Non-invasive detection algorithm of thermal comfort based on computer vision

The waste of building energy consumption is a major challenge in the world. Real-time detection of human thermal comfort is an effective way to deal with this issue. However, due to the difference of personal thermal comfort and changes caused by climatic variations, there is still a long way to reach this target. From another perspective, the current HVAC (heating, ventilating and air-conditioning) systems are reluctant to provide flexible interaction channels to adjust atmosphere which fails to follow continuously increasing requirements from users. All of them indicate the necessity to develop more intelligent detection method for human thermal comfort. In this paper, a non-invasion detection method toward thermal comfort is proposed from two perspectives: macro human postures and skin textures. In posture part, OpenPose is used for detecting the key points’ position coordinates of human body in images, which would be functionalized from the term of thermal comfort. In skin textures, deep neural network is used to regress the images of skin to its temperature. Based on Fanger’s theory of thermal comfort, the results of both parts are satisfying: subjects’ postures can be captured and interpreted into different thermal comfort level: hot, cold and comfort. And the absolute error of prediction from neurons network is less than 0.125 degrees centigrade which is the equipment error of thermometer used in data acquisition. With solutions of this paper, it is promising to non-invasively detect the thermal comfort level of users from postures and skin textures. And the conclusion and future work are discussed in final chapter

Numerical and Experimental Research of Noise Reduction due to Low Frequency Pressure Fluctuation of Rotary Compressor

In order to reduce the noise level due to the low frequency pressure fluctuation associated with a rotary compressor, the noise mechanism and noise reduction solutions were conducted by using numerical and experimental methods. A 1D simulation model was established and a sensitivity analysis was conducted for the parameters associated with the low frequency pressure fluctuation of the rotary compressor. Then, a 3D CFD simulation model corresponding to the operation procedure of the rotary compressor was established and the working process of the rotary compressor was simulated. At the same time, the low frequency pressure fluctuation and the noise spectral characteristic were measured by using a refrigerant test fixture established in this work. Based on numerical and experimental research results, several noise reduction solutions and basic methods to restrain the low frequency pressure fluctuation were proposed and verified by using experimental method. A good improvement for the noise performance due to the low frequency pressure fluctuation was obtained. The work in this paper provides a reference and a foundation for the improvement of the noise due to the low frequency pressure fluctuation associated with rotary compressors

Unbiased Watermark for Large Language Models

The recent advancements in large language models (LLMs) have sparked a growing apprehension regarding the potential misuse. One approach to mitigating this risk is to incorporate watermarking techniques into LLMs, allowing for the tracking and attribution of model outputs. This study examines a crucial aspect of watermarking: how significantly watermarks impact the quality of model-generated outputs. Previous studies have suggested a trade-off between watermark strength and output quality. However, our research demonstrates that it is possible to integrate watermarks without affecting the output probability distribution with appropriate implementation. We refer to this type of watermark as an unbiased watermark. This has significant implications for the use of LLMs, as it becomes impossible for users to discern whether a service provider has incorporated watermarks or not. Furthermore, the presence of watermarks does not compromise the performance of the model in downstream tasks, ensuring that the overall utility of the language model is preserved. Our findings contribute to the ongoing discussion around responsible AI development, suggesting that unbiased watermarks can serve as an effective means of tracking and attributing model outputs without sacrificing output quality

AlpaCare:Instruction-tuned Large Language Models for Medical Application

Large Language Models (LLMs) have demonstrated significant enhancements in instruction-following abilities through instruction tuning, achieving notable performances across various tasks. Previous research has focused on fine-tuning medical domain-specific LLMs using an extensive array of medical-specific data, incorporating millions of pieces of biomedical literature to augment their medical capabilities. However, existing medical instruction-tuned LLMs have been constrained by the limited scope of tasks and instructions available, restricting the efficacy of instruction tuning and adversely affecting performance in the general domain. In this paper, we fine-tune LLaMA-series models using 52k diverse, machine-generated, medical instruction-following data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive experimental results on both general and medical-specific domain free-form instruction evaluations showcase AlpaCare's strong medical proficiency and generalizability compared to previous instruction-tuned models in both medical and general domains. We provide public access to our MedInstruct-52k dataset and a clinician-crafted free-form instruction test set, MedInstruct-test, along with our codebase, to foster further research and development. Our project page is available at https://github.com/XZhang97666/AlpaCare

Identification of the enterprise financialization motivation on crowding out R&D innovation: evidence from listed companies in China

Enterprise financial asset allocation depends on its motivation, which significantly influences its R&D innovation. In this study, we theoretically analyzed the motivation behind the crowding-out effect of enterprise financialization on R&D innovation and constructed a panel data model to identify enterprise financialization behavior. Furthermore, we analyzed the characteristics of enterprises with two types of effects on R&D innovation: Crowding-out and non-crowding-out. Using disclosed data from listed companies in China as the sample, the following conclusions were drawn. First, there are two types of motivation for enterprise financial assets: reservoir motivation and substitute motivation. This difference in motivation leads to whether there is a crowding-out effect of enterprise financialization on R&D innovation. Second, based on whether there is a crowding-out effect on enterprise R&D innovation, we found that the difference in reservoir motivation between the two types of samples is reflected in the intensity of inhibition, while the difference in substitute motivation is reflected in significance. Third, there are differences in the mechanism variables of financialization motivation among different samples. The moderating effect of equity concentration is reflected in its intensity, while the moderating effect of financing constraints is reflected in its significance

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from vast open-source datasets, effectively minimizing manual curation and potential cost for instruction tuning an LLM. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal tool to identify discrepancies between a model's expected responses and its autonomous generation prowess. Through the adept application of IFD, cherry samples are pinpointed, leading to a marked uptick in model training efficiency. Empirical validations on renowned datasets like Alpaca and WizardLM underpin our findings; with a mere 10% of conventional data input, our strategy showcases improved results. This synthesis of self-guided cherry-picking and the IFD metric signifies a transformative leap in the optimization of LLMs, promising both efficiency and resource-conscious advancements. Codes, data, and models are available: https://github.com/MingLiiii/Cherry_LL

The characteristics of soil salinization effects on nitrogen mineralization and nitrification in upland fields

The influence of soil salinization on nitrogen (N) transformation is largely unknown, which impedes the reasonable management of N in saline fields. A comprehensive meta-analysis was thus conducted to evaluate the effects of salinity and relative soil physicochemical properties on net N mineralization and nitrification in upland soils. Results showed that effects of salinity on the net-N mineralization rate (Min) and nitrification rate (Nit) changed with the salinity level and incubation time. Generally, the inhibitory effect of salt on Min and Nit decreased gradually with incubation time. At 14–16 days of soil incubation, significant stimulatory effects on Min were observed in middle-level (ECe: 12–16 dS m-1) and high-level (ECe >16 dS m-1) saline soils, and on Nit in low-level (ECe: 4–12 dS m-1) saline soils. Regression analysis revealed that the effects of soil organic carbon (SOC), total N (TN), C/N, pH, and clay content on Min and Nit were closely related to salinity levels. Nit at 5–7 days of soil incubation first enhanced and then decreased with C/N increase, and the threshold value was 34.7. The effect of pH on Nit changed with salinity levels, and shifted from stimulation to inhibition with increasing pH. Min at 5–7 days of soil incubation in middle-level group first increased with increasing pH, and decreased when pH was higher than 8.1. Salinization deeply affected soil properties, which further influenced N turnover via alteration of the availability of substrates and microbial biomass and activities. Our findings suggest that the influence of salinity on soil N turnover closely related with salinity level, and salinity level should be considered fully when optimizing N management in saline upland fields

Staphylococcus aureus Bacteriophage Suppresses LPS-Induced Inflammation in MAC-T Bovine Mammary Epithelial Cells

Several previous studies have shown that bacteriophages can significantly affect the production of various cytokines. The aim of this present study was to investigate the inflammatory effects and mechanisms of bacteriophage vB_SauM_JS25 in stimulated MAC-T bovine mammary epithelial cells by real-time polymerase chain reaction (PCR) and Western blotting. Experiments show that vB_SauM_JS25 reduces Staphylococcus aureus- or lipopolysaccharide (LPS)-induced levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-8, IL-10, and regulated on activation, normal T cell expressed and secreted (RANTES) mRNA in MAC-T cells, in a manner expected to be unrelated to its antibacterial action. Moreover, S. aureus bacteriophage vB_SauM_JS25 suppressed the LPS-induced phosphorylation of nuclear factor (NF)-κB p65, which may represent an important mechanism mediating these effects. A carefully regulated balance between activation and inhibition by bacteriophages must be kept avoiding inappropriate inflammatory responses. The ability of vB_SauM_JS25 to influence the immune response highlights the potential development and application of bacteriophage-based therapies and may represent a novel anti-inflammatory therapeutic strategy