Experimental investigation on thermal comfort model between local thermal sensation and overall thermal sensation

To study the human local and overall thermal sensations, a series of experiments under various conditions were carried out in a climate control chamber. The adopted analysis method considered the effect of the weight coefficient of local average skin temperature and density of the cold receptors’ distribution in different local body areas. The results demonstrated that the thermal sensation of head, chest, back and hands is warmer than overall thermal sensation. The mean thermal sensation votes of those local areas were more densely distributed. In addition, the thermal sensation of arms, tight and calf was colder than the overall thermal sensation, which pronounced that thermal sensation votes were more dispersed. The thermal sensation of chest and back had a strong linear correlation with overall thermal sensation. Considering the actual scope of air-conditioning regulation, the human body was classified into three local parts: a) head, b) upper part of body and c) lower part of body. The prediction model of both the three-part thermal sensation and overall thermal sensation was developed. Weight coefficients were 0.21, 0.60 and 0.19 respectively. The model provides scientist basis for guiding the sage installation place of the personal ventilation system to achieve efficient energy use

Coal Mine Safety Comprehensive Evaluation Based on Extension Theory

AbstractThis paper indicates that Extenics theory can be used to solve the problem of mine safety. The method includes 4 steps: building the evaluation indexes system and matter-element model, determining the classical field and controlled field of the matter-element model of the coal mine safety comprehensive evaluation, determining the connection function of each index on every safety level and determining the evaluation grade. This paper builds up a coal mine safety comprehensive evaluation indexes system and a matter-element model of coal mine based on extension theory, and then illustrates.the model using a case of Bei-zao Mine and its data

Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals

We consider the problem of sampling from a distribution governed by a potential function. This work proposes an explicit score-based MCMC method that is deterministic, resulting in a deterministic evolution for particles rather than a stochastic differential equation evolution. The score term is given in closed form by a regularized Wasserstein proximal, using a kernel convolution that is approximated by sampling. We demonstrate fast convergence on various problems and show improved dimensional dependence of mixing time bounds for the case of Gaussian distributions compared to the unadjusted Langevin algorithm (ULA) and the Metropolis-adjusted Langevin algorithm (MALA). We additionally derive closed form expressions for the distributions at each iterate for quadratic potential functions, characterizing the variance reduction. Empirical results demonstrate that the particles behave in an organized manner, lying on level set contours of the potential. Moreover, the posterior mean estimator of the proposed method is shown to be closer to the maximum a-posteriori estimator compared to ULA and MALA, in the context of Bayesian logistic regression

Benefits of developing mental hospital with the mode of "combined psychiatry and comprehensive medical"

精神病专科医院生存状况不容乐观。以“大专科、大综合”模式发展精神病专科医院能提高医院管理水平，改善财政状况，改变设备不全、人才缺乏等现状，提高危急重症病人救治能力及科研教学水平，促进医院发展。The contemporary performance of mental hospitals goes below our best expectation. Developing mental hospital with the mode of “combined psychiatry and comprehensive medical” would increase the ability of management, improve financial state, change the present circumstance of incomplete equipment and personnel lack, enhance endangered patients’ treatment as well as scientific research and teaching and, finally, promote the development of hospital

TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling

We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of ten million sequence-host bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for point mutations under consistent experimental conditions, we effectively compared TemPL with state-of-the-art models. Notably, TemPL demonstrated superior performance in predicting protein stability. An ablation study was conducted to elucidate the influence of OGT prediction and language modeling modules on TemPL's performance, revealing the importance of integrating both components. Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activit