Study of travel behavior during the covid-19 pandemic

This study examines the impact of the COVID-19 pandemic on residents' commuting patterns in the United States. Using the MNL (Multinomial Logit) and binary logit models, we analyze the factors influencing the choice of commuting modes before and during the pandemic. Our findings indicate that various personal, travel-related, and COVID-19-related factors significantly affect commuting choices. For commuting methods other than driving, factors such as gender, age, possession of a driver's license, bicycle ownership, car ownership, family size, working days per week, COVID-19 testing, and mask restrictions play a significant role. The decision to walk to work is influenced by gender, vehicle ownership, travel time, travel distance, working days per week, family income, COVID-19-related relocation, and level of COVID-19 anxiety. Public transportation choices are influenced by factors such as age, race, possession of a driver's license, car ownership, travel time, travel distance, COVID-19-related migration, and COVID-19 testing of cohabitants. Furthermore, the binary logit model reveals that personal factors (e.g., gender, driver's license) and COVID-19-related factors (e.g., mask restrictions, level of concern about the coronavirus) significantly impact the consistency of travel modes before and during the pandemic. This study contributes to our understanding of the changes in commuting patterns during the COVID-19 pandemic and provides insights into the factors that shape residents' commuting choices. The findings can inform transportation planning and policy-making to promote sustainable and resilient transportation systems in the face of future disruptions

GPT4Table: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. While it is true that tables can be used as inputs to LLMs with serialization, there is a lack of comprehensive studies examining whether LLMs can truly comprehend such data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark we create includes seven tasks, each with its own unique challenges, \eg, cell lookup, row retrieval, and size detection. We conduct a series of evaluations on GPT-3.5 and GPT-4. We find that the performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we propose \textit{self-augmentation} for effective structural prompting, such as critical value / range identification using LLMs' internal knowledge. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, \eg, TabFact($\uparrow2.31\%$), HybridQA($\uparrow2.13\%$), SQA($\uparrow2.72\%$), Feverous($\uparrow0.84\%$), and ToTTo($\uparrow5.68\%$). We believe that our benchmark and proposed prompting methods can serve as a simple yet generic selection for future research.Comment: This paper has been accepted as a full paper at WSDM 202

The increasing water stress projected for China could shift the agriculture and manufacturing industry geographically

The sustainable development of China has been challenged by the misalignment of water demand and supply across regions under varying climate change scenarios. Here we develop a water stress prediction index using a fuzzy decision-making approach, which analyzes spatiotemporal variations of water stress and concomitant effects on the populace within China. Our results indicate that water stress will increase from 2020 to 2099 under both low and high emission scenarios, primarily due to decreased water supplies like surface runoff and snow water content. Seasonal analysis reveals that annual fluctuations in water stress are mainly driven by changes in spring and autumn. Water stress is projected to be considerably lower in southeastern provinces compared to northwestern ones, where, on average, over 20% of the Chinese population could be severely impacted. These changes in water stress could lead to the north-to-south migration of the agriculture sector, manufacturing sector, and human population

Whole exome sequencing and system biology analysis support the "two-hit" mechanism in the onset of Ameloblastoma

Ameloblastoma is the most frequent odontogenic tumor. Various evidence has highlighted the role of somatic mutations, including recurrent mutation BRAF V600E, in the tumorigenesis of Ameloblastoma, but the intact genetic pathology remains unknown. We sequenced the whole exome of both tumor tissue and healthy bone tissue from four mandibular ameloblastoma patients. The identified somatic mutations were integrated into Weighted Gene Co-expression Network Analysis on publicly available expression data of odontoblast, ameloblast, and Ameloblastoma. We identified a total of 70 rare and severe somatic mutations. We found BRAF V600E on all four patients, supporting previous discovery. HSAP4 was also hit by two missense mutations on two different patients. By applying Weighted Gene Co-expression Network Analysis on expression data of odontoblast, ameloblast, and Ameloblastoma, we found a proliferation-associated gene module that was significantly disrupted in tumor tissues. Each patient carried at least two rare, severe somatic mutations affecting genes within this module, including HSPA4, GNAS, CLTC, NES, and KMT2D. All these mutations had a ratio of variant-support reads lower than BRAF V600E, indicating that they occurred later than BRAF V600E. We suggest that a severe somatic mutation on the gene network of cell proliferation other than BRAF V600E, namely second hit, may contribute to the tumorigenesis of Ameloblastoma

Soil characteristics and microbial responses in post-mine reclamation areas in a typical resource-based city, China

Mining activities worldwide have resulted in soil nutrient loss, which pose risks to crop and environmental health. We investigated the effects of post-mine reclamation activities on soil physicochemical properties and microbial communities based on 16S rRNA sequencing and the further statistical analysis in the coal base in Peixian city, China. The results revealed significant differences in soil microbial relative abundance between reclamation and reference soils. Proteobacteria was the most abundant phyla in all seven mine sites regardless of reclamation age while considerable differences were found in microbial community structure at other levels among different sites. Notebly, Gammaproteobacteria, member of the phylum Proteobacteria, had relatively high abundance in most sites. Furthermore, Kendall’s tau-b correlation heatmap revealed that potentially toxic elements and other physicochemical properties play vital roles in microbial community composition

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

The distribution variation of pathogens and virulence factors in different geographical populations of giant pandas

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas

Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms

Article originally published International Journal of Machine Learning and ComputingSmartphones are widely used today, and it becomes possible to detect the user's environmental changes by using the smartphone sensors, as demonstrated in this paper where we propose a method to identify human activities with reasonably high accuracy by using smartphone sensor data. First, the raw smartphone sensor data are collected from two categories of human activity: motion-based, e.g., walking and running; and phone movement-based, e.g., left-right, up-down, clockwise and counterclockwise movement. Firstly, two types of features extraction are designed from the raw sensor data, and activity recognition is analyzed using machine learning classification models based on these features. Secondly, the activity recognition performance is analyzed through the Convolutional Neural Network (CNN) model using only the raw data. Our experiments show substantial improvement in the result with the addition of features and the use of CNN model based on smartphone sensor data with judicious learning techniques and good feature designs