosphere bacteria might enhance the phytoremediation of multi-metals, which could provide a reference for subsequent research.

The frequency domain analysis (FDA) offers greater computational efficiency than the method of characteristic (MOC) in simulating transient flow in pressurized pipes. However, its accuracy is hindered by linearisation errors. Violations of the assumptions for linearisation in friction term and valve equations during water distribution systems (WDSs) burst simulations make the FDA results meaningless. Linearisation procedures are modified as follows using the Heaviside property of pipeline bursts to address this problem: (1) the linearization of the friction term is adjusted, and (2) the valve equation is approximated using a three-step approach. The higher-order term dropped by the original FDA is linearly approximated to achieve better accuracy. The modified FDA is compared to the MOC in a real-life WDS by numerical experiment. Excellent precision can be observed even for a highly nonlinear case where the burst flow is 20% of the initial total demand. The simulation time is significantly shorter than when using the MOC. The proposed modification dramatically improves the applicability of the FDA for solving the nonlinear error issue during the simulation of the pipeline burst. This result implies the potential for its application in quick inverse analysis of pipeline bursts.

This review systematically summarizes the research progress of ketamine and its interaction with specific brain regions in the context of neuropathic pain comorbid with depression. As a non-competitive inhibitor of N-methyl-D-aspartate (NMDA) receptors, Ketamine exerts complex mechanisms and controversial topic. The structure and function of NMDA receptors, as well as the binding sites of ketamine, are summarized, and the hypothesis of disinhibition and neuroplasticity of ketamine's antidepressant effect is elaborated. The mechanism of key brain regions, such as hippocampus and anterior cingulate cortex, is discussed in detail, and the antidepressant effect of ketamine is explored from the perspective of transcriptomics. Finally, this review integrates bioinformatics, molecular biology and other interdisciplinary approaches to elucidate the therapeutic effects and potential mechanisms of ketamine in treating neuropathic pain and depression comorbidity, providing a comprehensive theoretical basis, new directions for subsequent research and novel insights for the clinical application of ketamine.

AQUA carries a long tradition: the first issue was published in 1951. Today, 70 years later, we relaunch AQUA: Water Infrastructure, Ecosystems and Society with a renewed focus. Water plays a key role in the transition to a sustainable world. To mark the transition, we have invited thought-leaders in the water supply base to share their visions for a sustainable future in water. The challenge has been open-ended: ‘If you carry in you a vision for such a future and can point at some of the elements we need to put in place for this to succeed, we invite you to submit a visionary paper.’ This has resulted in an issue full of visions, hope and ideas.

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.