Evidence of longitudinal differences in spring migration strategies of an Arctic-nesting goose.

During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large-scale changes in land use, climate, and potential carry-over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White-fronted Geese (Anser albifrons) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre-nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western- and eastern-Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital-income breeding continuum was influenced by longitude, revealing geographic effects of life-history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision-making across broad scales.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

Movement decisions driving metapopulation connectivity respond to social resources in a long-lived ungulate, bighorn sheep (Ovis canadensis).

The spatial availability of social resources is speculated to structure animal movement decisions, but the effects of social resources on animal movements are difficult to identify because social resources are rarely measured. Here, we assessed whether varying availability of a key social resource-access to receptive mates-produces predictable changes in movement decisions among bighorn sheep in Nevada, the United States. We compared the probability that males made long-distance 'foray' movements, a critical driver of connectivity, across three ecoregions with varying temporal duration of a socially mediated factor, breeding season. We used a hidden Markov model to identify foray events and then quantified the effects of social covariates on the probability of foray using a discrete choice model. We found that males engaged in forays at higher rates when the breeding season was short, suggesting that males were most responsive to the social resource when its existence was short lived. During the breeding season, males altered their response to social covariates, relative to the non-breeding season, though patterns varied, and age was associated with increased foray probability. Our results suggest that animals respond to the temporal availability of social resources when making the long-distance movements that drive connectivity. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Tensor product algorithms for inference of contact network from epidemiological data.

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

Recombination fraction in pre-recombinant inbred lines (PRERIL) - revisiting a century old problem in genetics.

BACKGROUND: Traditional recombinant inbred lines (RILs) are generated from repeated self-fertilization or brother-sister mating from the F1 hybrid of two inbred parents. Compared with the F2 population, RILs cumulate more crossovers between loci and thus increase the number of recombinants, resulting in an increased resolution of genetic mapping. Since they are inbred to the isogenic stage, another consequence of the heterozygosity reduction is the increased genetic variance and thus the increased power of QTL detection. Self-fertilization is the primary form of developing RILs in plants. Brother-sister mating is another way to develop RILs but in small laboratory animals. To ensure that the RILs have at least 98% of homozygosity, we need about seven generations of self-fertilization or 20 generations of brother-sister mating. Prior to homozygosity, these lines are called pre-recombinant inbred lines (PRERIL). Phenotypic values of traits in PRERILs are often collected but not used in QTL mapping. To perform QTL mapping in PRERILs, we need the recombination fraction between two markers at generation t for t < 7 (selfing) or t < 20 (brother-sister mating) so that the genotypes of QTL flanked by the markers can be inferred. RESULTS: In this study, we developed formulas to calculate the recombination fractions of PRERILs at generation t in self-fertilization, brother-sister mating, and random mating. In contrast to existing works in this topic, we used computer code to construct the transition matrix to form the Markov chain of genotype array between consecutive generations, the so-called recurrent equations. CONCLUSIONS: We provide R functions to calculate the recombination fraction using the newly developed recurrent equations of ordered genotype array. With the recurrent equations and the R code, users can perform QTL mapping in PRERILs. Substantial time and effort can be saved compared with QTL mapping in RILs.

Point-of-care Testing HbA1c screening for type 2 diabetes in urban and rural areas of China: a cost-effectiveness analysis.

Background: Point-of-care Testing (POCT) glycosylated hemoglobin (HbA1c) is a convenient, cheap, effective and accessible screening method for type 2 diabetes in rural areas and community settings that is widely used in the European region and Japan, but not yet widespread in China. The study is the first to evaluate the cost-effectiveness of POCT HbA1c, fasting capillary glucose (FCG), and venous blood HbA1c to screen for type 2 diabetes in urban and rural areas of China, and to identify the best socio-economically beneficial screening strategy. Methods: Based on urban and rural areas in China, economic models for type 2 diabetes screening were constructed from a social perspective. The subjects of this study were adults aged 18-80 years with undiagnosed type 2 diabetes. Three screening strategies were established for venous blood HbA1c, FCG and POCT HbA1c, and cost-effectiveness analysis was performed by Markov models. One-way sensitivity analysis and probabilistic sensitivity analysis were performed on all parameters of the model to verify the stability of the results. Results: Compared with FCG, POCT HbA1c was cost-effective with an incremental cost-utility ratio (ICUR) of $500.06/quality-adjusted life year (QALY) in urban areas and an ICUR of $185.10/QALY in rural areas, within the willingness-to-pay threshold (WTP = $37,653). POCT HbA1c was cost-effective with lower cost and higher utility compared with venous blood HbA1c in both urban and rural areas. In the comparison of venous blood HbA1c and FCG, venous blood HbA1c was cost-effective (ICUR = $20,833/QALY) in urban areas but not in rural areas (ICUR = $41,858/QALY). Sensitivity analyses showed that the results of the study were stable and credible. Conclusions: POCT HbA1c was cost-effective for type 2 diabetes screening in both urban and rural areas of China, which could be considered for future clinical practice in China. Factors such as geographic location, local financial situation and resident compliance needed to be considered when making the choice of venous blood HbA1c or FCG.

Unveiling Allosteric Regulation and Binding Mechanism of BRD9 through Molecular Dynamics Simulations and Markov Modeling.

Bromodomain-containing protein 9 (BRD9) is a key player in chromatin remodeling and gene expression regulation, and it is closely associated with the development of various diseases, including cancers. Recent studies have indicated that inhibition of BRD9 may have potential value in the treatment of certain cancers. Molecular dynamics (MD) simulations, Markov modeling and principal component analysis were performed to investigate the binding mechanisms of allosteric inhibitor POJ and orthosteric inhibitor 82I to BRD9 and its allosteric regulation. Our results indicate that binding of these two types of inhibitors induces significant structural changes in the protein, particularly in the formation and dissolution of α-helical regions. Markov flux analysis reveals notable changes occurring in the α-helicity near the ZA loop during the inhibitor binding process. Calculations of binding free energies reveal that the cooperation of orthosteric and allosteric inhibitors affects binding ability of inhibitors to BRD9 and modifies the active sites of orthosteric and allosteric positions. This research is expected to provide new insights into the inhibitory mechanism of 82I and POJ on BRD9 and offers a theoretical foundation for development of cancer treatment strategies targeting BRD9.

Improving adherence to a daily PrEP regimen is key when considering long-time partnerships.

A population model of HIV that includes susceptible individuals not taking the pre-exposure prophylaxis (PrEP), susceptible individuals taking daily PrEP, and infected individuals is developed for casual partnerships, as well as monogamous and non-monogamous long-term partnerships. Reflecting the reality of prescription availability and usage in the U.S., the PrEP taking susceptible population is a mix of individuals designated by the CDC as high and low risk for acquiring HIV. The rate of infection for non-monogamous long-term partnerships with differential susceptibility is challenging to calculate and requires Markov chain theory to represent the movement between susceptible populations before infection. The parameters associated with PrEP initiation, suspension and adherence impact both the reproduction number of the model and the elasticity indices of the reproduction model. A multi-parameter analysis reveals that increasing adherence has the largest effect on decreasing the number of new infections.

Theoretical framework and inference for fitting extreme data through the modified Weibull distribution in a first-failure censored progressive approach.

The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

Monitoring and predicting development of built-up area in sub-urban areas: A case study of Sleman, Yogyakarta, Indonesia.

Monitoring built-up areas in the previous year and possible predictions for the following year are important in planning regional development and controlling the expansion of built-up areas. This study detects changes in the built-up area (2018-2022). It predicts the future (2026) using Landsat satellite imagery in the Sleman Regency, Yogyakarta Special Region, Indonesia study area. Mapping built-up areas is identified using the Normalized Difference Built-Up Index (NDBI). Vegetation conditions were analyzed using the Normalized Difference Vegetation Index (NDVI). Changes in the built-up area are predicted using the CA-Markov chain model for 2026. The prediction is calibrated by comparing the simulated map with the results of the classification of built-up areas in 2022. The research findings show that the built-up area has increased by 12.84 % from 2018 to 2022 and is predicted to increase by 15.48 % in 2026. The existence of built-up areas has an influence on land surface temperatures where the analysis results show a moderate correlation between NDBI and LST, namely 2018 (R2 = 0.401), 2019 (R2 = 0.323), 2020 (R2 = 0.401), 2021 (R2 = 0.415), and 2022 (R2 = 0.384). The higher the NDBI value, the higher the LST value, and vice versa. Therefore, regional development planning, mainly built-up areas, is an important recommendation for decision-makers in the study area.

Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities.

OBJECTIVE: Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations. Approach: We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly. Main results: We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies. Significance: The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases. .

Adaptive Gaussian Markov random fields for child mortality estimation.

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985 to 2019, a time period which includes the Rwandan civil war and genocide.

Disease progression in chronic heart failure is linear: Insights from multistate modelling.

AIMS: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. METHODS AND RESULTS: Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. CONCLUSIONS: A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

A Bayesian competing risk analysis of renal cancer patients based on SEER database.

BACKGROUND: Renal cell carcinoma (RCC) remains a global health concern due to its poor survival rate. This study aimed to investigate the influence of medical determinants and socioeconomic status on survival outcomes of RCC patients. We analyzed the survival data of 41,563 RCC patients recorded under the Surveillance, Epidemiology, and End Results (SEER) program from 2012 to 2020. METHODS: We employed a competing risk model, assuming lifetime of RCC patients under various risks follows Chen distribution. This model accounts for uncertainty related to survival time as well as causes of death, including missing cause of death. For model analysis, we utilized Bayesian inference and obtained the estimate of various key parameters such as cumulative incidence function (CIF) and cause-specific hazard. Additionally, we performed Bayesian hypothesis testing to assess the impact of multiple factors on the survival time of RCC patients. RESULTS: Our findings revealed that the survival time of RCC patients is significantly influenced by gender, income, marital status, chemotherapy, tumor size, and laterality. However, we observed no significant effect of race and origin on patient's survival time. The CIF plots indicated a number of important distinctions in incidence of causes of death corresponding to factors income, marital status, race, chemotherapy, and tumor size. CONCLUSIONS: The study highlights the impact of various medical and socioeconomic factors on survival time of RCC patients. Moreover, it also demonstrates the utility of competing risk model for survival analysis of RCC patients under Bayesian paradigm. This model provides a robust and flexible framework to deal with missing data, which can be particularly useful in real-life situations where patients information might be incomplete.

A health decision analytical model to evaluate the cost-effectiveness of female genital schistosomiasis screening strategies: The female genital schistosomiasis SCREEN framework.

Female genital schistosomiasis is a chronic gynaecological disease caused by the waterborne parasite Schistosoma (S.) haematobium. It affects an estimated 30-56 million girls and women globally, mostly in sub-Saharan Africa where it is endemic, and negatively impacts their sexual and reproductive life. Recent studies found evidence of an association between female genital schistosomiasis and increased prevalence of HIV and cervical precancer lesions. Despite the large population at risk, the burden and impact of female genital schistosomiasis are scarcely documented, resulting in neglect and insufficient resource allocation. There is currently no standardised method for individual or population-based female genital schistosomiasis screening and diagnosis which hinders accurate assessment of disease burden in endemic countries. To optimise financial allocations for female genital schistosomiasis screening, it is necessary to explore the cost-effectiveness of different strategies by combining cost and impact estimates. Yet, no economic evaluation has explored the value for money of alternative screening methods. This paper describes a novel application of health decision analytical modelling to evaluate the cost-effectiveness of different female genital schistosomiasis screening strategies across endemic settings. The model combines a decision tree for female genital schistosomiasis screening strategies, and a Markov model for the natural history of cervical cancer to estimate the cost per disability-adjusted life-years averted for different screening strategies, stratified by HIV status. It is a starting point for discussion and for supporting priority setting in a data-sparse environment.

Hierarchical deep reinforcement learning for self-adaptive economic dispatch.

It is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility of renewable energy. Deep reinforcement learning (DRL) algorithms are introduced as a solution to avoid modeling the complex uncertainties and to adapt the fluctuation of uncertainty by interacting with the environment and using feedback to continuously improve their strategies. However, the large-scale nature and uncertainty of the system lead to the sparse reward problem and high-dimensional space issue in DRL. A hierarchical deep reinforcement learning (HDRL) scheme is designed to decompose the process of solving this problem into two stages, using the reinforcement learning (RL) agent in the global stage and the heuristic algorithm in the local stage to find optimal dispatching decisions for power systems under uncertainty. Simulation studies have shown that the proposed HDRL scheme is efficient in solving power system economic dispatch problems under both deterministic and uncertain scenarios thanks to its adaptation system uncertainty, and coping with the volatility of uncertain factors while significantly improving the speed of online decision-making.

Cost-effectiveness analysis of the tislelizumab versus docetaxel for advanced or metastatic non-small-cell lung cancer in China.

Background: Tislelizumab is the first PD-1 inhibitor in China to demonstrate superior efficacy in second-line or third-line treatment of patients with advanced or metastatic non-small-cell lung cancer (NSCLC). This study aimed to evaluate the cost-effectiveness of tislelizumab compared to docetaxel from a Chinese healthcare system perspective. Methods: A dynamic Markov model was developed to evaluate the cost-effectiveness of tislelizumab in comparison to docetaxel in second or third-line treatment. The efficacy data utilized in the model were derived from the RATIONALE-303 clinical trial, while cost and utility values were obtained from the drug data service platform and published studies. The primary outcomes of the model encompassed quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). One-way sensitivity analysis and probabilistic sensitivity analysis were conducted to validate the robustness of the base case analysis results. Results: The tislelizumab group demonstrated a cost increase of CNY 117,473 and a gain of 0.58 QALYs compared to the docetaxel group, resulting in an ICER value of CNY 202,927 per QALY gained. Conclusion: The administration of tislelizumab in patients with advanced or metastatic NSCLC not only extends the progression-free survival (PFS) and overall survival (OS). Moreover, this treatment demonstrates a favorable cost-effectiveness profile across the Chinese population.

Cost-effectiveness analysis of NVX-CoV2373 COVID-19 vaccination for elderly people in Japan.

Background: NVX-CoV2373 is one of the vaccines marketed for COVID-19 prevention in Japan. Information on its cost-effectiveness is important for making well-informed decisions on the future of Japan's COVID-19 vaccination programme from the public healthcare payer's perspective. The aim of this study was to evaluate the cost-effectiveness of NVX-CoV2373 vaccination in the elderly Japanese population. Methods: Two analysis populations that included elderly Japanese individuals (aged ≥ 65 years) were defined in this study: those who had not received a COVID-19 vaccine or had not completed a primary vaccination series (i.e., first two vaccinations) with an approved COVID-19 vaccine (analysis population 1), and those who had received two primary vaccinations with an approved COVID-19 vaccine (analysis population 2). A literature-informed Markov model for each analysis population was developed to evaluate the cost-effectiveness of vaccination with NVX-CoV2373 against no vaccination with NVX-CoV2373 from the public healthcare payer's perspective as a base-case analysis and from the societal perspective as a scenario analysis. Vaccine efficacy was estimated from a phase 3 study of NVX-CoV2373 (EudraCT number: 2020-004123-16). Cost-effectiveness was assessed using a willingness-to-pay threshold of Japanese yen (JPY) 5 million per quality-adjusted life-year (QALY). Deterministic and probabilistic sensitivity analyses were also performed. Results: For analysis population 1, NVX-CoV2373 primary and booster vaccinations would reduce costs by JPY 37,647 and prolong QALYs by 0.01601. Therefore, NVX-CoV2373 primary and booster vaccinations were considered to be dominant over no vaccination. For analysis population 2, an NVX-CoV2373 booster vaccination would increase costs by JPY 5010 and prolong QALYs by 0.00550, with the incremental cost-effectiveness ratio of JPY 910,566 per QALY gained. Conclusions: Our analyses suggest that a vaccination strategy with NVX-CoV2373 is cost-effective in the elderly population (aged ≥ 65 years) of Japan.

Absolute direction in organelle movement.

In movement analysis, correlated random walk (CRW) models often use so-called turning angles, which are measured relative to the previous movement direction. To segregate between different movement modes, hidden Markov models (HMMs) describe movements as piecewise stationary CRWs in which the distributions of turning angles and step sizes depend on the underlying state. This typically allows for the segregation of movement modes that show different movement speeds. We show that in some cases, it may be interesting to investigate absolute angles, that is, biased random walks (BRWs) instead of turning angles. In particular, while discrimination between states in the turning angle setting can only rely on movement speed, models with absolute angles can be used to discriminate between sections of different movement directions. A preprocessing algorithm is provided that enables the analysis of absolute angles in the existing R package moveHMM. In a data set of movements of cell organelles, models using not the turning angle but the absolute angle could capture interesting additional properties. Goodness-of-fit was increased for HMMs with absolute angles, and HMMs with absolute angles tended to choose a higher number of states, suggesting the existence and relevance of prominent directional changes in the present data set. These results suggest that models with absolute angles can provide important information in the analysis of movement patterns if the existence and frequency of directional changes is of biological importance.

Spatial characteristics and dynamic differences of power industry's low carbon transition efficiency.

The power industry's low carbon transition is pivotal for achieving carbon reduction and sustainable development. This study uses the super epsilon-based measurement (Super-EBM) model and the Malmquist index to evaluate the power industry's low carbon transition efficiency using data from 30 provinces in China from 2010 to 2020, and utilizes the Tobit model to comprehensively analyze the factors affecting the low carbon transition of power industry. In addition, this paper examines the spatial differences in the power industry's low carbon transition efficiency as well as its distributional characteristics and dynamic evolutionary patterns. Conclusion is drawn as follows this paper analyzes the regional differences, spatial distribution characteristics and dynamic evolutionary trends of the power industry's low carbon transition. The main conclusions are as follows: (1) The power industry's low carbon transition efficiency in China shows an uptrend, with the western China region having the highest overall level of efficiency, greater fluctuations in the central China region, and more stability in the eastern China region, technological progress is a central factor in increasing total factor productivity, the efficiency of the power industry's low carbon transition is positively influenced by the electricity prices, and negatively influenced by the energy structure, environmental regulations and economic structure; (2) the Intraregional differences and hypervariable density are the main reasons sources of the overall differences in the efficiency of the power industry's low carbon transition; Intraregional differences in the eastern, central, and western China regions are decreasing year by year, but the efficiency of the power industry's low carbon transition in the western China region is still distributed in a multipolar way; (3) The dynamic evolutionary trends of the efficiency distribution of the low carbon transition in power industry is influenced by the type of spatial lag in the neighboring area. Where areas with low efficiency makes it difficult to achieve short-term leapfrog development, and areas with a cluster of high-efficiency provinces are prone to "Siphon Effect". The findings provide a theoretical basis for promoting the efficiency of the power industry's low carbon transition and coordinating the strategic adjustment of economic and environmental green development.