Imbalance Characteristics Research Articles

Analysis of the relationship between body imbalance characteristics and physical ability in air force cadets: physical function and gravity acceleration resistance

Air Force pilots and some athletes (e.g., in sledding sports) are frequently exposed to high G-forces. High-intensity physical activity is essential to withstand gravitational acceleration, and morphological symmetry and functional movement are directly related to injury in those who perform these activities. However, the relationship between G-endurance and physical imbalance and the effect on physical performance is poorly understood. This study aimed to analyze the physical performance of those exposed to a high load of G-tolerance and who performed high-intensity physical activity. A total of 363 male cadets from the Korean Air Force Academy underwent functional movement screen tests, anatomical structure measurements, G-tests (fourth grade), and body composition and physical fitness tests. Participants were classified into the G-test-pass and -fail groups depending on the G-test results. The pass group showed significantly different bilateral imbalance in active straight leg raises (p < 0.05), while the fail group showed a significant difference in deep squats (p < 0.05), shoulder mobility (p < 0.01), rotary stability (p < 0.05), and functional leg length (p < 0.05). Leg length was significantly correlated with the hurdle step (p < 0.01), inline lunge (p < 0.01), and active straight leg raise (p < 0.01). Functional leg lengths on both sides and structural leg lengths were also significantly correlated (p < 0.01). High body balance positively affects gravitational acceleration and exercise performance. Therefore, functional movement and physical imbalance affect performance in pilots affected by gravitational acceleration. This study can also be applied to strengthen the performance of winter sports athletes affected by gravitational acceleration.

Attributing spatially stratified heterogeneity in biodiversity of urban–rural interlaced zones based on the OPGD model

Urban–rural interlaced zones are characterized by the interpenetration of urban and rural elements, drastic changes in construction, relatively weak planning and management, and anthropogenic activities that constantly impact the ecological background. However, the ecological barriers and hinterland spaces of the city center provide important ecological functions. In this study, we selected forest birds as indicator species of the urban ecological environment and explored the spatial stratification and heterogeneity effects of environmental substrates and anthropogenic activities on bird diversity (species richness, abundance, Shannon–Wiener index, and Simpson index) in urban–rural interlaced zones using the optimal parameter geographic detector model to characterize the changes in ecological functions in these zones. The results of this study are as follows:(1) The bird diversity index in Minhang District, Shanghai, showed an obvious urban-rural gradient divergence with the transition of urban zone- rural-urban interface - rural zone.An agglomerative spatial differentiation pattern was observed more in the southeast but less in the northwest, with a high degree of spatial distribution concentrations and evident imbalance characteristics.(2) The spatially stratified heterogeneity in bird diversity in urban–rural interlaced zones was because of the combined action of multiple driving factors under the three dimensions of habitat environment, degree of urbanization, and anthropogenic interference. Most of the interactions between any two factors showed non-linear or bifactorial enhancement effects. Furthermore, the one-factor explanatory power of urbanization and anthropogenic interference factors on bird diversity was significantly higher than that of the habitat environment factor.(3) Population heat distribution (PD), distance to the center of Shanghai (DC), and nighttime lighting index(NTL) were the main drivers of spatially stratified heterogeneity in bird diversity and the key indicators of urban–rural gradient changes. The interaction between PD and DC had the strongest explanatory power for the spatial differentiation of bird diversity.

Understanding regional structure through spatial networks: A simulation optimization framework for exploring balanced development

The spatial structure inherently reflects a region's pattern and developmental processes. Understanding the rationality behind this structure and identifying optimization directions are pivotal for macro-level regulation of regional development and collaborative efforts. This study initiates a discussion on the composition and optimization mechanisms inherent in spatial structures. Adopting a spatial network perspective, we interpret the interaction dynamics within regional morphological structures as the consumption of resources through flows. We transform structural optimization into an equilibrium matching process of resources and establish a quantitative research framework that integrates network modeling, structural issue identification, and optimization strategies. Then, we selected the Guangdong-Hong Kong-Macao Greater Bay Area as our empirical subject, and obtained three important results: (1) Before optimization, we identified structural spatial imbalance characteristics; (2) We explored the evolutionary trend of optimization and delineated four main optimization stages; (3) The spatial structure of the region was improved, and significant benefits in equilibrium and resource utilization was achieved. Finally, we discussed the feasibility of the framework and the necessity to integrate policy networks, hoping to assist regional managers in macro-governance and enhance the rationality of regional planning and coordination.

Evidence reasoning-based alarm method for silicon content in blast furnace hot metal using unbalanced monitoring samples

Silicon content monitoring and alarm based on data-driven methods is an essential approach to ensure product quality and smelting status in the blast furnace ironmaking process. However, due to the frequent fluctuation of production process, the sample data of silicon content usually shows obvious imbalance characteristics, i.e., less abnormal samples and more normal samples, posing great challenge to alarm task. In this case, this paper develops a novel data-driven alarm method based on the evidence reasoning (ER) rule. First, a normalization method of likelihood probability is given to construct the offline referential evidence matrix (REM) for characterizing the imbalanced sample distributions. Second, the online sample matches with the REM to generate the corresponding alarm evidence, and then the iterative ER fusion strategy is proposed to combine the historical and current evidence. Finally, the alarm decision can be made according to the fusion results. The integration of current and historical information helps to reduce the false alarms/missed alarms caused by the imbalanced samples and frequent state switches. The proposed ER rule-based monitoring approach is tested on real industrial data and comparison results with conventional methods like filtering and delay timer show that it has better comprehensive performance.

Novel extended NI-MWMOTE-based fault diagnosis method for data-limited and noise-imbalanced scenarios

Under real-world conditions, faulty samples of key components (e.g., bearings and cutting tools, etc.) are typically limited and sparse. Additionally, their historical data is characterized by time-series and imbalance characteristics. In other words, the training samples are not only limited and noisy, but also exhibit both within-class and between-class imbalance. These factors present significant challenges in the realm of fault monitoring modeling. To tackle these challenges, this paper presents an innovative fault diagnosis method rooted in the extended NI-MWMOTE and LS-SVM. NI-MWMOTE stands as an advanced noise-immunity majority weighted minority oversampling technique, originally introduced in our prior research, and it has exhibited exceptional competitiveness in noisy imbalanced benchmark datasets. It champions an adaptive noise processing strategy leveraging the distribution characteristics of noisy imbalanced data and the essence of machine learning. Specifically, it employs Euclidean distance and neighbor density to differentiate between spurious noise and true noise, and it determines the optimal processing strategy based on misclassification error and iteration. Furthermore, it employs unsupervised aggregative hierarchical clustering, misclassification error, and majority-weighted minority oversampling in a collaborative manner to address both within-class and between-class imbalanced problems. The primary contribution of our paper lies in the context of the monitoring scenario mentioned above. We have expanded the hyper-parameter range of NI-MWMOTE, corrected and optimized its built-in noise function to enhance the interpretability of the model, and successfully applied it in conjunction with LS-SVM to this particular setting. Notably, this marks the pioneering endeavor within our established knowledge sphere into the domain of tool wear state monitoring. The results suggest that, when compared to 11 well-known algorithms, our framework demonstrates significant competitiveness in real-world scenarios characterized under data-limited and noise-imbalanced scenarios for bearings and cutting tools fault diagnosis. This establishes a solid theoretical and practical foundation for similar scenarios.

Spatial disparities and variation sources decomposition of energy poverty in China

Addressing energy poverty and promoting energy equity are crucial for China to eradicate poverty comprehensively and achieve shared prosperity. Through a combined dynamic evaluation approach to accurately measure the extent of energy poverty, this paper analyzes the staged time series, spatial distribution characteristics of energy poverty applies the Dagum Gini coefficient and variance decomposition to observe the imbalance characteristics of energy poverty and identify the sources of variation. The objective of this paper is to mitigate the influence of spatial disparities on coordinating efforts to eradicate energy poverty across different regions. Consequently, provides valuable insights for decision-making to effectively eradicate energy poverty and expedite the achievement of energy equity. The results indicate: The reduction process of energy poverty in China exhibit phased characteristics from 2000 to 2020, with significant variations in reduction levels among provinces. Different sources of energy poverty exhibits imbalanced spatial distribution and evolution. The overall disparity in energy poverty widens as the phases progress, with characteristic nodes experiencing a temporal delay of 2–3 years compared to the policy nodes. Inter-regional disparities are the primary spatial source of energy poverty differences, while energy use capacity is the main factor contributing to variation.

Identification of Essential Protein Using Chemical Reaction Optimization and Machine Learning Technique.

For the survival, development, and reproduction of the organism, understanding the working process of the cell, disease study, design drugs, etc. essential protein plays a crucial role. Due to a large number of biological information, computational methods are becoming popular in recent times to identify the essential protein. Many computational methods used machine learning techniques, metaheuristic algorithms, etc. to solve the problem. The problem with these methods is that the essential protein class prediction rate is still low. Many of these methods have not considered the imbalance characteristics of the dataset. In this paper, we have proposed an approach to identify essential proteins using a metaheuristic algorithm named Chemical Reaction Optimization (CRO) and machine learning method. Both topological and biological features are used here. The Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) datasets are used in the experiment. Topological features are calculated from the PPI network data. Composite features are calculated from the collected features. Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor (SMOTE+ENN) technique is applied to balance the dataset and then the CRO algorithm is applied to achieve the optimal number of features. Our experiment shows that the proposed approach gives better results in both accuracy and f-measure than the existing related methods.

Autism Spectrum Disorder detection by an Intelligent Deep Learning Network

Mental disorder Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects a person’s behaviour and communication. In today’s world, ASD is gaining energy faster than ever before, limiting social and cognitive capacities while exhibiting varying expressions from one person to the next. Despite the fact that a lot of research has been done on ASD using various methodologies, the results haven’t shown enough progress in precision and execution. Similarly, finding mental imbalance characteristics through screening exams is time-consuming and costly. The main goal is to present a powerful half breed forecast model using high-level AI approaches like as SVM (Support Vector Machine) and NB (Nave Bayes). When compared to current models that use a real dataset acquired from people, the evaluation findings reveal that the suggested forecast model provides higher results in terms of precision, execution, and accuracy. Furthermore, if the problem is identified, space-specific professionals from designated urban regions are advised.

Experiment on Space-Time Evolution Characteristics of Deformation and Failure of Overlying strata in Longwall Mining of Steeply Dipping Coal Seam

Revealing the space-time evolution characteristics of overlying strata deformation and failure is the key for safe and efficient mining of steeply dipping coal seam. In this paper, based on the physical similarity simulation experiment and field measurement method, the non-uniform filling characteristics of gangue in gob and the roof migration law are comprehensively determined, the evolution characteristics and distribution patterns of the abutment pressure in the upper and lower sections, the distribution characteristics of the working resistance of the support, and the regional strata behavior law in the mining process are analyzed, and the technical measures for the stability control of the support in steeply dipping coal seam are put forward. Results shown: With the advancing distance increases, the accumulation range of gangue increases, and the inverted triangle airspace begins to appear repeatedly in the gob behind the support. In the deep gob, the angle between the gangue pile and the horizontal plane decreases continuously from the lower part to the upper part along the inclined direction. Horizontal displacement, vertical displacement and combined displacement of roof show an evolutionary trend of ‘increase – stability’. The peak displacement position moves from the upper part to the middle part of the working face, which is located at the hinge point between the broken block and the failure envelope surface. The evolution of abutment pressure of headgate and tailgate T-junction shows a trend of ‘increase – stability’. The peak and concentration coefficient at headgate T-junction are greater than tailgate T-junction. The load imbalance characteristics of the support in working face are obvious, and the basic characteristics along the inclined direction are the largest in the middle area, the second in the upper and the smallest in the lower area. The initial force and working resistance of support should be improved, and the position of the support should be adjusted in time in practical engineering. The results provide reference and guidance for practical production.

Simulation Analysis of Relay Protection Action and Weak Point Adaptability Under Non Preset Operating Conditions of Complex Large Power Grid

In order to improve the protection performance of relay protection devices in complex large-scale power grid, a simulation analysis method of relay protection action and weak point adaptability under non preset operating conditions of complex large-scale power grid is proposed. In this method, pscad/emtdc simulation software is used to establish the simulation models of zero sequence and negative sequence directional elements, distance elements and longitudinal current differential elements of relay protection devices. Under the non preset working conditions of complex large power grid, the DC voltage imbalance characteristics, AC and DC power distribution characteristics and fault harmonic characteristics are analyzed, and the adaptability and weak points of relay protection action are analyzed according to these characteristics. The experimental results show that this method is more accurate in obtaining the characteristic value of DC fault under non preset conditions of complex large power grid, and can effectively obtain the action of relay protection devices at different times when non preset conditions occur in complex large power grid and when the setting value of DC low voltage is different. It is concluded that its action adaptability is better when the setting value of DC low voltage is 1.5 kA, and when the setting value of DC low voltage is 5.5 kA and 15 kA, The fault time is short, and the relay protection action has weak points.

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

AbstractFault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near‐Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non‐linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity‐based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM‐based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.

Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China.

In the context of rapid urbanization and limited land amount, it is essential to scientifically evaluate the urban land green use efficiency (ULGUE) to promote regional sustainable development. Current studies are of great value for enriching the theoretical system and application research of ULGUE. Still, most of them only consider industrial pollution but ignore carbon emission as an essential environmental influencing indicator. This paper introduced carbon emissions into the input-output indicator system, measured ULGUE of 57 cities in the Yellow River Basin (YRB) over the 2004–2017 periods using the super-efficiency slacked-based measure (Super-SBM) model, analyzed its spatio-temporal patterns with the kernel density estimation (KDE) model and spatial autocorrelation model, and then identified the influencing factors with the Spatial Durbin model (SDM). As shown by the results, firstly, the ULGUE in the YRB over the 2004–2017 periods showed a trend of first decreasing and then increasing. Secondly, the ULGUE exhibited spatio-temporal imbalance characteristics across the YRB. Thirdly, ULGUE was the interaction of multiple indicators, and its influencing factors had spatial spillover effects. All in all, this paper is fundamental to the high-quality development of cities in the background of the Chinese policy of “carbon peak, carbon neutralization”.

Оптимизация страбизмологического обследования пациентов при планировании хирургического лечения первичной гиперфункции нижних косых мышц

Purpose. To perform a comparative assessment of the results of strabismological examination of patients with primary inferior oblique muscle overaction according to the generally accepted and proposed methods. To analyze the change in the surgical procedure protocol depending on the results obtained. Material and methods. The research included 123 patients with a bilateral symmetric primary inferior oblique muscle overaction. The generally accepted method consisted in measuring the heterotropia magnitude when moving the gaze while turning the head left right and in measuring the magnitude of when moving the gaze following the ophthalmoscope beam when it is horizontally displaced upwards by 25° and downwards by 35° from 33 cm (assessment of V-pattern and its severity). The proposed method consisted in measuring the hypertropia magnitude of the adducted eye with the maximum fixing movement of the abducted eye gaze and in measuring the heterotropia magnitude with the maximum gaze movement upwards and downwards. Results. After the strabismological examination was carried out according to the proposed method, the average magnitude of hypertropia in adduction and V-pattern in the study group was statistically significantly (p&lt;0.05) changed in the diagnostic protocols. A more prominent magnitude of hypertropia was revealed in 110 patients (89.43%). A more prominent V-pattern was diagnosed in 117 patients (95.12%). Studying the specifics of the oculomotor system disorders in patients resulted in optimization of the surgical procedure protocol in 83 patients according to the proposed method (67.48%). Conclusion. The average under-diagnosis of hypertropia and V-pattern during the examination according to the generally accepted method is 3.98±2.39° and 3.72±2°, respectively. Clarification of the oculomotor imbalance characteristics after the examination according to the proposed method requires a change in the surgical procedure protocol in more than 67% of cases. Key words: primary inferior oblique muscle overaction, V-pattern, gaze position, Botox, partial marginal myotomy, myectomy

Post-Graduate Geographical Education in China: Can Talents Meet the Need of Sustainable Development?

As widely acknowledged and targeted in Transforming Our World: The 2030 Agenda for Sustainable Development, talents education and training is an important measure to systematically solve the problem of economically, societally, resource, and environmentally sustainable development, and so as the post-graduate geographical talents education and training. Whether post-graduate geographical talents education and training can meet the need of sustainable development is an increasingly significant issue in geography science. Therefore, from the perspective of population scale, education quality, and education input, taking Chinese post-graduate geographical education as an example, the paper empirically investigated the spatial differentiation and talents production mechanism. With the support of spatial analysis tools by ArcGIS and GeoDa software, the strong inter-regional differentiation and imbalance characteristics of post-graduate geographical talents education were detected, outlining a general east-west geographical pattern in China. Moreover, the spatial production mechanism of post-graduate geographical talents has its own global and national scale, regional comprehensive and province-related characteristics, and production of the talents education and training in human geography, physical geography, and cartography. GIS also has its own focuses and demands.

Robust Attitude Control Scheme for Constantly Spinning Small-Sized Quadrotor in the Presence of Imbalance Characteristics

Unlike normal yaw motion, a constantly spinning quadrotor experiences severe instability while rotating at high angular speeds owing to its imbalancecharacteristics originating from the off-center parts. To resolve this issue, this study proposes a novel composite robust control scheme for a constantly spinning quadrotor, with the aim to achieving omnidirectional sensing by scanning its surroundings with a single field of view limited sensor. The proposed scheme makes use of both offline calibration and online disturbance observation to compensate for the variation in the center of mass and misalignment between the body-fixed frame and principal axis of rotation, respectively. Additionally, the reference inputs are reshaped, or leading in time, to achieve the desired flying direction. It is demonstrated through an experiment that the proposed control scheme compensates well for the effect of the imbalance characteristics of the quadrotor, thus achieving good omnidirectional sensing with more stable rotation.

Feasible Distributed Energy Supply Options for Household Energy Use in China from a Carbon Neutral Perspective.

This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China’s regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

Spatiotemporal Differentiation of the School-Age Migrant Population in Liaoning Province, China, and Its Driving Factors

This study analyzed the spatiotemporal distribution and driving factors of the floating school-age population in Liaoning Province, China from 2008 to 2020 using county-level statistical education data combined with spatial autocorrelation and the multiscale geographically weighted regression model. The major findings are as follows. From 2008 to 2020, the distribution of the school-age migrant population exhibited obvious spatial imbalance characteristics both in terms of the number and proportion of school-age migrants. Specifically, the school-age migrant population was concentrated in the municipal districts of large and medium-sized cities and continued to increase over time in the suburbs of large and medium-sized cities. Over the past 12 years, the distribution of the school-age migrant population in Liaoning Province exhibited significant spatial autocorrelation. From the number of school-age migrants, the cold and hot spot area expanded. Conversely, from the proportion of school-age migrants, the cold and hot spot area decreased gradually, whereas the cold spot area became more diffuse. Regarding the driving factors, the quantity and quality of teaching staff, the quality of teaching equipment and conditions, and the quality of the education environment played a role in promoting or restraining the differentiation of the school-age migrant population in Liaoning Province. Moreover, the degree of influence of the driving factors exhibited substantial spatial differences.

Bingham deep neural and oppositional fish swarm optimized protein structure prediction

It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several essential protein prediction methods have been proposed. However, those existing prediction methods were not satisfactory because to low sensitivity to imbalance characteristics. To address this issue, this paper presents a novel secondary protein structure prediction method, called, Bingham Deep Convolutional-based Oppositional Artificial Fish Optimized (BDC-OAFO). First, a protein structure identification framework, called, Bingham Distributed Deep Convolutional (BDDC) is designed to identify the essential proteins by eliminating the imbalanced learning issue. Next, secondary structure prediction framework, called, Oppositional Artificial Fish Swarm Optimization is proposed that obtain precise prediction results. Then, predicting secondary protein structure by emulating three biological behaviors of artificial fishes, including foraging behavior, following behavior, swarming behavior in which process, proximal count, oppositional function and Gaussian function are utilized. To evaluate the performance of BDC-OAFO method, we conduct experiments on Protein Data Bank dataset the experimental results show that our method BDC-OAFO achieves a better performance for identifying essential proteins and precise prediction in comparison with several other well-known prediction methods, which confirms the significance of BDC-OAFO. Communicated by Ramaswamy H. Sarma

Equidistant distribution loss for person re-identification

Person re-identification (re-id) is a technology that matches images of a certain pedestrian among non-overlapping cameras. Recently, many approaches based on deep learning have been studied comprehensively. Most of these methods try to learn discriminative pedestrian features via elaborated network architectures and one or multi loss functions to maximize inter-class separability and intra-class compactness. However, the class imbalance characteristics from the datasets are ignored, and the discriminative information cannot be completely exploited. In this paper, we propose a novel loss function termed as Equidistant Distribution Loss (EDL) to improve the imbalance problem that exists in re-id tasks. Specifically, we first normalize the learned features in the embedding layer and the weights in the last fully connected layer, so that both the features and the weights can be projected on a hypersphere space. After that, we directly impose an equidistance constraint among the weights to guide the learned features spread uniformly in the sphere space. The proposed EDL can effectively address the local squeeze or imbalance problem caused by imbalanced samples, making full use of feature space and profiting the performance. Extensive experiments on re-id datasets, including Market-1501, DukeMTMC-reID are conducted to demonstrate the preferable performance of the proposed method.

Non-Alcoholic Fatty Liver Disease and Hypokalemia in Primary Aldosteronism Among Chinese Population.

BackgroundIn recent years, evidence that aldosteronism is a risk factor for metabolic disorders has increased. This study was designed to investigate the role of nonalcoholic fatty liver disease (NAFLD) and hypokalemia in primary aldosteronism (PA).MethodsA total of 222 patients diagnosed with PA and 222 non-PA patients were included in our study. Demographic data, medical histories, clinical evaluations, complete blood counts, serum biochemical analyses, aldosterone and potassium levels were obtained. Data are presented as the means ± standard deviation (SD). To compare the parameters between cases and controls, Student’s t-tests or Mann-Whitney U tests were used for continuous variables, and χ2 tests were used for categorical variables. Pearson correlation analysis was used to define relationships between pairs of parameters. A two-sided P < 0.05 was considered statistically significant. Multivariate logistic regression was performed to assess the independent effects of potassium and other metabolic variables on NAFLD in PA patients.ResultsThe diagnosis of NAFLD was more common in PA patients (n=222, 35.1%) than in non-PA subjects (29.7%). PA patients with and without NAFLD had similar metabolic imbalance characteristics. In PA patients with hypokalemia, relatively higher prevalences of NAFLD (44% vs. 27%, P < 0.05) and diabetes mellitus (19.8% vs. 9.9%, P < 0.05) were observed. Hypokalemic PA patients had a worse metabolic status than PA patients without hypokalemia, including higher body mass index (BMI) (25.4 ± 3.4 vs. 24.1 ± 3.9 kg/m2, P < 0.05), more severe dyslipidemia as well as insulin resistance, higher serum uric acid levels (354 ± 95 vs. 319 ± 87 μmol/L, P < 0.01) and aggravated inflammation.ConclusionThe prevalence of NAFLD was higher in PA patients than in non-PA patients, although the patterns of obesity, dyslipidemia and insulin resistance were similar. Hypokalemic PA patients had a worse metabolic status than normokalemic PA patients. This study provides new insights that can inform further mechanistic studies about metabolic imbalance in patients with aldosteronism.