The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Efforts to stabilize the global climate change while also continuing human development depend upon \"decoupling\" economic growth from fossil fuel CO2 emissions. However, evaluations of such decoupling have typically relied on production-based emissions, which do not account for emissions embodied in international trade. Yet international trade can greatly change emissions accounting and reshape the decoupling between emissions and economic growth. Here, we evaluate decoupling of economic growth from different accounts of emissions in each of the 159 countries and analyze the drivers of decoupling. We find that between 1995 and 2015, although 29 countries exhibited strong decoupling of territorial emissions (growing economies and decreasing emissions), only 19 countries achieved economic growth while their consumption-based emissions decreased. Most developed countries have achieved decoupling of emissions related to domestic goods and services, but have not achieved decoupling of emissions related to imported goods and services. The U-test confirms that the domestic component of consumption-based emissions exhibits a stronger decoupling trend from gross domestic product (GDP) growth than consumption-based emissions, and emissions from imports continue to rise with GDP per capita without a corresponding decline, providing a statistical validation of the decoupling analysis. Moreover, in the countries where economic growth and consumption-based emissions are most decoupled, a key driver is decreasing emissions intensity due to technological progress─and especially reductions in the intensity of imported goods and services. Our results reveal the importance of assessing decoupling using consumption-based emissions; successful decoupling may require international cooperation and coordinated mitigation efforts of trading partners.

Greenwashing is more virulent than ever. A profusion of environmental, social, and governance and net zero commitments are becoming fraught with questionable and misleading claims. At the same time, we are no closer to solving the pressing environmental and social issues of our time. In this review, we seek to examine this shift and summarize changes in greenwash research into three key phases: (a) 1.0 Static Communication; (b) 2.0 Dynamic Management; and (c) 3.0 Narratives about the Future. We analyze current key areas of developing literature and point to numerous open questions for future research. Next, we go beyond much of the published work to examine emerging tactics and lay out a forward-looking agenda for future research. We also propose a model of Corporate Miscommunication, integrating various streams in greenwash research. In doing so, we seek to lay a pathway for greenwashing researchers to finally find that elusive \"end\" to greenwashing.

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a feasible solution is graph knowledge distillation (KD), which can learn high-performance student Multi-layer Perceptrons (MLPs) to replace GNNs by mimicking the superior output of teacher GNNs. However, state-of-the-art graph knowledge distillation methods are mainly based on distilling deep features from intermediate hidden layers, this leads to the significance of logit layer distillation being greatly overlooked. To provide a novel viewpoint for studying logits-based KD methods, we introduce the idea of decoupling into graph knowledge distillation. Specifically, we first reformulate the classical graph knowledge distillation loss into two parts, i.e., the target class graph distillation (TCGD) loss and the non-target class graph distillation (NCGD) loss. Next, we decouple the negative correlation between GNN\'s prediction confidence and NCGD loss, as well as eliminate the fixed weight between TCGD and NCGD. We named this logits-based method Decoupled Graph Knowledge Distillation (DGKD). It can flexibly adjust the weights of TCGD and NCGD for different data samples, thereby improving the prediction accuracy of the student MLP. Extensive experiments conducted on public benchmark datasets show the effectiveness of our method. Additionally, DGKD can be incorporated into any existing graph knowledge distillation framework as a plug-and-play loss function, further improving distillation performance. The code is available at https://github.com/xsk160/DGKD.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

The stoichiometric ratio between seawater CO2 and dissolved oxygen (DO) during phytoplankton metabolism holds significant importance in evaluating ecological and biogeochemical processes. We collected high-resolution underway temperature, salinity, DO, and pH data in the East China Sea inner shelf in May 2017. Our results revealed high pH (8.36) and supersaturated DO (171 %) in the outer Changjiang Estuary, indicating the occurrence of an algal bloom event. They were significantly correlated with a regression slope of 0.0029, which roughly followed the Redfield ratio. In contrast, a much higher ratio (0.0088) manifested in a low-salinity patch on north of the Changjiang Estuary, featuring a pH of 8.40 and oxygen saturation of approximately 123 %. The substantially faster air-sea equilibrium rate of O2 than CO2 probably caused such decoupling, offering insight into the temporal evolutions of algal bloom. Theoretically, a steeper regression slope implies an earlier onset of algal bloom. An end-member mixing model was constructed to exclude the physical mixing influences on dissolved inorganic carbon (ΔDIC) and DO (ΔDO). Furthermore, we conducted simulations to explore the temporal variations of ΔDIC-ΔDO regression slope with time. Comparing slopes derived from simulation and mixing model suggested that the biological signal of the decoupled waters likely preceded our observations by 6-10 days. Satellite results captured high-Chl a waters southwest of the low-salinity patch a week before our observation, potentially transported northward by prevailing southwest wind. Given that oxygen and pH are frequently measured in aquatic environments, their combined assessment could be a valuable method for assessing temporal algal bloom dynamics.

Many recent studies have used boil-and-bite style instrumented mouthguards to measure head kinematics during impact in sports. Instrumented mouthguards promise greater accuracy than their predecessors because of their superior ability to couple directly to the skull. These mouthguards have been validated in the lab and on the field, but little is known about the effects of decoupling during impact. Decoupling can occur for various reasons, such as poor initial fit, wear-and-tear, or excessive impact forces. To understand how decoupling influences measured kinematic error, we fit a boil-and-bite instrumented mouthguard to a 3D-printed dentition mounted to a National Operating Committee on Standards for Athletic Equipment (NOCSAE) headform. We also instrumented the headform with linear accelerometers and angular rate sensors at its center of gravity (CG). We performed a series of pendulum impact tests, varying impactor face and impact direction. We measured linear acceleration and angular velocity, and we calculated angular acceleration from the mouthguard and the headform CG. We created decoupling conditions by varying the gap between the lower jaw and the bottom face of the mouthguard. We tested three gap conditions: 0 mm (control), 1.6 mm, and 4.8 mm. Mouthguard measurements were transformed to the CG and compared to the reference measurements. We found that gap condition, impact duration, and impact direction significantly influenced mouthguard measurement error. Error was higher for larger gaps and in frontal (front and front boss) conditions. Higher errors were also found in padded conditions, but the mouthguards did not collect all rigid impacts due to inherent limitations. We present characteristic decoupling time history curves for each kinematic measurement. Exemplary frequency spectra indicating characteristic decoupling frequencies are also described. Researchers using boil-and-bite instrumented mouthguards should be aware of their limitations when interpreting results and should seek to address decoupling through advanced post-processing techniques when possible.

The purpose of this work was to quantitatively characterize the effectiveness of oxidative phosphorylation uncouplers and decoupling agents in functionally active mitochondria, taking into account their content in the hydrophobic region of the inner membrane of these organelles. When conducting theoretical studies, it is accepted that uncouplers and decouplers occupy part of the volume of mitochondria to exhibit their activity, which is defined as the effective volume. The following quantities characterizing the action of these reagents are considered: (1) concentrations of reagents that cause double stimulation of mitochondrial respiration in state 4 ( C 200 ); (2) effective distribution coefficient ( E MW ) - the ratio of the amount of reagents in the effective volume of mitochondria and the water volume; (3) the relative amount of reagents associated with the effective volume of mitochondria ( U M / U T ); (4) specific activity of reagents localized in the effective volume of mitochondria ( A M ). We have developed methods for determining these values, based on an analysis of the dependence of the rate of mitochondrial respiration on the concentration of uncouplers and decoupling agents at two different concentrations of mitochondrial protein in the incubation medium. During experimental studies, we compared the effects of the classical protonophore uncouplers 2,4-dinitrophenol (DNP) and сarbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), the natural uncouplers lauric and palmitic acids, and the natural decouplers α,ω-tetradecanedioic (TDA) and α,ω-hexadecanedioic (HDA) acids that differ both in the structure of the molecule and in the degree of solubility in lipids. Using the developed methods, we have clarified the dependence of the degree of activity of these uncouplers and decoupling agents on the distribution of their molecules between the effective volume of mitochondria and the water volume.

A two-stage decoupling model based on an artificial neural network with polynomial regression is proposed for the six-component force sensor load decoupling problem in the case of multidimensional mixed loading. The six-dimensional load categorization stage model constructed in the first stage combines 63 load category label sets with a deep BP neural network. The six-dimensional load regression stage model was constructed by combining polynomial regression with a BP neural network in the second stage. Meanwhile, the six-component force sensor with a fiber Bragg grating (FBG) sensor as the sensitive element was designed, and the elastomer simulation and calibration experimental dataset was established to realize the validation of the two-stage decoupling model. The results based on the simulation data show that the accuracy of the classification stage is 93.65%. The MAPE for the force channel in the regression stage is 6.29%, and 3.24% for the moment channel. The results based on experimental data show that the accuracy of the classification stage is 87.80%. The MAPE for the force channel in the regression phase is 5.63%, and 4.82% for the moment channel.