Experimental Study on Variation Strategies for Complex Social Pedestrian Groups in Conflict Conditions

The paper concentrates on an experimental study of the variation strategies of complex social pedestrian groups in conflict conditions. We tracked the trajectories of group members and analysed the configuration of both the complex group and its subgroups when the groups walked through a narrowing passage, passed by an obstacle or faced counter flows. We summarized the variation strategies of complex groups when they faced these conflict conditions. The effect of groups on the crowd was also studied. It was found that groups could have significant effect on self-organization of the crowd. The results in the paper could be applied in modelling pedestrian group decision and behaviour and analysing crowd dynamics

HELLaMA: LLaMA-based Table to Text Generation by Highlighting the Important Evidence

Large models have demonstrated significant progress across various domains, particularly in tasks related to text generation. In the domain of Table to Text, many Large Language Model (LLM)-based methods currently resort to modifying prompts to invoke public APIs, incurring potential costs and information leaks. With the advent of open-source large models, fine-tuning LLMs has become feasible. In this study, we conducted parameter-efficient fine-tuning on the LLaMA2 model. Distinguishing itself from previous fine-tuning-based table-to-text methods, our approach involves injecting reasoning information into the input by emphasizing table-specific row data. Our model consists of two modules: 1) a table reasoner that identifies relevant row evidence, and 2) a table summarizer that generates sentences based on the highlighted table. To facilitate this, we propose a search strategy to construct reasoning labels for training the table reasoner. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results. Additionally, we observed that highlighting input tables significantly enhances the model's performance and provides valuable interpretability

Characterization of geolocation accuracy of Suomi NPP Advanced Technology Microwave Sounder measurements

The Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership satellite has 22 channels at frequencies ranging from 23 to 183 GHz for probing the atmospheric temperature and moisture under all weather conditions. As part of the ATMS calibration and validation activities, the geolocation accuracy of ATMS data must be well characterized and documented. In this study, the coastline crossing method (CCM) and the land-sea fraction method (LFM) are utilized to characterize and quantify the ATMS geolocation accuracy. The CCM is based on the inflection points of the ATMS window channel measurements across the coastlines, whereas the LFM collocates the ATMS window channel data with high-resolution land-sea mask data sets. Since the ATMS measurements provide five pairs of latitude and longitude data for K, Ka, V, W, and G bands, respectively, the window channels 1, 2, 3, 16, and 17 from each of these five bands are chosen for assessing the overall geolocation accuracy. ATMS geolocation errors estimated from both methods are generally consistent from 40 cases in June 2014. The ATMS along-Track (cross-Track) errors at nadir are within ±4.2 km (±1.2 km) for K/Ka, ±2.6 km (±2.7 km) for V bands, and ±1.2 km (±0.6 km) at W and G bands, respectively. At the W band, the geolocation errors derived from both algorithms are probably less reliable due to a reduced contrast of brightness temperatures in coastal areas. These estimated ATMS along-Track and cross-Track geolocation errors are well within the uncertainty requirements for all bands. © 2016. American Geophysical Union. All Rights Reserved

Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing

The hippocampus has been known as one of the most important structures referred to as Alzheimer’s disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists

Dwell Time Modelling and Optimized Simulations for Crowded Rail Transit Lines Based on Train Capacity

Understanding the nature of rail transit dwell time has potential benefits for both the users and the operators. Crowded passenger trains cause longer dwell times and may prevent some passengers from boarding the first available train that arrives. Actual dwell time and the process of passenger alighting and boarding are interdependent through the sequence of train stops and propagated delays. A comprehensive and feasible dwell time simulation model was developed and optimized to address the problems associated with scheduled timetables. The paper introduces the factors that affect dwell time in urban rail transit systems, including train headway, the process and number of passengers alighting and boarding the train, and the inability of train doors to properly close the first time because of overcrowded vehicles. Finally, based on a time-driven micro-simulation system, Shanghai rail transit Line 8 is used as an example to quantify the feasibility of scheduled dwell times for different stations, directions of travel and time periods, and a proposed dwell time during peak hours in several crowded stations is presented according to the simulation results

Boosting the thermoelectric performance of p-type heavily Cu-doped polycrystalline SnSe via inducing intensive crystal imperfections and defect phonon scattering

In this study, we, for the first time, report a high Cu solubility of 11.8% in single crystal SnSe microbelts synthesized via a facile solvothermal route. The pellets sintered from these heavily Cu-doped microbelts show a high power factor of 5.57 μW cm−1 K−2 and low thermal conductivity of 0.32 W m−1 K−1 at 823 K, contributing to a high peak ZT of ∼1.41. Through a combination of detailed structural and chemical characterizations, we found that with increasing the Cu doping level, the morphology of the synthesized Sn1−xCuxSe (x is from 0 to 0.118) transfers from rectangular microplate to microbelt. The high electrical transport performance comes from the obtained Cu+ doped state, and the intensive crystal imperfections such as dislocations, lattice distortions, and strains, play key roles in keeping low thermal conductivity. This study fills in the gaps of the existing knowledge concerning the doping mechanisms of Cu in SnSe systems, and provides a new strategy to achieve high thermoelectric performance in SnSe-based thermoelectric materials

A rapid and nondestructive method to determine the distribution map of protein, carbohydrate and sialic acid on Edible bird’s nest by hyper-spectral imaging and chemometrics

Edible bird’s nest (EBN) is a precious functional food in Southeast Asia. A rapid and nondestructive method for determining the distribution map of protein content (PC), carbohydrate content (CC) and sialic acid content (SAC) on EBN sample was proposed. Firstly, 60 EBNs were used for hyperspectral image acquisition, and components content (PC, CC and SAC) were determined by chemical analytical methods. Secondly, the spectral signals of EBN hyperspectral image and EBN components content were used to build calibration models. Thirdly, spectra of each pixel in EBN hyperspectral image were extracted, and these spectra were substituted in the calibration models to predict the PC, CC and SAC of each pixel in the EBN image, so the visual distribution maps of PC, CC and SAC on the whole EBN were obtained. It is the first time to show the distribution tendency of PC, CC and SAC on the whole EBN sample