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Cavity ringdown spectroscopy (CRDS), relying on measuring the decay time of photons inside a high-finesse optical cavity, offers an important analytical tool for chemistry, physics, environmental science, and biology. Through the reflection of a slight amount of phase-coherent light back to the laser source, the resonant optical feedback approach effectively couples the laser beam into the optical cavity and achieves a high signal-to-noise ratio. However, the need for active phase-locking mechanisms complicates the spectroscopic system, limiting its primarily laboratory-based use. Here, we report how passive optical feedback can be implemented in a quantum cascade laser (QCL) based CRDS system to address this issue. Without using any phase-locking loops, we reflect a moderate amount of light (–18.2 dB) to a continuous-wave QCL simply using a fixed flat mirror, narrowing the QCL linewidth from 1.2 MHz to 170 kHz and significantly increasing the laser-cavity coupling efficiency. To validate the method’s feasibility and effectiveness, we measured the absorption line (P(18e), 2207.62 cm−1) of N2O in a Fabry–Perot cavity with a high finesse of ~52000 and an inter-mirror distance of 33 cm. This agile approach paves the way for revolutionizing existing analytical tools by offering compact and high-fidelity mid-infrared CRDS systems.

In recent years, there has been a significant growth in research on emotion expression in the field of human-robot interaction. In the process of human-robot interaction, the effect of the robot’s emotional expression determines the user’s experience and acceptance. Gaze is widely accepted as an important media to express emotions in human-human interaction. But it has been found that users have difficulty in effectively recognizing emotions such as happiness and anger expressed by animaloid robots that use eye contact individually. In addition, in real interaction, effective nonverbal expression includes not only eye contact but also physical expression. However, current animaloid social robots consider human-like eyes as the main emotion expression pathway, which results in a dysfunctional robot appearance and behavioral approach, affecting the quality of emotional expression. Based on retaining the effectiveness of eyes for emotional communication, we added a mechanical tail as a physical expression to enhance the robot’s emotional expression in concert with the eyes. The results show that the collaboration between the mechanical tail and the bionic eye enhances emotional expression in all four emotions. Further more, we found that the mechanical tail can enhance the expression of specific emotions with different parameters. The above study is conducive to enhancing the robot’s emotional expression ability in human-robot interaction and improving the user’s interaction experience.

Ensuring the absence of vulnerabilities or flaws in smart contracts before their deployment is crucial for the smooth progress of subsequent work. Existing detection methods heavily rely on expert rules, resulting in low robustness and accuracy. Therefore, we propose EDSCVD, an enhanced deep learning vulnerability detection model based on dual-channel networks. Firstly, the contract fragments are preprocessed by BERT into the required word embeddings. Next, we utilized adversarial training FGM to the word embeddings to generate perturbations, thereby producing symmetric adversarial samples and enhancing the robustness of the model. Then, the dual-channel model combining BiLSTM and CNN is utilized for feature training to obtain more comprehensive and symmetric information on temporal and local contract features.Finally, the combined output features are passed through a classifier to classify and detect contract vulnerabilities. Experimental results show that our EDSCVD exhibits excellent detection performance in the detection of classical reentrancy vulnerabilities, timestamp dependencies, and integer overflow vulnerabilities.

Frustration is often seen as negative, but as to whether it may have a positive impact on the individual is still undecided. This research was conducted to explore the influence of frustration on altruistic tendency and altruistic level in college students (17–21 years old). By presenting a highly difficult task combined with negative feedback, we effectively induced frustration in Experiment 1 (n = 70). By assessing the donation behavior of participants (n = 54) in a real-life scenario following the experimental manipulation of frustration, we examined the relationship between frustration and altruism in Experiment 2. Results showed that frustrating situations could, on some level, improve altruistic behavior [t(8.834) = 3.013, p = 0.015]. More specifically, among participants who donated, the amount donated was higher in the frustration group compared to the control (fulfillment) group; the proportion of people who donated did not differ by group.

Quantum cascade lasers subject to tilted optical feedback produce periodic oscillations, quasi-periodic oscillations, and low-frequency oscillations. This work presents the modulation characteristics of period-one (P1) oscillations in a quantum cascade laser with tilted optical feedback. The electrical signal at the oscillation frequency is more than 50 dB higher than the noise level, and the electrical linewidth is less than 2.0 kHz. This electrical linewidth is about four orders of magnitude narrower than the optical linewidth (around 16 MHz) of the free-running laser, which suggests that the optical sidebands induced by the P1 oscillations are highly coherent with the main optical mode. In addition, the modulation depth of the optical signal is found to be in the range of 1% to 3.5%. In addition, it is verified in the simulations that the P1 oscillations induce not only amplitude modulation but also frequency modulation due to the phase-amplitude coupling effect.

In recent years, the number of smart contracts deployed on blockchain has exploded. However, the issue of vulnerability has caused incalculable losses. Due to the irreversible and immutability of smart contracts, vulnerability detection has become particularly important. With the popular use of neural network model, there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts. This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts. Subsequently, it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection. These tools are categorized based on their open-source status, the data format and the type of feature extraction they employ. Then we conduct a comprehensive comparative analysis of these tools, selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy. Finally, Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools, we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile, forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection. [ABSTRACT FROM AUTHOR]