Exploiting Data and Human Knowledge for Predicting Wildlife Poaching

Poaching continues to be a significant threat to the conservation of wildlife and the associated ecosystem. Estimating and predicting where the poachers have committed or would commit crimes is essential to more effective allocation of patrolling resources. The real-world data in this domain is often sparse, noisy and incomplete, consisting of a small number of positive data (poaching signs), a large number of negative data with label uncertainty, and an even larger number of unlabeled data. Fortunately, domain experts such as rangers can provide complementary information about poaching activity patterns. However, this kind of human knowledge has rarely been used in previous approaches. In this paper, we contribute new solutions to the predictive analysis of poaching patterns by exploiting both very limited data and human knowledge. We propose an approach to elicit quantitative information from domain experts through a questionnaire built upon a clustering-based division of the conservation area. In addition, we propose algorithms that exploit qualitative and quantitative information provided by the domain experts to augment the dataset and improve learning. In collaboration with World Wild Fund for Nature, we show that incorporating human knowledge leads to better predictions in a conservation area in Northeastern China where the charismatic species is Siberian Tiger. The results show the importance of exploiting human knowledge when learning from limited data.Comment: COMPASS 201

Visualization analysis of research literature on early warning of geo-hazards based on meteorological factors in the past 20 years

Early warning of geo-hazards based on meteorological factors in China began in 2003, and early warning informations are issued to the public every year during the flood season to alert the public to guard against geo-hazards such as landslides, debris flows, and mudslides caused by rainfall. To better understand the current situation and future trends of research for early warning of geo-hazards based on meteorological factors, the visualization tool CiteSpace was utilized to review the domestic and foreign literature on geo-hazards meteorological risk warning published from 2003 to 2023 and construct the scientific knowledge map. The CNKI database and SCI-Expanded databases were used as data sources. The result show that: (1) The study of critical rainfall thresholds based on the relationship between historical rainfall and disaster is in the leading position in research on early warning of geo-hazards based on meteorological factors, and the study of rainfall-related thresholds will continue to be a research hotspot in the future. (2) Keyword cluster analysis shows that a large number of studies have conducted research on early warning and prediction models and critical thresholds of geo-hazards under different formation conditions, meteorological conditions, different types, and genetic models, indicating that refinement is an important direction for the development of early warning of geo-hazards based on meteorological factors. (3) In the international research, Chinese scholars account for 39% of the total number of publications, showing an absolute advantage in research on geo-hazards meteorological risk early warning. The research results objectively demonstrate the development context, domestic and foreign research hotspots, and trends of research on geo-hazards meteorological risk warning in China, aiming to contribute to the progress of geo-hazards research and early warning forcast business in China

20-year early warning for regional geo-hazards risk in China: 2003-2022

Early warning of geo-hazards based on meteorological factors has played an important supportive role in disaster prevention and mitigation in China since its inception in 2003. This paper summarizes the the 20-year development process, technical methods, and disaster reduction effects of the early warning works. (1) The development process of early warning work process is divided into three stages: initiation and promotion (2003-2009), deepening cooperation (2010-2017), and reform and enhancement (2018-2022). (2) With 24-hour early warning work as the main content, a progressive warning model and a relatively complete warning service system have been gradually formed. (3) Three sets of early warning model technology and method systems have been gradually developed, including critical precipitation threshold model, the threshold model based on geo-hazards risk, and the dynamic early warning models, with the publication of industry standards for warning. (4) The spatial and temporal accuracy of warning products continues to improve, with the national and 26 provincial warning spatial accuracies exceeding 5 km × 5 km. The focus is on 24-hour warnings, with development towards 72-hour and medium- to long-term forecasts. Over 8 provincial-level and some municipal and county-level authorities have implemented 3-hour short-term warnings, gradually forming a work system to support service short impending warning response, medium-term prevention and long term deployment. (5) Where there is warning, there is response. The Ministry of Natural Resources has taken the national early warning as one of the bases for initiating defense responses, and 18 provinces have clarified the working mechanisms of the early warning response linkage. (6) The awareness of multi-party disaster prevention has been continuously enhanced. With strengthened inspections,evacuations, and successful risk aversion after receiving early warning information, the effectiveness of disaster prevention and mitigation is evident. The experience of early warning works in the past 20-year can provide reference for the next step in promoting the early warning of geo-hazards based on meteorological factors, supporting the enhancement of China’s capability and level of geo-hazards prevention and control work

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future

Machine Learning Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery

Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop management. This paper aims at initially exploiting the potentials of UAV aerial RGB image in crop water stress assessment by developing a simple but effective supervised learning system. Various techniques are seamlessly integrated into the system including vegetation segmentation, feature engineering, Bayesian optimization and Support Vector Machine (SVM) classifier. In particular, wheat pixels are first segmented from soil background by using the classical vegetation index thresholding. Rather than performing pixel-wise classification, pixel squares of appropriate dimension are defined as samples, from which various features for pure vegetation pixels are extracted including spectral and color index (CI) features. SVM with Bayesian optimization is adopted as the classifier. To validate the developed system, a Unmanned Aerial Vehicle (UAV) survey is performed to collect high-resolution atop canopy RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. Two levels of soil moisture were designed after seedling establishment for wheat plots by using intelligent irrigation and rain shelter, where field measurements were to obtain ground soil water ratio for each wheat plot. Comparative experiments by three-fold cross-validation demonstrate that pixel-wise classification, with a high computation load, can only achieve an accuracy of 82.8% with poor F1 score of 71.7%; however, the developed system can achieve an accuracy of 89.9% with F1 score of 87.7% by using only spectral intensities, and the accuracy can be further improved to 92.8% with F1 score of 91.5% by fusing both spectral intensities and CI features. Future work is focused on incorporating more spectral information and advanced feature extraction algorithms to further improve the performance

Clinical application of Kirschner wires combined with 5-Ethibond fixation for patella fractures

BackgroundPatella fractures that require surgery are conventionally treated using Kirschner wires (K-wires) and stainless steel wires. In recent years, the nonabsorbable polyester has been reported to have excellent outcomes clinically. Therefore, the goal of our study was to evaluate the effects of Kirschner wires combined with 5-Ethibond on treating patellar fractures.MethodsFrom July 2018 to January 2022, 22 patella fracture patients were treated with Kirschner wires combined with 5-Ethibond. Radiographs of the knees were used to evaluate fracture healing and hardware complications. The clinical results were evaluated through the functional score, knee joint range of motion (ROM), and Bostman patella fracture functional score.ResultsThe average age of patients was 57.4 ± 11.9 (range 33–74) years. The mean follow-up time was 15.2 ± 7.6 (range 4–36) months. The mean operation time was 56.8 ± 8.7 (range 45–80) min. The entire patients had bony union at an average of 10.5 ± 1.9 (range 8–14) weeks. At the final follow-up, the mean range of postoperative ROM was 123.4° ± 14.6° (range 95°–140°), and the functional score was 28.7 ± 1.2 (range 26–30) points. No patient exhibited internal fixation failure, and no symptomatic implants or skin complications were recorded.ConclusionsThe fixation approach using K-wires combined with 5-Ethibond has a lower complication rate and delivers superior clinical results. This research reveals that such technology is a safe and prospective substitute for conventional metal fixation approaches

Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded similar to 0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of similar to 81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs

The effect of curing conditions on the expansion efficiency of MgO expansion agent

In order to further promote the research and application of MgO expansion agent in concrete field, this paper carried out the effect of different humidity and temperature conditions on the expansion properties of mortar and mortar specimens mixed with MgO expansion agent. In addition, the mechanism of the factors affecting the sensitivity of the MgO expansion agent is revealed by combining microscopic technology. The results show that the higher the curing temperature and the greater the curing humidity, the greater the expansion efficiency of the MgO expansion agent. The temperature of 20~40°C has no obvious effect on the efficiency of the MgO expansion agent, but the expansion value of the specimen doubles as the temperature rises to 40~80°C. Besides, the higher the curing humidity, the better the expansion efficiency of the MgO expansion agent, but the MgO expansion agent is more sensitive to the low humidity environment, and the specimen shrinks in the lower humidity environment (RH=60%)