Modelling unsignalised traffic flow with reference to urban and interurban networks

A new variant of cellular automata (CA) models is proposed, based on Minimum Acceptable Space (MAP) rules, to study unsignalised traffic flow at two-way stop-controlled (TWSC) intersections and roundabouts in urban and interurban networks. Categorisation of different driver behaviour is possible, based on different space requirements (MAPs), which allow a variety of conditions to be considered. Driver behaviour may be randomly categorised as rational, (when optimum conditions of entry are realised), conservative, urgent and radical, with specified probabilities at each time step. The model can successfully simulate both heterogeneous and inconsistent driver behaviour and interactions at the different road features. The impact of driver behaviour on the overall performance of intersections and roundabouts can be quantified and conditions for gridlock determined. Theorems on roundabout size and throughput are given. The relationship between these measures is clearly non-monotonic. Whereas previous models consider these road features in terms of T-intersections, our approach is new in that each is a unified system. Hence, the relationship between arrival rates on entrance roads can be studied and critical arrival rates can be identified under varied traffic and geometric conditions. The potential for extending the model to entire urban and interurban networks is discussed

Unleashing the Power of ChatGPT for Translation: An Empirical Study

The recently released ChatGPT has demonstrated surprising abilities in natural language understanding and natural language generation. Machine translation is an important and extensively studied task in the field of natural language processing, which heavily relies on the abilities of language understanding and generation. Thus, in this paper, we explore how to assist machine translation with ChatGPT. We adopt several translation prompts on a wide range of translations. Our experimental results show that ChatGPT with designed translation prompts can achieve comparable or better performance over professional translation systems for high-resource language translations but lags behind significantly on low-resource translations. We further evaluate the translation quality using multiple references, and ChatGPT achieves superior performance compared to the professional systems. We also conduct experiments on domain-specific translations, the final results show that ChatGPT is able to comprehend the provided domain keyword and adjust accordingly to output proper translations. At last, we perform few-shot prompts that show consistent improvement across different base prompts. Our work provides empirical evidence that ChatGPT still has great potential in translations

Construction of Emergency Adaptability Evaluation Index System for High-Rise Residential Buildings Based on Major Public Health Emergencies

With the continuous variation and increasing infectivity of novel coronavirus, people are forced to stay at home for a long time, and their lives and lives are constantly threatened by the virus. In order to provide scientific basis for evaluating and optimizing the epidemic prevention and control capability of high-rise residential buildings under public health emergencies, the evaluation index system of emergency adaptive performance of high-rise residential buildings is constructed. First of all, this paper uses Delphi method to consult experts in the form of questionnaire survey, and determines the framework of evaluation index system through two rounds of index screening process. Then use the analytic hierarchy process to determine the weight value of the evaluation index system, and finally check the consistency of the index weight. As a result, the emergency adaptation performance evaluation system of high-rise residential buildings under public health emergencies is obtained

Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition

RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion. Although many RGB-D based action and gesture recognition approaches have demonstrated remarkable results by utilizing highly integrated spatio-temporal representations across multiple modalities (i.e., RGB and depth data), they still encounter several challenges. Firstly, vanilla 3D convolution makes it hard to capture fine-grained motion differences between local clips under different modalities. Secondly, the intricate nature of highly integrated spatio-temporal modeling can lead to optimization difficulties. Thirdly, duplicate and unnecessary information can add complexity and complicate entangled spatio-temporal modeling. To address the above issues, we propose an innovative heuristic architecture called Multi-stage Factorized Spatio-Temporal (MFST) for RGB-D action and gesture recognition. The proposed MFST model comprises a 3D Central Difference Convolution Stem (CDC-Stem) module and multiple factorized spatio-temporal stages. The CDC-Stem enriches fine-grained temporal perception, and the multiple hierarchical spatio-temporal stages construct dimension-independent higher-order semantic primitives. Specifically, the CDC-Stem module captures bottom-level spatio-temporal features and passes them successively to the following spatio-temporal factored stages to capture the hierarchical spatial and temporal features through the Multi- Scale Convolution and Transformer (MSC-Trans) hybrid block and Weight-shared Multi-Scale Transformer (WMS-Trans) block. The seamless integration of these innovative designs results in a robust spatio-temporal representation that outperforms state-of-the-art approaches on RGB-D action and gesture recognition datasets.Comment: ACM MM'2

Assessment of students’ cognitive–affective states in learning within a computer-based environment: Effects on performance

Students’ cognitive-affective states are human elements that are crucial in the design of computer-based learning (CBL) systems.This paper presents an investigation of students’ cognitiveaffective states (i.e., engaged concentration, anxiety, and boredom) when they learn a particular course within CBL systems.The results of past studies by other researchers suggested that certain cognitive-affective states; particularly boredom and anxiety could negatively influence learning in a computer-based environment.This paper investigates the types of cognitive-affective state that students experience when they learn through a specifi c instance of CBL (i.e., a content sequencing system). Further, research was carried to understand whether the cognitive-affective states would infl uence students’ performance within the environment.A one-way between-subject-design experiment was conducted utilizing four instruments (i) CBL systems known as IT-Tutor for learning computer network, (ii) a pre-test, (iii) a post-test, and (iv) self-report inventory to capture the students’ cognitive-affective states. A cluster analysis and discriminant function analysis were employed to identify and classify the students’ cognitiveaffective states.Students were classifi ed according to their prior knowledge to element the effects of it on performance.Then,non-parametric statistical tests were conducted on different pairs of cluster of the cognitive-affective states and prior knowledge to determine differences on students’ performance. The results of this study suggested that all the three cognitive-affective states were experienced by the students. The cognitive-affective states were found to have positive effects on the students’ performance.This study revealed that disengaged cognitive-affective states, particularly boredom can improve learning performance for lowprior knowledge students

Synchronization of reaction–diffusion Hopfield neural networks with s-delays through sliding mode control

Synchronization of reaction–diffusion Hopfield neural networks with s-delays via sliding mode control (SMC) is investigated in this paper. To begin with, the system is studied in an abstract Hilbert space C([–r; 0];U) rather than usual Euclid space Rn. Then we prove that the state vector of the drive system synchronizes to that of the response system on the switching surface, which relies on equivalent control. Furthermore, we prove that switching surface is the sliding mode area under SMC. Moreover, SMC controller can also force with any initial state to reach the switching surface within finite time, and the approximating time estimate is given explicitly. These criteria are easy to check and have less restrictions, so they can provide solid theoretical guidance for practical design in the future. Three different novel Lyapunov–Krasovskii functionals are used in corresponding proofs. Meanwhile, some inequalities such as Young inequality, Cauchy inequality, Poincaré inequality, Hanalay inequality are applied in these proofs. Finally, an example is given to illustrate the availability of our theoretical result, and the simulation is also carried out based on Runge–Kutta–Chebyshev method through Matlab