Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study.

Understanding the nexus of land use and water quality can potentially underline the influences within the groundwater management. The study envisages land use-specific qualitative assessment of the groundwater resources in Ghaziabad district, in western Uttar Pradesh, India. For encountering the relative impacts of land use on the groundwater quality, chemometric analysis has been employed to apportion the pollution sources. The integration of quality parameters, in the information entropy index modeling, has segregated the quality classes and visualized the seasonal suitability trends as per potability standards along with non-carcinogenic health hazard risk assessment (HHRA). The qualitative assessment of the groundwater resources, along with spatial distribution, has deciphered a polluting impact, specifically in western and south western parts of district, and observed the linkages with direct and indirect discharges/seepages from densely populated residential and industrial land use types localized in urbanized areas. Statistically significant annual and seasonal variations have been found exclusively for EC, Mg2+, F-, Cd, Cr(total), Ni, and Pb which inferred variable concentrations, whereas land use types showed a non-significant variation within groundwater quality. Chemometric-based source apportioning and hierarchical cluster analysis (HCA) have derived salinization and enrichment of dissolved salts, arising from mixed sources and contributes to metal pollution, i.e., mainly from anthropogenic sources. Information EWQI derived poor to extremely poor category represented degraded potability specifically for fewer sites located within western and southern parts on the Yamuna-Hindon flood plains for limited sites of residential, industrial, and agricultural in an urbanized region. However, majority of the samples fall under excellent to good groundwater quality, recommendable in the north and north-eastern (peri-urban) regions. Non-carcinogenic HHRA has shown that majority of the samples categorized under unsafe value for hazard index (HI > 1), for females and children and thus, presumed probable health hazard risk from metal groundwater pollution in south-western part, eastern, and northern regions.

Constructing an evaluation model for the comprehensive level of sustainable development of provincial competitive sports in China based on DPSIR and MCDM.

This study focuses on the objective assessment of sport development in socio-economic environments, considering the challenges faced by the industry. These challenges include disparities in regional investments, limited market participation, slow progress towards sports professionalization, and insufficient technological innovations. To tackle these challenges, we suggest implementing an integrated evaluation model that follows the DPSIR (Drivers, Pressures, States, Impacts, Responses) framework and incorporates comprehensive socioeconomic indicators. Subsequently, we utilized the Entropy power method and TOPSIS (Order Preference Technique for Similarity to an Ideal Solution, TOPSIS) analysis to comprehensively assess the progress of competitive sports development in 31 provinces and cities in China. Additionally, we recommended further developments in competitive sports and proposed precise strategies for promoting its growth. The framework and methodology developed in this paper provide an objective and scientifically based set of decision-making guidelines that can be adopted by government agencies and related industries in order to create successful plans that promote the sustainable growth of competitive sport. This is expected to bolster the nation's global influence, enhance social unity, and fuel economic expansion. The findings of this study offer policymakers valuable insights regarding competitive sports and can advance the development of the sports sector in China, thus making it a crucial driver of regional socio-economic progress.

The entropy rate of Linear Additive Markov Processes.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.

Coupling coordination and underlying mechanisms of urbanization development and land use efficiency in the Gansu section of the Yellow River Basin.

This swift progression of urbanization has led to increasingly prominent conflicts over the use of land, particularly around its supply and demand. Researchers, both in China and internationally, have underscored the inherent interconnection between urbanization and land utilization. This relationship has gradually become more complex with the development of urbanization. With the implementation of the Yellow River Basin's strategy to preserve the environment while ensuring high-quality development, the Yellow River Basin has become a focal point of attention for numerous scholars. This study centers on the 57 county-level administrative divisions within the Gansu segment of the Yellow River Basin. We employed an extensive array of methodologies, such as GIS technology, the entropy method, data envelopment analysis, the coupling coordination degree model, and the panel vector autoregressive model. We established an index system and a measurement model to evaluate the degree of urbanization and the efficiency of land use. We also investigated the coupling coordinated dynamics between these two variables, to further explore the dynamic interplay between urbanization and land use and reveal their underlying mechanisms. The conclusions are as follows. The urbanization level and efficiency of land use in the Gansu section of the Yellow River Basin have exhibited a consistent upward trajectory, albeit at levels that are not particularly high, indicating substantial room for improvement in the future. The level of coupling coordination between urbanization and land use efficiency in the Gansu section of the Yellow River Basin has shown a generally upward trend. However, the overall coordination level remains relatively low, characterized by an imbalance, with "high coupling but low coordination". Regarding spatial distribution patterns, considerable disparities exist in the level of coordination development, which generally decreases from the eastern toward the western regions. A strong reciprocal and interactive relationship exists between the urbanization level and land use efficiency. An elevated level of economic urbanization can initially stimulate land use efficiency. Similarly, the improvement in the level of population urbanization, social urbanization, and ecological urbanization tends to exert a restraining influence on the augmentation of land use efficiency. Conversely, the enhancement of land use efficiency makes a distinct contribution to promoting the elevation of the urbanization level.

Biomechanical Assessment Methods Used in Chronic Stroke: A Scoping Review of Non-Linear Approaches.

Non-linear and dynamic systems analysis of human movement has recently become increasingly widespread with the intention of better reflecting how complexity affects the adaptability of motor systems, especially after a stroke. The main objective of this scoping review was to summarize the non-linear measures used in the analysis of kinetic, kinematic, and EMG data of human movement after stroke. PRISMA-ScR guidelines were followed, establishing the eligibility criteria, the population, the concept, and the contextual framework. The examined studies were published between 1 January 2013 and 12 April 2023, in English or Portuguese, and were indexed in the databases selected for this research: PubMed®, Web of Science®, Institute of Electrical and Electronics Engineers®, Science Direct® and Google Scholar®. In total, 14 of the 763 articles met the inclusion criteria. The non-linear measures identified included entropy (n = 11), fractal analysis (n = 1), the short-term local divergence exponent (n = 1), the maximum Floquet multiplier (n = 1), and the Lyapunov exponent (n = 1). These studies focused on different motor tasks: reaching to grasp (n = 2), reaching to point (n = 1), arm tracking (n = 2), elbow flexion (n = 5), elbow extension (n = 1), wrist and finger extension upward (lifting) (n = 1), knee extension (n = 1), and walking (n = 4). When studying the complexity of human movement in chronic post-stroke adults, entropy measures, particularly sample entropy, were preferred. Kinematic assessment was mainly performed using motion capture systems, with a focus on joint angles of the upper limbs.

Spatio-temporal evolution of sports tourism industry integration: A empirical analysis of Xinjiang in China.

Since the issuance of the "Guiding Opinions on Vigorously Developing Sports Tourism" in 2016, the integration of sports and tourism has become a strategy in regional economic development. It creates new economic growth points, enhances local images, and promotes cultural communication. In the context of the "Tourism Makes Xinjiang Thrive" strategy, quantitatively investigating the integration of the sports and tourism industries helps people to better understand their interaction which can serve as the valuable input in policy-making for the comprehensive development of a region. This paper uses entropy weight method, stochastic frontier analysis and coupling coordination model to quantitatively analyze the effect of sports tourism industry integration in Xinjiang from the perspective of integration path. Meanwhile, the Dagum Gini coefficient and nuclear density estimation were used to analyze the regional differences and dynamic evolution of industrial integration quality. The result shows that (1) The sports and tourism integration quality in Xinjiang has not reached the optimal goal of complete integration. In the process of mutual industrial promotion, tourism promotes a higher degree of integration with the sports industry. (2) The industrial integration quality shows a phenomenon of "imbalance and inadequacy" among the regions. The regions with high quality of industrial integration were Urumqi, Ili, Kashgar, Altay and Changji, which have rich sports tourism resources. (3) The overall spatial difference in the quality of industrial integration presented a fluctuation downtrend. The difference between the tourism industrial belts was very significant.

Causality-driven multivariate stock movement forecasting.

Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

Urban transportation system toughness assessment under New Crown epidemics.

Since the concept of toughness was introduced to transportation systems, transportation system toughness has received extensive attention from researchers in the field of transportation worldwide. In this paper, a methodology for quantifying and assessing the toughness of urban transportation systems is proposed in the context of the New Crown epidemic. Firstly, the definition of urban transportation system toughness in this context is clarified, and the entropy evaluation method is applied to construct the performance curve of urban transportation systems over time. Then, it is proposed to quantify the system's resistance, recovery, and adaptive ability in terms of the change in the cumulative amount of system performance. Finally, the three characteristic abilities of system toughness are organically combined to obtain a comprehensive assessment of system toughness. Example calculations and analyses are carried out in four Chinese cities with different levels of development, and the results show that the performance of urban transportation systems is positively correlated with their levels of development, and all of them fluctuate greatly under the influence of the epidemic, but Wuhan has the strongest resistance and recovery ability of the transportation system, and shows the highest toughness, followed by Lanzhou, Changchun, and Shanghai. The system toughness quantification and assessment methods proposed in this paper provide a reference for research on improving the ability of urban transportation systems to deal with multiple uncertainty disturbances.

Entropy-weighted water quality index, hydrogeochemistry, and Monte Carlo simulation of source-specific health risks of groundwater in the Morava River plain (Serbia).

Population growth, urbanization, industry, floods, and agriculture globally degrade groundwater in river plains, necessitating action for its quality assessment and management. Hence, a comprehensive methodology, including hydrogeochemical facies (Piper, Gibbs), irrigation indices (SAR, Wilcox), entropy-weighted water quality index (EWQI), positive matrix factorization (PMF), and Monte Carlo simulation of source-specific health risks was used in this study to analyze groundwater in the Morava river plain (Serbia). The results revealed a prevalent Ca-Mg-HCO3 groundwater type, influenced by water-rock interactions. Although groundwater was found suitable for irrigation, only 66.7 % of the samples were considered drinkable. Agricultural activities, natural processes, and municipal wastewater were identified as primary pollution sources. The incremental lifetime cancer risk (ILCR) and hazard index (HI) threshold exceedance for adults and children ranged from 8.5 % to 39 % of the samples, with arsenic identified as the most risk-contributing contaminant. These findings provide valuable insights for researchers studying groundwater vulnerability in river plains.

Analyzing entropy features in time-series data for pattern recognition in neurological conditions.

In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.

Evaluation of national fitness and national health development and coupling and coordination in 11 provinces and cities in Eastern China.

Under the influence of development strategies with regard to national fitness and health in China, the interactive development between national fitness and national health is becoming increasingly strong. To explore the coupling and coordination relationship between national fitness and national health, this paper conducts an empirical analysis of the coupling and coordination relationship between national fitness and national health in 11 provinces and cities in Eastern China using the entropy weight method, a coupling coordination model, spatial visualization of the coupling coordination degree and spatial autocorrelation analysis. The research confirms that the comprehensive development level of national fitness and national health in Eastern China shows a steady upward trend, with a lag in national fitness as a whole, and that the growth rate of national fitness development is faster than that of national health development. The coupling coordination degree of the two systems of national fitness and national health in Eastern China shows a slow upward trend, and the coupling coordination degree rises from barely coordinated to primary coordination. There are significant differences in the spatial pattern of coupling coordination: the spatial pattern from north to south generally shows 'low-high-high-low-high-low' characteristics, and the spatial spillover effect of coupling coordination in various regions has not yet appeared. The revised GM(1.1) prediction results indicate that the level and improvement rate of coupling coordination will accelerate significantly in the next 10 years, but the spatial differences will still exist. Finally, suggestions are proposed to optimize the coupling and coordinated development of national fitness and national health based on policy guarantees as well as strengthening and cross-regional cooperation.

Forecasting China carbon price using an error-corrected secondary decomposition hybrid model integrated fuzzy dispersion entropy and deep learning paradigm.

Forecasting China's carbon price accurately can encourage investors and manufacturing industries to take quantitative investments and emission reduction decisions effectively. The inspiration for this paper is developing an error-corrected carbon price forecasting model integrated fuzzy dispersion entropy and deep learning paradigm, named ICEEMDAN-FDE-VMD-PSO-LSTM-EC. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to primary decompose the original carbon price. Subsequently, the fuzzy dispersion entropy (FDE) is conducted to identify the high-complexity signals. Thirdly, the variational mode decomposition (VMD) and deep learning paradigm of particle swarm optimized long short-term memory (PSO-LSTM) models are employed to secondary decompose the high-complexity signals and perform out-of-sample forecasting. Finally, the error-corrected (EC) method is conducted to re-modify and strengthen the above-predicted accuracy. The results conclude that the forecasting performance of ICEEMDAN-type secondary decomposition models is significantly better than the primary decomposition models, the deep learning PSO-LSTM-type models have superiority in forecasting China carbon price, and the EC method for improving the forecasting accuracy has been proved. Noteworthy, the proposed model presents the best forecasting accuracy, with the forecasting errors RMSE, MAE, MAPE, and Pearson's correlation are 0.0877, 0.0407, 0.0009, and 0.9998, respectively. Especially, the long-term forecasting ability for 750 consecutive trading prices is outstanding. Those conclusions contribute to judging the carbon price characteristics and formulating market regulations.

Does the openness of the Boundary Shell system influence the sustainable development of the high-tech industry?

High-technology industries have gained substantial recognition as pivotal drivers of economic growth and technological advancement in modern society. The imperative of sustainable development in high-tech industries cannot be overemphasized, as it plays a crucial role in enabling long-term growth, fostering innovation, and assuming environmental responsibility. This article presents a study on sustainable development in high-tech industries using Boundary Shell theory. The study investigates the role of the stable and sustainable entropy criterion for the Boundary Shell system of high-tech industries from an entropy balance perspective. It analyzes the upper and lower limits of the Boundary Shell support force. Additionally, it improves the traditional boundary system ratio model to comprehensively and objectively evaluate the sustainable development of high-tech industries. The results illustrate that the Boundary Shell of industrial innovation is stronger than that of external dependency, with a reversed ranking of internal evaluation factor strengths compared to the traditional model. This research integrates reaction-diffusion equations theory with entropy balance equations theory to address sustainability issues in the high-tech industry. We further analyze the sustainable development of the high-tech industry through a Boundary Shell theory perspective to advance sustainability in high-tech industries. Moreover, it provides useful insights into the sustainable development of high-tech industries.

Sample Entropy Improves Assessment of Postural Control in Early-Stage Multiple Sclerosis.

Postural impairment in people with multiple sclerosis (pwMS) is an early indicator of disease progression. Common measures of disease assessment are not sensitive to early-stage MS. Sample entropy (SE) may better identify early impairments. We compared the sensitivity and specificity of SE with linear measurements, differentiating pwMS (EDSS 0-4) from healthy controls (HC). 58 pwMS (EDSS ≤ 4) and 23 HC performed quiet standing tasks, combining a hard or foam surface with eyes open or eyes closed as a condition. Sway was recorded at the sternum and lumbar spine. Linear measures, mediolateral acceleration range with eyes open, mediolateral jerk with eyes closed, and SE in the anteroposterior and mediolateral directions were calculated. A multivariate ANOVA and AUC-ROC were used to determine between-groups differences and discriminative ability, respectively. Mild MS (EDSS ≤ 2.0) discriminability was secondarily assessed. Significantly lower SE was observed under most conditions in pwMS compared to HC, except for lumbar and sternum SE when on a hard surface with eyes closed and in the anteroposterior direction, which also offered the strongest discriminability (AUC = 0.747), even for mild MS. Overall, between-groups differences were task-dependent, and SE (anteroposterior, hard surface, eyes closed) was the best pwMS classifier. SE may prove a useful tool to detect subtle MS progression and intervention effectiveness.

Exploring the relationship between regional tourism development and land use efficiency: A case study of Guangxi Zhuang Autonomous Region, China.

The utilization efficiency of land resources is an essential embodiment of economic development, social development, and ecological development and is a critical core to measure how to maximize the efficiency of land resources under limited conditions. The land is an important content and essential carrier of the research of tourism development level. This paper selects Panel Data from 2010 to 2019 to research the Guangxi regional tourism development. The entropy weight method and stochastic frontier production function (SFA) model were used to evaluate the development level of urban-rural tourism and the utilization efficiency of land resources in Guangxi. This paper uses the Panel Vector Autoregression (PVAR) model to analyze the internal relationship between urban-rural tourism development. The results show that: (1) Guangxi has a good level of tourism development and a high land use efficiency. (2) There is a reciprocal causation relationship between the regional tourism development level and land use efficiency in Guangxi, with significant levels of 0.005 and 0.034 respectively, indicating high credibility. This indicates that there is a mutual promotion and interaction between the two, which rely on and drive each other, promoting the joint sustainability of tourism development and land use efficiency. (3) . The tourism development level is greatly influenced by itself, with impact values all above 0.99. At the same time, land use also has a significant self-impact, with impact values all above 0.87. Their internal optimization system is solid and endogenous impetus is robust, which can drive their development. Establishing an effective strategy for developing and protecting land use is beneficial to promote the long-term effectiveness of sustainable tourism development, enhancing high-quality development of the tourism economy and improving people's living standards and quality.

Index construction and application of digital transformation in the insurance industry: Evidence from China.

In the context of digitization, the insurance industry's value chain is undergoing significant shifts. However, the existing research on its comprehension and measurement remains relatively limited. This study constructs an index system for digital transformation in the insurance industry (DTII) on three components: digital infrastructure, digital platform, and digital applications. Utilizing data from 31 provinces in China, this study employs the entropy weight method, analytic hierarchy process method and minimum relative entropy method to measure the weights of indicators, empirically applying this index system. The results show that DTII in China experiences rapid advancement with an average annual growth rate of 20.46% from 2014 to 2020 and there exists strong regional convergence. In addition, the spatial agglomeration and spatial effects of DTII are mainly concentrated in the life insurance industry and the eastern region. This study provides an index system and empirical evidence for evaluating the DTII, providing policy insights for exploring the sustainable development path of the insurance industry in the digital era.

A GIS approach for groundwater quality evaluation with entropy method and fluoride exposure with health risk assessment.

This study used the entropy water quality index to analyse the suitability of groundwater for human consumption as well as the hazard index to identify the probable non-carcinogenic dangers among children, women, and men in Nawada, Bihar (India). A total of 75 groundwater samples were taken from hand pumps and tube/bore wells in the pre-monsoon of 2017, and they were evaluated for various physicochemical characteristics. The region's groundwater major cations and anions are dominated by Ca2+ > Mg2+ and [Formula: see text] > Cl- > [Formula: see text] > NO3- > F > [Formula: see text]. Fluoride, chloride, and hardness exceeded WHO and BIS safe standards. Calcium, sodium, magnesium, sulphate, and chloride showed positive correlations, indicating water-rock interactions and mineral leaching and dissolution. Ionic cross-plots reveal that the dissolution of carbonate minerals was the primary source of calcium and magnesium in the groundwater. Also, silicate weathering contributed to these ions in the groundwater. The entropy water quality index (EWQI) found that 79% of groundwater samples were drinkable, whereas 21% were not consumable. The eastern, western, and some southern study areas have the worst drinking water quality. The main source of fluoride toxicity in people is groundwater. For all sampling locations, the HQ fluoride was calculated to be in the ranges of 0.04-3.69 (male), 0.04-3.27 (female), and 0.05-4 (children), indicating a considerably greater risk than the permissible levels (> 1). The fluoride-based non-carcinogenic risks are 27%, 20%, and 21% for children, women, and men, respectively. Children have higher risks from polluted water than adults, according to the non-carcinogenic health risk assessment. This study establishes a standard for regional and global scientific studies that help decision-makers and planners determine the quality of groundwater and fluoride risk and management.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Measurement and spatiotemporal characteristics of China's green finance.

To study the extent of green finance development in China, this article constructs a green finance index system and employs the entropy value method to measure China's green finance by using a yearly provincial panel data from 2001 to 2020. The Thiel and Moran indices are then used to systematically analyze the temporal and spatial distribution of China's regional green finance. The findings are summarized as follows. Firstly, the overall green finance index in China experiences an upward trend. The development of green finance in the eastern region is superior to that in other regions in terms of absolute value and growth rate. Moreover, the differences in China's green finance index have shown an increasing trend over the last two decades, which is mostly contributed by the intra-regional differences. Finally, the inter-regional distribution of green finance index demonstrates that green finance development has a spatial spillover effect.

The nexus between green finance and energy consumption in regional comprehensive economic partnership countries.

Green finance has been valued and promoted by Regional Comprehensive Economic Partnership (RCEP) countries for its attribute of supporting green and sustainable development. However, the fossil fuel-centric growth pattern poses a threat to the sustainable development of RCEP countries. The existing literature remains inadequate regarding the relationship between green finance and fossil energy consumption. An in-depth understanding and empirical examination of the nexus between green finance development and fossil energy consumption in RCEP countries is key to successful policymaking and sustainable development. This study proposes a theoretical framework for analyzing the nexus between green finance and fossil energy consumption. Then, a green finance development index is constructed by using the entropy weight method based on green credits, green securities, and green investments. By utilizing method of moment quantile regression (MMQR), panel data of 10 RCEP countries from 2008 to 2020 are investigated. The results demonstrate a strong nonlinear pattern of green finance's impact on reducing fossil energy consumption. Conventional finance and fossil energy consumption from the preceding period significantly promote fossil energy consumption, while green technology serves as a mitigating factor for fossil energy consumption. However, the impacts of education and environmental expenditure on fossil energy consumption are limited and inconsistent. Hence, the relevant practitioners, including governments and policymakers, are encouraged to collaboratively promote the green finance policies, and devise tailor-made strategies based on countries' features. Additionally, this study recommends that the RCEP countries incorporate research and development of green technologies, as well as environmental and educational expenditures, into the policymaking process.