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Marx suggested that it is infeasible and wrong to arrange the economic categories according to the order by which they have worked in history. Their order is determined by their interrelationship in the modern bourgeois society, which is in contrast to their natural sequence or that which is in accordance with the course of history. Sometimes, a logical sequence is precisely opposite to the historical sequence. There are many efforts to be done in the study of China’s economic and social issues with Marxist logical and historical methods. The paper aims to discuss these issues.

When reading Das Kapital, we can clearly see the historical materialism methods. Another method of Marxist political economics is the scientific abstract method.

This is based on the new development idea to carry out scientific and technological innovation and change the focus of development from quantity to quality. With regard to the supply side structural reform as the main focus, people’s ever-growing demand for a better life can be satisfied and the higher level dynamic supply–demand balance can be kept.

In fact, measures to remedy unbalanced and inadequate development of the social principal contradiction have been plainly indicated in the report delivered at the 19th National Congress of the Communist Party of China. This is based on the new development idea to carry out scientific and technological innovation and change the focus of development from quantity to quality.

The shared development concept is crucial for the construction of a socialist political economy with Chinese characteristics. The paper aims to discuss this issue.

This is because shared development constitutes the logic thread of the socialist political economy with Chinese characteristics and the core for the formation and development of its whole system.

China’s modernization is well underway and is following a unique path with its own characteristics, whereby shared development is undoubtedly one of its core values.

In the new era, the development path under the concept of shared development of socialism with Chinese characteristics must adhere to the all-round development of human beings, promote social equity and justice via development, and embrace inclusive growth, specifically, pro-poor growth.

As there are different interpretations of the object of study in the preface to the first edition of Capital (Volume I) by Karl Marx, disagreements arise over the object of study on political economy, which becomes a “difficult problem.” The purpose of the paper is to bring a new solution to the “difficult problem.”

Based on the analysis of the logic of the original text, the authors attempted to give a new interpretation of the “difficult problem” by analyzing the structure of Capital. The object of study of political economy is “the relations of production in the broad sense” of the capitalist mode of production.

It comprises relations of production in the narrow sense and exchange relations in the broad sense, and the latter can be divided into exchange relations in the narrow sense and distribution relations. The three of them correspond to Volume I, II and III of Capital, respectively. Consumption in “the four-section theory” is not studied by the political economy.

And the four-section theory is not a part of the theory of Marxist economics but a part of the classical economics criticized by Marx. Therefore, the object of study of socialist political economy with Chinese characteristics is “the relations of production in the broad sense” regarding the socialist mode of production with Chinese characteristics, which is different from the capitalist relations of production in the broad sense.

Under the prior information that upper and lower bounds of the random quantity are symmetric with respect to the best estimate, this paper analyses the Bayesian prior distribution assignment using the principle of maximum entropy. With the exact lower and upper bounds, it approves uniform for the probability density function of the quantity and it has a curvilinear trapezoidal form for the inexact lower and upper bounds.

Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method.

We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method.

To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors.

The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light.

The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method.

For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out.

The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel.

The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

Reductions in emissions intensity have been expressed in commitments of many countries’ intended nationally determined contribution. Energy structure adjustment is one of the main approaches to reduce carbon emissions. This paper aims to study the causal relationship between carbon emission intensity and energy consumption structure in China based on path analysis.

After data collection, this paper performs correlation analysis, regression and path analysis.

Correlation results display clear collinearity among energy structure variables. Regression finds that coal, oil, natural gas and technology can be used as indicators for carbon intensity while primary electricity has been excluded. Path analysis shows that coal had the largest direct and positive impact on emission intensity. Natural gas had a positive direct and negative indirect effect through its negative relationship with coal on emission intensity. Technology has the largest negative elasticity while all fossil energies are positive. Results indicate a negative effect of energy structure adjustment on China’s national carbon intensity.

Given the major role of China in global climate change mitigation, significant future reductions in China’s CO₂ emissions will require transformation toward low-carbon energy systems. Considering the important role in mitigating global climate change, China needs to transition toward a low-carbon energy system to significantly reduce its carbon intensity in the future.

The problem of environmental pollution is becoming increasingly severe, and international consensus confirms the need for eco-friendly consumption. Worldwide, the eco-friendly food market is booming, so understanding consumers’ motivations to purchase these foods is crucial. This paper aimed to construct a model explaining consumers’ intentions to purchase eco-friendly food by combining stimuli-organism-response (SOR) and signalling theories and exploring the mechanisms by which macro- and micro-signals impact perceptions of value and consumers’ subsequent willingness to purchase eco-friendly food.

An online questionnaire was distributed through the Qualtrics platform, and the completed questionnaires were collected in March and April 2023. The study used partial least squares structural equation modelling (PLS-SEM) to analyse the 331 valid responses received.

The results indicated that trustworthy eco-labels for high-quality and health-promoting products, as conveyed in macro signals, significantly enhanced consumers’ perceptions of functional value. The peer effect and a moderate level of food anthropomorphism conveyed in micro-signals substantially improved their perceptions of social value, whilst the perceived value of products significantly and positively influenced their purchase intentions.

This study explains consumers’ motivations to purchase eco-friendly products. This provides an explanation for the effect of macro- and micro-signals on value perceptions. By integrating the different dimensions of these signals to create a unified research perspective, the paper provides an integrated model, thereby filling a research gap concerning the influence of two-dimensional signals on purchase intention. By supporting eco-friendly food use, the paper contributes to environmental protection and sustainable development.

The purpose of this paper is to develop a mobile social networking service (SNS) addiction scale to measure respondents’ addiction levels.

Drawing on the existing literature on the components model of addiction by Griffiths (2005) and mobile SNS addiction, an initial scale in a five-point Likert-format was developed. It was refined through the pilot study with 100 participants and the main study with 423 participants utilizing factor analysis and Rasch analysis.

Mobile SNS addiction as a behavioral addiction, demonstrated six addiction symptoms: modification, salience, tolerance, withdrawal, conflict and relapse, which were interrelated with each other. The mobile SNS addiction scale developed in this study was found to be psychometrically robust and unidimensional.

The mobile SNS addiction scale consists of nine items, thus making it easier and more convenient to be applied to academic research and clinical practice.

The combined use of factor analysis and the Rasch model could largely reduce potential negative effects associated with limitations of classical test theory and improve the chance of developing a psychometrically robust instrument. The mobile SNS addiction scale covers a range of types of SNSs, thus being more generic. The items in the scale are unidimensionally loaded on the latent construct of mobile SNS addiction and demonstrate measurement invariance across respondents of different demographics.