Visible Light-Driven Alkyne-Grafted Ethylene-Bridged Azobenzene Chromophores for Photothermal Utilization

Molecular photoswitches are considered to be important candidates in the field of solar energy storage due to their sensitive and reversible bidirectional optical response. Nevertheless, it is still a daunting challenge to design a molecular photoswitch to improve the low solar spectrum utilization and quantum yields while achieving charging and discharging of heat without solvent assistance. Herein, a series of visible-light-driven ethylene-bridged azobenzene (b-Azo) chromophores with different alkyne substituents which can undergo isomerization reactions promoted in both directions by visible light are reported. Their visible light responsiveness improves their solar spectrum utilization while also having high quantum yields. In addition, as the compounds are liquids, there is no need to dissolve the compounds in order to exploit this switching. The photoisomerization of b-Azo can be adjusted by alkyne-related substituents, and hexyne-substituted b-Azo is able to store and release photothermal energy with a high density of 106.1 J·g⁻¹, and can achieve a temperature increase of 1.8 °C at a low temperature of −1 °C.     

波束开角对多普勒测频结果的影响*

具有不同波束开角的声学多普勒测速设备未标定的测速结果存在偏差。基于水底椭圆散射模型研究了波束开角对声学多普勒测速结果的影响,对提高声学测速性能具有实际意义。水底椭圆散射模型下,足印上不同方位传播损失和散射强度存在差异,导致频谱不对称,测频结果偏小。利用不对称系数量化频谱不对称程度,若波束开角增大,方位不对称系数随之增大,测频偏差增大,推导了波束开角导致的测频偏差解析式。分析仿真和外场试验数据测频结果可知,不同多普勒频移下测频偏差和理论计算值相符,不同波束开角下实际测频偏差与理论公式计算的测频偏差均随波束开角增大而增大。     

地铁沿线建筑内噪声多维度感受影响机制

随着城市铁路系统的快速发展,铁路沿线建筑内人群对地铁运行产生噪声的抱怨逐渐增多,为控制室内地铁噪声对人群所产生的负面影响,有必要对地铁噪声产生的多维度负面情绪进行评估。采集了79名受试者对建筑内地铁噪声的多维度负面感受(压抑感,不舒适度和不满意度),基于偏最小二乘法分析了主客观影响因素对多维度负面感受的作用机制,并比较了多维度负面感受在反映地铁噪声影响上的差异。结果表明,多维度负面感受主要取决于主观响度和声学参数,活动干扰度、地铁噪声的敏感度、厌烦度和适应性的影响明显更弱;3个多维度负面感受中,相比于压抑感,不舒适度和不满意度受主观响度的影响轻微更显著,在相同的主观响度感受下,不满意度等级最高。 该文可以为地铁沿线建筑物内噪声负面感受的评价和改善提供参考。     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification

Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.     

声波团聚净化尿素造粒塔尾气实验研究*

尿素造粒塔大多采用水洗工艺处理尾气粉尘,存在较为严重的细颗粒粉尘溢出和水汽拖尾问题,因此深入探究声波团聚技术在造粒塔尾气处理方面的应用。通过实验研究了声波与喷雾对除尘效率的影响,发现声波单独作用时在3500 Hz达到最佳除尘效率,为74.49%,协同喷雾时2600 Hz即可实现相近的除尘效率,为73.83%。进一步探究其中机理,采用以粒径测试为依据的团聚效率作为评价指标,结果显示,2600 Hz声波与喷雾单独作用时团聚效率分别为30.37%和28.82%,协同作用时团聚效率大幅提升至57.95%。该研究可为后续造粒塔工程改造提供理论与数据支持。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

Facile Synthesis of Tricyclic 1,2,4-Oxadiazolines-Fused Tetrahydro-Isoquinolines from Oxime Chlorides with 3,4-Dihydroisoquinoline Imines

A mild and efficient strategy for the synthesis of tricyclic 1,2,4-oxadiazolines-fused tetrahydro-isoquinolines derivatives via [3 + 2] cycloaddition reaction is reported. The reactions provided the functionalized tricyclic 1,2,4-oxadiazolines in high yields (up to 96%). This protocol is simple and easy to handle. Moreover, a gram-scale experiment further highlights the synthetic utility. The chemical structure of the product was determined by X-ray single-crystal structure analysis. A possible mechanism for this transformation is proposed to explain the reaction process.     

Thermo-Responsive ZnPc-g-TiO2-g-PNIPAM Photocatalysts Sensitized with Phthalocyanines for Water Purification under Visible Light

A novel thermo-responsive 2,9(10),16(17),23(24)-tetrakis[(3-carboxyacrylamide) phthalocyaninato] zinc (ZnPc)-g-TiO₂-g-poly(N-isopropylacrylamide) (PNIPAM) photocatalyst modified with phthalocyanines was prepared. The photocatalyst exhibited thermo-responsive properties due to the introduction of PNIPAM, which performed recovery for reuse above the lower critical solution temperature (LCST, about 26 °C). ZnPc-g-TiO₂-g-PNIPAM effectively expanded the light response range to the visible light region and inhibited the recombination of electron–hole pairs, which enhanced the performance of the photocatalyst. As expected, ZnPc-g-TiO₂-g-PNIPAM (0.3 g/L) exhibited excellent photocatalytic performance for the removal of Rhodamine B (RhB, 1.0 × 10⁻⁵ mol/L) and methylene blue (MB, 1.0 × 10⁻⁵ mol/L) under visible light, which reached 97.2% and 88.6% at 20 °C within 40 min, respectively. Furthermore, the influence of temperature upon photocatalytic performance was also investigated. When the temperature increased from 20 °C to 45 °C, the removal of RhB decreased by approximately 53.8%. The stability of the photocatalyst demonstrated that the photocatalytic activity was still above 80% for the removal of RhB after 3 cycles. Above all, this work provided an intelligent thermally responsive photocatalyst based on phthalocyanine for water purification under visible light.