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Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people's mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. [ABSTRACT FROM AUTHOR]

As a high-sensitivity and non-invasive technique, optical molecular tomography (OMT) can locate and visualize tissue quantitatively in three dimensions by collecting surface photons generated from luminescent biomarkers. It has great potential for tumor detection, surgery guidance, and pharmacokinetics research studies. However, due to the limited measurable surface photons and the highly scattered feature of photons, the reconstruction of OMT is highly ill-posed and ill-conditioned, which limits the performance in practice. To improve the accuracy of OMT, plenty of studies focus on precisely modeling photon propagation and accurately reconstructing light source. Since these methods are carried out based on different theories, we review famous solutions for the reconstruction in terms of the photon propagation models and inverse reconstruction algorithms to make an overall understanding for the methods of OMT reconstruction. Additionally, some prospects are listed to provide possible research orientation that may benefit future research. [ABSTRACT FROM AUTHOR]

This article reports on a large-scale survey study investigating EFL learners' beliefs about oral corrective feedback (CF). A 44-item questionnaire tapping into learners' beliefs about corrective feedback was administered to 2670 Chinese EFL learners. These learners were from 15 Chinese universities in 14 provinces and municipalities across the country, which were stratified in accordance with per capita GDP values. An exploratory factor analysis generated seven factors: general attitude toward CF, CF timing, output-prompting CF, uptake, input-providing CF, peer CF, and gravity of errors. The results indicate that participants had an overall positive attitude toward CF, and they showed more preferences for immediate CF over delayed CF, and output-prompting CF over input-providing CF. Additionally, learners were slightly positive about the efficacy of uptake and peer correction. Findings also suggest some consistency between the Chinese learners' CF beliefs and empirical SLA research about the effectiveness of error correction, as well as the variance of CF-related beliefs across educational contexts.

This study explored how secondary school EFL teachers engaged in professional learning opportunities when they participated in the development of ELT materials. While increasing research has examined language teaching materials and teacher learning, the two research fields have remained largely disconnected, hindering the potential of materials writing for professional learning. With eight teachers as focal participants, the data included in-depth interviews, written reflections, and official documents. Findings showed that the EFL teachers took advantage of materials writing for their professional learning in the areas of policy, theory, and practice. Their engagement with the national curriculum standards, language, and language learning theories, together with local classroom realities in relation to teaching materials, reciprocally interacted with their professional learning. Based on the findings, we recommend that professional development programs include materials writing by localizing the content, clarifying its objectives, teaming teachers with researchers, and creating feedback loops. [ABSTRACT FROM AUTHOR]

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide‐and‐conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals. [ABSTRACT FROM AUTHOR]

Lignin dissolution and the fluid behaviour of reaction systems play important roles in the conversion rate of lignin. This study investigated the solubility of enzymatic hydrolysis lignin (EHL) in 1-butyl-3-methylimidazole bisulphate ethanol solutions ([BMIM]HSO4/ethanol) and the dynamic shear rheological characterisation of EHL-[BMIM]HSO4/ethanol. Different with [BMIM]HSO4/water, [BMIM]HSO4/ethanol exhibited excellent abilities to dissolve EHL at [BMIM]HSO4% 20%–90%, with the maximum EHL solubility obtained at 40%–70%. The dynamic shear rheological characterisation strongly depended on temperatures, [BMIM]HSO4% and EHL%, exhibited as pseudoplastic fluid at higher [BMIM]HSO4% and EHL%, and Dilatant fluid at lower [BMIM]HSO4% and EHL%. Adjusting an appropriate [BMIM]HSO4% and EHL%, EHL-[BMIM]HSO4/ethanol was Newtonian fluid with the viscosity stable. EHL was regenerated from EHL-[BMIM]HSO4/ethanol without obvious changes in the chemical structure and thermal behaviour. This study provided ideas for the design of the [BMIM]HSO4/ethanol system and the optimisation of conversion processes for the economic utilisation of lignin. [ABSTRACT FROM AUTHOR]