Application of Principle of Work and Energy in Its Differential Form to MDOF Systems

This paper is intended to reveal some new aspects of the principle of work and energy in its differential form. A theorem is given to state the principle of work and energy in differential form that can be applied alone to build up all the dynamical equations of a special class of MDOF mechanical systems. Some illustrations and discussions are presented to demonstrate the refined applicability of the principle of work and energy in its differential form through the comparison  with the Lagrange equation method

An innovative parameter optimization of Spark Streaming based on D3QN with Gaussian process regression

Nowadays, Spark Streaming, a computing framework based on Spark, is widely used to process streaming data such as social media data, IoT sensor data or web logs. Due to the extensive utilization of streaming media data analysis, performance optimization for Spark Streaming has gradually developed into a popular research topic. Several methods for enhancing Spark Streaming's performance include task scheduling, resource allocation and data skew optimization, which primarily focus on how to manually tune the parameter configuration. However, it is indeed very challenging and inefficient to adjust more than 200 parameters by means of continuous debugging. In this paper, we propose an improved dueling double deep Q-network (DQN) technique for parameter tuning, which can significantly improve the performance of Spark Streaming. This approach fuses reinforcement learning and Gaussian process regression to cut down on the number of iterations and speed convergence dramatically. The experimental results demonstrate that the performance of the dueling double DQN method with Gaussian process regression can be enhanced by up to 30.24%

Optimal Ordering and Disposing Policies in the Presence of an Overconfident Retailer: A Stackelberg Game

This paper investigates the impact of the retailer’s overconfident behavior on supply chain performance. We start with a basic model on the rational newsvendor model and investigate the retailer’s optimal ordering decision and expected profit. Next, we extend the basic model and introduce an overconfident retailer. We find that the retailer’s overconfident behavior does not necessarily damage the supply chain compared with the basic model when the overconfident level does not exceed a threshold. We also design the cooperation and buyback mechanism and conduct numerical analysis to compare the manufacturer’s and retailer’s expected profits and real profits with those in the basic newsvendor model. It can achieve Pareto improvement in the supply chain when the overconfident level is low. When the retailer’s overconfident level exceeds a threshold, the retailer’s ordering decision cannot make the whole supply chain sustainable development

TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots

The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body

Tip-induced bond weakening, tilting, and hopping of a single CO molecule on Cu(100)

The interaction between a probing tip and an adsorbed molecule can significantly impact the molecular chemical structure and even induce its motion on the surface. In this study, the tip-induced bond weakening, tilting, and hopping processes of a single molecule were investigated by sub-nanometre resolved tip-enhanced Raman spectroscopy (TERS). We used single carbon monoxide (CO) molecules adsorbed on the Cu (100) surface as a model system for the investigation. The vibrational frequency of the C−O stretching mode is always redshifted as the tip approaches, revealing the weakening of the C−O bond owing to tip−molecule interactions. Further analyses of both the vibrational Stark effect and TERS imaging patterns suggest a delicate tilting phenomenon of the adsorbed CO molecule on Cu(100), which eventually leads to lateral hopping of the molecule. While a tilting orientation is found toward the hollow site along the [110] direction of the Cu(100) surface, the hopping event is more likely to proceed via the bridge site to the nearest Cu neighbour along the [100] or [010] direction. Our results provide deep insights into the microscopic mechanisms of tip−molecule interactions and tip-induced molecular motions on surfaces at the single-bond level