Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC

Transit-Based Emergency Evacuation with Transit Signal Priority in Sudden-Onset Disaster

This study presents methods of transit signal priority without transit-only lanes for a transit-based emergency evacuation in a sudden-onset disaster. Arterial priority signal coordination is optimized when a traffic signal control system provides priority signals for transit vehicles along an evacuation route. Transit signal priority is determined by “transit vehicle arrival time estimation,” “queuing vehicle dissipation time estimation,” “traffic signal status estimation,” “transit signal optimization,” and “arterial traffic signal coordination for transit vehicle in evacuation route.” It takes advantage of the large capacities of transit vehicles, reduces the evacuation time, and evacuates as many evacuees as possible. The proposed methods were tested on a simulation platform with Paramics V6.0. To evaluate and compare the performance of transit signal priority, three scenarios were simulated in the simulator. The results indicate that the methods of this study can reduce the travel times of transit vehicles along an evacuation route by 13% and 10%, improve the standard deviation of travel time by 16% and 46%, and decrease the average person delay at a signalized intersection by 22% and 17% when the traffic flow saturation along an evacuation route is 0.8<V/C≤1.0 and V/C>1.0, respectively

High Efficiency Water Splitting using Ultrasound Coupled to a BaTiO 3 Nanofluid

To date, a number of studies have reported the use of vibrations coupled to ferroelectric materials for water splitting. However, producing a stable particle suspension for high efficiency and long-term stability remains a challenge. Here, the first report of the production of a nanofluidic BaTiO3 suspension containing a mixture of cubic and tetragonal phases that splits water under ultrasound is provided. The BaTiO3 particle size reduces from approximately 400 nm to approximately 150 nm during the application of ultrasound and the fine-scale nature of the particulates leads to the formation of a stable nanofluid consisting of BaTiO3 particles suspended as a nanofluid. Long-term testing demonstrates repeatable H2 evolution over 4 days with a continuous 24 h period of stable catalysis. A maximum rate of H2 evolution is found to be 270 mmol h–1 g–1 for a loading of 5 mg l–1 of BaTiO3 in 10% MeOH/H2O. This work indicates the potential of harnessing vibrations for water splitting in functional materials and is the first demonstration of exploiting a ferroelectric nanofluid for stable water splitting, which leads to the highest efficiency of piezoelectrically driven water splitting reported to date

LAI estimation based on physical model combining airborne LiDAR waveform and Sentinel-2 imagery

Leaf area index (LAI) is an important biophysical parameter of vegetation and serves as a significant indicator for assessing forest ecosystems. Multi-source remote sensing data enables large-scale and dynamic surface observations, providing effective data for quantifying various indices in forest and evaluating ecosystem changes. However, employing single-source remote sensing spectral or LiDAR waveform data poses limitations for LAI inversion, making the integration of multi-source remote sensing data a trend. Currently, the fusion of active and passive remote sensing data for LAI inversion primarily relies on empirical models, which are mainly constructed based on field measurements and do not provide a good explanation of the fusion mechanism. In this study, we aimed to estimate LAI based on physical model using both spectral imagery and LiDAR waveform, exploring whether data fusion improved the accuracy of LAI inversion. Specifically, based on the physical model geometric-optical and radiative transfer (GORT), a fusion strategy was designed for LAI inversion. To ensure inversion accuracy, we enhanced the data processing by introducing a constraint-based EM waveform decomposition method. Considering the spatial heterogeneity of canopy/ground reflectivity ratio in regional forests, calculation strategy was proposed to improve this parameter in inversion model. The results showed that the constraint-based EM waveform decomposition method improved the decomposition accuracy with an average 12% reduction in RMSE, yielding more accurate waveform energy parameters. The proposed calculation strategy for the canopy/ground reflectivity ratio, considering dynamic variation of parameter, effectively enhanced previous research that relied on a fixed value, thereby improving the inversion accuracy that increasing on the correlation by 5% to 10% and on R2 by 62.5% to 132.1%. Based on the inversion strategy we proposed, data fusion could effectively be used for LAI inversion. The inversion accuracy achieved using both spectral and LiDAR data (correlation=0.81, R2 = 0.65, RMSE=1.01) surpassed that of using spectral data or LiDAR alone. This study provides a new inversion strategy for large-scale and high-precision LAI inversion, supporting the field of LAI research

Photocatalytic enantioselective α-aminoalkylation of acyclic imine derivatives by a chiral copper catalyst

廉价金属催化的可见光反应为惰性化学键的断裂和重组提供了新的策略，且具有经济、低毒、易操作的特点，是发展绿色合成方法的理想选择。然而，由于这些金属配合物的可见光吸收相对弱、光化学稳定性较差、激发态寿命短等缺点，相关研究较为有限，特别是其催化的可见光不对称合成因涉及自由基等高活性物种转化的立体化学控制，是极具挑战性却有着重大意义的研究方向。课题组在近期工作基础上以易得、易修饰的手性噁唑啉-铜（II）配合物作为催化剂，在可见光照射下实现了非环亚胺的高对映选择性α-胺基烷基化反应，为手性二胺及其衍生物的制备提供了一条经济、便捷的路线。控制实验充分证明了其机理。该项研究利用单一催化剂发挥多重功效，使转化困难的反应在极为简单、温和的条件得以实现，为多功能催化及绿色合成方法的发展提供了新的思路。 该研究由龚磊副教授指导，实验部分由已毕业硕士生韩博闻（第一作者）、博士生李延军、硕士生余莹合作完成。 此文章并被选为Editors’ Highlights论文。Copper-based asymmetric photocatalysis has great potential in the development of green synthetic approaches to chiral molecules. However, there are several formidable challenges associated with such a conception. These include the relatively weak visible light absorption, short excited-state lifetimes, incompatibility of different catalytic cycles, and the difficulty of the stereocontrol. We report here an effective strategy by means of single-electron-transfer (SET) initiated formation of radicals and photoactive intermediates to address the long-standing problems. Through elaborate selection of well-matched reaction partners, the chiral bisoxazoline copper catalyst is engaged in the SET process, photoredox catalysis, Lewis acid activation and asymmetric induction. Accordingly, a highly enantioselective photocatalytic α-aminoalkylation of acyclic imine derivatives has been accessed. This strategy sheds light on how to make use of diverse functions of a single transition metal catalyst in one reaction, and offers an economic and simplified approach to construction of highly valuable chiral vicinal diamines.We gratefully acknowledge funding from the National Natural Science Foundation of China (grant no. 21572184), the Natural Science Foundation of Fujian Province of China (grant no. 2017J06006), and the Fundamental Research Funds for the Central Universities (grant no. 20720190048).研究工作得到国家自然科学基金（21572184）、福建省杰出青年基金（2017J06006）、厦门大学校长基金（20720190048）、南强青年拔尖人才计划（B类）等的支持

Dynamical entropy in Banach spaces

We introduce a version of Voiculescu-Brown approximation entropy for isometric automorphisms of Banach spaces and develop within this framework the connection between dynamics and the local theory of Banach spaces discovered by Glasner and Weiss. Our fundamental result concerning this contractive approximation entropy, or CA entropy, characterizes the occurrence of positive values both geometrically and topologically. This leads to various applications; for example, we obtain a geometric description of the topological Pinsker factor and show that a C*-algebra is type I if and only if every multiplier inner *-automorphism has zero CA entropy. We also examine the behaviour of CA entropy under various product constructions and determine its value in many examples, including isometric automorphisms of l_p spaces and noncommutative tensor product shifts.Comment: 33 pages; unified approach to last three sections give

Mind Your Data! Hiding Backdoors in Offline Reinforcement Learning Datasets

A growing body of research works has focused on the Offline Reinforcement Learning (RL) paradigm. Data providers share large pre-collected datasets on which others can train high-quality agents without interacting with the environments. Such an offline RL paradigm has demonstrated effectiveness in many critical tasks, including robot control, autonomous driving, etc. A well-trained agent can be regarded as a software system. However, less attention is paid to investigating the security threats to the offline RL system. In this paper, we focus on a critical security threat: backdoor attacks. Given normal observations, an agent implanted with backdoors takes actions leading to high rewards. However, the same agent takes actions that lead to low rewards if the observations are injected with triggers that can activate the backdoor. In this paper, we propose Baffle (Backdoor Attack for Offline Reinforcement Learning) and evaluate how different Offline RL algorithms react to this attack. Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack. More specifically, Baffle modifies $10\%$ of the datasets for four tasks (3 robotic controls and 1 autonomous driving). Agents trained on the poisoned datasets perform well in normal settings. However, when triggers are presented, the agents' performance decreases drastically by $63.6\%$, $57.8\%$, $60.8\%$ and $44.7\%$ in the four tasks on average. The backdoor still persists after fine-tuning poisoned agents on clean datasets. We further show that the inserted backdoor is also hard to be detected by a popular defensive method. This paper calls attention to developing more effective protection for the open-source offline RL dataset.Comment: 13 pages, 6 figure

An Ileal Crohn's Disease Gene Signature Based on Whole Human Genome Expression Profiles of Disease Unaffected Ileal Mucosal Biopsies

Previous genome-wide expression studies have highlighted distinct gene expression patterns in inflammatory bowel disease (IBD) compared to control samples, but the interpretation of these studies has been limited by sample heterogeneity with respect to disease phenotype, disease activity, and anatomic sites. To further improve molecular classification of inflammatory bowel disease phenotypes we focused on a single anatomic site, the disease unaffected proximal ileal margin of resected ileum, and three phenotypes that were unlikely to overlap: ileal Crohn's disease (ileal CD), ulcerative colitis (UC), and control patients without IBD. Whole human genome (Agilent) expression profiling was conducted on two independent sets of disease-unaffected ileal samples collected from the proximal margin of resected ileum. Set 1 (47 ileal CD, 27 UC, and 25 Control non-IBD patients) was used as the training set and Set 2 was subsequently collected as an independent test set (10 ileal CD, 10 UC, and 10 control non-IBD patients). We compared the 17 gene signatures selected by four different feature-selection methods to distinguish ileal CD phenotype with non-CD phenotype. The four methods yielded different but overlapping solutions that were highly discriminating. All four of these methods selected FOLH1 as a common feature. This gene is an established biomarker for prostate cancer, but has not previously been associated with Crohn's disease. Immunohistochemical staining confirmed increased expression of FOLH1 in the ileal epithelium. These results provide evidence for convergent molecular abnormalities in the macroscopically disease unaffected proximal margin of resected ileum from ileal CD subjects

Are We Ready to Embrace Generative AI for Software Q&A?

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, specifically by 10% on the overall score. We release the data, analysis scripts, and detailed results at https://anonymous.4open.science/r/GAI4SQA-FD5C.Comment: Accepted by the New Ideas and Emerging Results (NIER) track at The IEEE/ACM Automated Software Engineering (ASE) Conferenc