Effect of resistive load on the performance of an organic Rankine cycle with a scroll expander

An experimental investigation is performed for an organic Rankine cycle system with different electrical resistive loads. The test rig is set up with a small scroll expander-generator unit, a boiler and a magnetically coupled pump. R134a is used as the working fluid in the system. The experimental results reveal that the resistive load coupled to the scroll expander-generator unit affects the expander performance and power output characteristics. It is found that an optimum pressure ratio exists for the maximum power output. The optimal pressure ratio of the expander decreases markedly as the resistive load gets higher. The optimum pressure ratio of the scroll expander is 3.6 at a rotation speed of 3450 r/min for a resistive load of 18.6 Ω. The maximum electrical power output is 564.5 W and corresponding isentropic and volumetric efficiencies are 78% and 83% respectively

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure

FreeU: Free Lunch in Diffusion U-Net

In this paper, we uncover the untapped potential of diffusion U-Net, which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising, whereas its skip connections mainly introduce high-frequency features into the decoder module, causing the network to overlook the backbone semantics. Capitalizing on this discovery, we propose a simple yet effective method-termed "FreeU" - that enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps, to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models, e.g., Stable Diffusion, DreamBooth, ModelScope, Rerender and ReVersion, to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference. Project page: https://chenyangsi.top/FreeU/.Comment: Project page: https://chenyangsi.top/FreeU

Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared with natural limb control. We also expect to understand human intent with low training data requirements. In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy. These modules provide a user-friendly approach that enables the robot to deliver the tools as per user need, especially when the user is working with both hands. Therefore, users can focus on their task execution without additional training in the use of human-machine interfaces, while the robot interprets their intuitive gestures. The proposed multimodal interaction framework is executed in the UR5e robot platform equipped with a RealSense D435i camera, and the effectiveness is assessed through a soldering circuit board task. The experiment results have demonstrated superior performance in hand gesture recognition, where the static hand gesture recognition module achieves an accuracy of 94.3\%, while the dynamic motion recognition module reaches 97.6\% accuracy. Compared with human solo manipulation, the proposed approach facilitates higher efficiency tool delivery, without significantly distracting from human intents.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A specialized prey-capture apparatus in mid-Cretaceous rove beetles

Cai et al. report specialized prey-capture structures in two species of the stenine rove beetles from mid-Cretaceous Burmese amber. The discovery provides critical information about the origin and early evolution of both the novel predatory structure and of the subfamily Steninae (Coleoptera: Staphylinidae)