The strong chromatic index of 1-planar graphs

The chromatic index $\chi'(G)$ of a graph $G$ is the smallest $k$ for which $G$ admits an edge $k$-coloring such that any two adjacent edges have distinct colors. The strong chromatic index $\chi'_s(G)$ of $G$ is the smallest $k$ such that $G$ has a proper edge $k$-coloring with the condition that any two edges at distance at most 2 receive distinct colors. A graph is 1-planar if it can be drawn in the plane so that each edge is crossed by at most one other edge. In this paper, we show that every graph $G$ with maximum average degree $\bar{d}(G)$ has $\chi'_{s}(G)\le (2\bar{d}(G)-1)\chi'(G)$. As a corollary, we prove that every 1-planar graph $G$ with maximum degree $\Delta$ has $\chi'_{\rm s}(G)\le 14\Delta$, which improves a result, due to Bensmail et al., which says that $\chi'_{\rm s}(G)\le 24\Delta$ if $\Delta\ge 56$

Magnetic Resonance Characterization of Porous Media Using Diffusion through Internal Magnetic Fields

When a porous material is inserted into a uniform magnetic field, spatially varying fields typically arise inside the pore space due to susceptibility contrast between the solid matrix and the surrounding fluid. As a result, direct measurement of the field variation may provide a unique opportunity to characterize the pore geometry. The sensitivity of nuclear magnetic resonance (NMR) to inhomogeneous field variations through their dephasing effects on diffusing spins is unique and powerful. Recent theoretical and experimental research sheds new light on how to utilize susceptibility-induced internal field gradients to quantitatively probe the microstructure of porous materials. This article reviews ongoing developments based on the stimulated echo-pulse sequence to extend the characterization of porous media using both spatially resolved and unresolved susceptibility-induced internal gradients that operate on a diffusing-spin ensemble.open

Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China

Nowadays, relevant data collected from hospital buildings remain insufficient because hospital buildings often have stricter environmental requirements resulting in more limited data access than other building types. Additionally, existing window-opening behavior models were mostly developed and validated using data measured from the experimental building itself. Hence, their accuracy is only assessed by the algorithm’s evaluation index, which limits the model’s applicability, given that it is not tested by the actual cases nor cross-verified with other buildings. Based on the aforementioned issues, this study analyzes the window-opening behavior of doctors and patients in spring in a maternity hospital in Beijing and develops behavioral models using logistic regression. The results show that the room often has opened windows in spring when the outdoor temperature exceeds 20 °C. Moreover, the ward windows’ use frequency is more than 10 times higher than those of doctors’ office. The window-opening behavior in wards is more susceptible to the influence of outdoor temperature, while in the doctors’ office, more attention is paid to indoor air quality. Finally, by embedding the logistic regression model of each room into the EnergyPlus software to simulate the CO2 concentration of the room, it was found that the model has better applicability than the fixed schedule model. However, by performing cross-validation with different building types, it was found that, due to the particularity of doctors’ offices, the models developed for other building types cannot accurately reproduce the window-opening behavior of doctors. Therefore, more data are still needed to better understand window usage in hospital buildings and support the future building performance simulations of hospital buildings

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

A thermal comfort field study on subway passengers during air-conditioning season in Beijing

With the massive increase of subway passengers in large Chinese cities, thermal comfort in the subway has become an important topic. The thermal comfort of subway passengers is different from that in above-ground buildings and has its own particularities, as the subway comprises of underground semi-closed buildings. Moreover, the thermal environment experienced by passengers is dynamic. Considering these reasons, this study adopts a dynamic thermal comfort study method for investigating thermal sensations in the subway. The field study was conducted in two Beijing subway stations in May 2016 and June–July 2017, and a total of 628 samples were obtained. Variations in thermal sensation and thermal comfort were analyzed. The results showed that the thermal sensations of subway passengers change dramatically through the entire process, and alternate between hot and cold. For the station hall and platform, although the temperature and the relative humidity were beyond the standard range of the Code for Design of Metro in China (GB50157- 2013), passengers find them acceptable. The study also provides reliable basic data for the design and operation management of ventilation and air-conditioning systems in the subway station, so as to create a comfortable thermal environment for subway passengers

Field experiment provides ground truth for surface nuclear magnetic resonance measurement

The need for sustainable management of fresh water resources is one of the great challenges of the 21st century. Since most of the planet’s liquid fresh water exists as groundwater, it is essential to develop non-invasive geophysical techniques to characterize groundwater aquifers. A field experiment was conducted in the High Plains Aquifer, central United States, to explore the mechanisms governing the non-invasive Surface NMR (SNMR) technology. We acquired both SNMR data and logging NMR data at a field site, along with lithology information from drill cuttings. This allowed us to directly compare the NMR relaxation parameter measured during logging, T2, to the relaxation parameter T2* measured using the SNMR method. The latter can be affected by inhomogeneity in the magnetic field, thus obscuring the link between the NMR relaxation parameter and the hydraulic conductivity of the geologic material. When the logging T2 data were transformed to pseudo- T2* data, by accounting for inhomogeneity in the magnetic field and instrument dead time, we found good agreement with T2* obtained from the SNMR measurement. These results, combined with the additional information about lithology at the site, allowed us to delineate the physical mechanisms governing the SNMR measurement. Such understanding is a critical step in developing SNMR as a reliable geophysical method for the assessment of groundwater resources