With the successful application of Artificial intelligence (AI) technology in a range of information intelligence projects similar to bridge, a more scientifically rigorous new field has been opened by studying the logic of AI technology. AI has demonstrated the ability to challenge human intelligence projects such as bridge. This study focuses on the bidding system of bridge, obtains real simulated data of bridge bidding, combines basic card features with artificially constructed features, and analyzes the key issues faced by bidding research. A neural network structure based on Deep Q-Network (DQN) is proposed to find a suitable neural network structure for bidding game research. Through the comparison of testing costs and total bidding times, as well as the analysis of feature ablation experiments, it is found that different features have different impacts on the machine learning model performance of the bridge bidding system. Features such as "number of each suit cards," "high-card points," and "honors" have a positive effect on the model performance, while other features have limited impact on the model performance. The application of Deep Learning methods in bridge bidding algorithm research contributes to the advancement of machine gaming and AI industries.

Ankle-foot rehabilitation robots have garnered significant attention due to their importance in assisting patients with rehabilitation training and enhancing treatment efficiency. This review systematically explores the current state of research on ankle-foot rehabilitation robots, covering structural design, control methods, and clinical application cases. Firstly, the article analyzes the advantages of rigid actuators in terms of control precision and the benefits of flexible actuators in adaptability, highlighting the potential of rigid-flexible coupled structures in improving rehabilitation outcomes. Secondly, it discusses the current control strategies for ankle-foot rehabilitation robots, including passive control, active control, and impedance control, and examines their applications at different rehabilitation stages. Finally, the article summarizes clinical application cases of ankle-foot rehabilitation robots and suggests that future research should focus on optimizing structural design, developing intelligent control algorithms, and promoting widespread clinical application of these robots to provide more personalized and efficient rehabilitation solutions.

The main problems in transportation are traffic accidents, increasingly slow traffic flow, and pollution. It requires huge infrastructure investments in traditional transportation systems to solve. The advent of autonomous driving techniques with intelligent transportation systems (ITS) can overcome these problems. This paper investigates the integration of autonomous driving technology with intelligent transportation systems (ITS) and explores the latest case studies and research findings on this integration. The purpose is to emphasize the crucial role of merging autonomous driving technology with ITS in-boosting transportation efficiency, ensuring road safety, and fostering sustainability. The study delves into innovations such as intelligent traffic management systems and autonomous logistics distribution vehicles. Key findings highlight the potential of this integration to enhance traffic safety, and efficiency, and reduce congestion and accidents. However, unresolved challenges persist in system integration, data correlation, and hazard detection. The paper concludes by emphasizing the transformative potential of autonomous driving within ITS while proposing future research directions to address these challenges.

Capacitive strain sensors play a crucial role in tracking human joint motion. However, for a long time, the sensitivity GF of the strain sensor is theoretically limited to 1 and is difficult to improve under low strain. This study aims to enhance the sensitivity for more accurate joint motion monitoring by utilizing an auxetic structure (rotating squares system) combined with a wrinkled design to replace the traditional dielectric layer. This novel approach leverages the negative Poisson’s ratio property of auxetic materials. In this study, inorganic silica gel (SiO_2) and conductive fabric are used to fabricate the dielectric layer and the electrodes, respectively. The strain sensor (80.0mm*20.0mm*1.0mm) with the proposed design achieves a maximum GF of 10.83 and a minimum GF of 4.07. Finally, the strain sensor is tested by measuring the bending angles of the knee joint during walking, jumping, a jump shot, and a fadeaway.

This study selects nine cities within the Nanjing urban agglomeration. Based on the traditional gravity model, the study constructs an evaluation index system for assessing the comprehensive strength of cities, applies the entropy method to calculate the cities’ overall strength, and uses an improved potential model to investigate the internal spatial structure and development relationships within the urban agglomeration. The results indicate that from 2010 to 2020, the southern region of the Nanjing urban agglomeration formed the strongest interaction zone. Nanjing exhibits a clear agglomeration effect, with some peripheral cities benefiting from its radiation effect, while Huai’an and Wuhu, located at the northern and southern ends of the agglomeration, lag in development and show relatively slow growth. Based on these findings, the study proposes a strategy for developing the Nanjing urban agglomeration into a hierarchical urban system of “one core, one sub-core, and three tiers” and a spatial structure of “one center, three circles, and four clusters,” aiming to create an “imbalanced balance” in spatial distribution.

The shape estimation function can help solve end positioning or gripping control of soft robots. However, there is a lack of sensing and modeling techniques for accurate deformation estimation and soft robots with axial elongation, e.g., pneumatic soft robotic graspers. This paper presents a self-shape sensing pneumatic soft grasper with integrated liquid metal composite piecewise curvature sensors. Ga-Ln-Sn alloy was used as the basis of the sensor with addition of NdFeB and Ni. Then, a piecewise variable curvature model was developed to predict the deformation of the robotic fingers. A three-fingers soft robotic grasper (working similarly as a two-finger grasper on the 2D working plane) was built to test the performances of the sensor and the model. The result indicated that the grasper is not only capable of self-shape sensing, but also contact detection and gripping object size estimation. By statistical analysis, it is proven valid that the data collected by the sensor is able to go through machine learning processes to achieve gripping object shape identification.

It has been two hundred years since the Carnot cycle was proposed and used to analyze the working process of a heat engine in 1824 by the French engineer Nicolas-Léonard Sadi Carnot. Similarly, the Carnot cycle has been improved and practiced for the same time. Why have the results of Carnot's work been so highly regarded and so widely used for over a century? Moreover, how can people improve on Carnot's research based on the ideal state required by the Carnot cycle more efficiently? This paper will describe the basic principles of the Carnot cycle and how it has been applied to heat engines. Based on recent research, scientists have come up with ideas and methods of improvement. Based on previous experience and research, this thesis will provide a certain summary of what is known about the Carnot cycle and provide some ideas on further improving the direction of Carnot heat engine efficiency afterwards. The data of the paper is cited from China Knowledge and Google Scholar.

Dense coding is a basic protocol for quantum information transfer. We study the noisy and multi-use of quantum channels for dense coding protocols. For this purpose, we replace the standard channel, namely the EPR pair by a noisy two-qubit pure state. We construct the measurement Alice carries out to encode her message to be transferred to Bob. It turns out that the resulting states are not all orthogonal, and we propose a probability function for success transfer. We mathematically maximize the function in terms of the quantum measurements taken by Bob. We further develop the protocol to multi-use channels, and investigate the probability function for success transfer.

The exploration of the relationship between fractals and chaotic systems not only contributes to advancing fundamental disciplines such as theoretical physics and mathematics, but also offers novel perspectives and methodologies for solving interdisciplinary practical problems, thereby demonstrating extensive application prospects and potential value. By thoroughly investigating the intricate connections between these two concepts, we can gain deeper insights into the underlying mechanisms that drive complex systems and harness their principles to tackle challenges in a wide array of fields. This holistic approach fosters a more comprehensive understanding and utilization of fractals and chaotic systems, enabling researchers to unravel the mysteries of complex phenomena and develop innovative solutions. Ultimately, this interdisciplinary exploration drives innovation and progress across multiple disciplines, showcasing the transformative power of fractals and chaotic systems in shaping our understanding of the world.

In this paper, we address the challenge of illegal trafficking of multiple wildlife species worldwide and propose three major data-driven measures aimed at providing wildlife protection options to law enforcement agencies. For measure 1, taking the number of cases intercepted by law enforcement agencies each year as a measure, a BP neural network based on particle swarm optimization algorithm is established to predict the effect of the measure. The results show that measure 1 can reduce the number of cases by 15.07% in the next five years. For measure 2, the improved SIR Model is creatively applied to predict the measure. The results show that the proposed method can reduce the number of cases by 10.17%. For measure 3, a linear programming model is used to calculate the number of satellites that need to be launched by World Wide Fund for Nature (WWF), and the results show that the scheme reduces the number of cases by 15.08%.