(E)-4-[(1-Benzyl-4-benzyl­idene-2,5-di­oxopyrrolidin-3-yl)meth­yl]benzalde­hyde 0.25-hydrate

The crystal structure of the title compound, C26H21NO3·0.25H2O, reveals one stereogenic centre in the mol­ecule. Nevertheless, due to the observed centrosymmetric space group, both enanti­omers are present in the crystal packing. The water molecule of crystallisation is located on a crystallographic inversion center. The mol­ecule contains one five-membered ring (A) and three six-membered rings (benzyl ring B, benzyl­idene ring C and formyl­benzyl ring D). All four rings are not coplanar: the dihedral angles between rings A and B, A and C, and A and D are 70.35 (9), 33.8 (1) and 60.30 (9)°, respectively. In the crystal, pairs of weak C—H⋯O inter­actions lead to the formation of centrosymmetric dimers. Additional C—H⋯O inter­actions link the dimers into chains along [011]

Research on trajectory tracking control for wet clutch engagement based on SMC

AbstractTo improve tracking control quality of the clutch actuator during the wet clutch engagement, models of the clutch actuator were established firstly, including the control cylinder model, flow equilibrium equation and pressure control model. Secondly, taking the clutch output speed as tracking target, the state space equation of the tracking control system was set up and the sliding mode controller (SMC) was designed. Finally, a simulation test was performed. The results show that a higher tracking accuracy as well as a better performance to resist disturbance can be achieved with the proposed sliding control method, compared to PI control. It was also shown that the exponent approaching sliding mode control can produce smaller chattering compared with the constant rate approaching sliding mode control

Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries

Progress and Opportunities of Foundation Models in Bioinformatics

Bioinformatics has witnessed a paradigm shift with the increasing integration of artificial intelligence (AI), particularly through the adoption of foundation models (FMs). These AI techniques have rapidly advanced, addressing historical challenges in bioinformatics such as the scarcity of annotated data and the presence of data noise. FMs are particularly adept at handling large-scale, unlabeled data, a common scenario in biological contexts due to the time-consuming and costly nature of experimentally determining labeled data. This characteristic has allowed FMs to excel and achieve notable results in various downstream validation tasks, demonstrating their ability to represent diverse biological entities effectively. Undoubtedly, FMs have ushered in a new era in computational biology, especially in the realm of deep learning. The primary goal of this survey is to conduct a systematic investigation and summary of FMs in bioinformatics, tracing their evolution, current research status, and the methodologies employed. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. We delve into the specifics of the problem at hand including sequence analysis, structure prediction, function annotation, and multimodal integration, comparing the structures and advancements against traditional methods. Furthermore, the review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases. Finally, we outline potential development paths and strategies for FMs in future biological research, setting the stage for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also as a roadmap for future explorations and applications of FMs in biology.Comment: 27 pages, 3 figures, 2 table

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.Comment: This paper is published in IJCV (refer to DOI). Please cite the published IJC

Ferrocenyl conjugated oxazepines/quinolines: multiyne coupling and ring–expanding or rearrangement

Ferrocenyl conjugated oxazepine/quinoline derivatives were presented through the reaction of hexadehydro-Diels–Alder (HDDA) generated arynes with ferrocenyl oxazolines under mild conditions via ring-expanding or rearrangement processes. Water molecule participated in this unexpected rearrangement process to produce quinoline skeletons, and DFT calculations supported a ring-expanding and intramolecular hydrogen migration process for the formation of oxazepine derivatives. Two variants of this chemistry, expanded the reactivity between ferrocenyl conjugated substances and arynes, further providing an innovative approach for the synthesis of ferrocene derivatives

The traceability of sudden water pollution in river canals based on the pollutant diffusion quantification formula

For the problem that the traceability parameters of sudden water pollution are difficult to determine, a fast traceability model based on a simplified mechanistic model coupled with an optimization algorithm is proposed to improve the accuracy of sudden water pollution traceability. In this paper, according to the diffusion law of pollutants, a quantitative formula of pollutant diffusion is proposed, and the differential calculation process of the pollutant convection equation is optimized. The Dynamic Programming and Beetle Antennae Search algorithm (DP-BAS) with dynamic step size is used in the reverse optimization process, which can avoid the problem of entering the local optimal solution in the calculation process. The DP-BAS is used to inverse solve the quantization equation to realize the decoupling of pollutant traceability parameters, transforming the multi-parameter coupled solution into a single-parameter solution, reducing the solution dimension, and optimizing the difficulty and solution complexity of pollutant traceability. The proposed traceability model is applied to the simulation case, the results show that the mean square errors of pollutant placement mass, location, and time are 2.39, 1.16, and 1.19 percent, respectively. To further verify the model reliability, the Differential Evolution and Markov Chain Monte Carlo simulation method (DE-MCMC) as well as Genetic Algorithms (GA) were introduced to compare with the proposed model to prove that the model has certain reliability and accuracy