Adaptive Constraint Partition based Optimization Framework for Large-scale Integer Linear Programming(Student Abstract)

Integer programming problems (IPs) are challenging to be solved efficiently due to the NP-hardness, especially for large-scale IPs. To solve this type of IPs, Large neighborhood search (LNS) uses an initial feasible solution and iteratively improves it by searching a large neighborhood around the current solution. However, LNS easily steps into local optima and ignores the correlation between variables to be optimized, leading to compromised performance. This paper presents a general adaptive constraint partition-based optimization framework (ACP) for large-scale IPs that can efficiently use any existing optimization solver as a subroutine. Specifically, ACP first randomly partitions the constraints into blocks, where the number of blocks is adaptively adjusted to avoid local optima. Then, ACP uses a subroutine solver to optimize the decision variables in a randomly selected block of constraints to enhance the variable correlation. ACP is compared with LNS framework with different subroutine solvers on four IPs and a real-world IP. The experimental results demonstrate that in specified wall-clock time ACP shows better performance than SCIP and Gurobi.Comment: To be published in AAAI2023 Student Abstrac

Analysis of ecosystem resilience in Jiuzhaigou Valley Scenic Area under the effect of geohazards

The effects of geohazards on the ecological environment and ecological spatial pattern have received wide attention from scholars. However, the positive role played by ecological restoration projects on the environment and in the reduction of geohazards has usually been neglected. Jiuzhaigou Valley Scenic Area is a world natural heritage area, has a high incidence of geohazards, and is a demonstration area for ecological restoration projects. Based on remote sensing technology, this paper adopted an InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) and ecological landscape index to extract information about spatio-temporal changes in Jiuzhaigou from 2013 to 2020. This study utilized a quantitative analysis of the ecological recoverability of Jiuzhaigou in cases of artificial restoration and spontaneous restoration under different types of geohazards. Results showed that forests play a vital role in maintaining and controlling habitat quality; artificial restoration can significantly ameliorate the impact of geohazards on the scenic area. As of 2020, the forested scenic area recovered 3.868 km2, and the habitat quality index rebounded to 98.88% of the historical high. The ecological restoration project significantly shortened the scenic area recover time of its ecosystem service capability

Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method

The accuracy of data-driven landslide susceptibility mapping (LSM) is closely affected by the quality of non-landslide samples. This research proposes a method combining a self-organizing-map (SOM) and a one-class SVM (SOM-OCSVM) to generate more reasonable non-landslide samples. We designed two steps: first, a random selection, a SOM network, a one class SVM model, and a SOM-OCSVM model were used to generate non-landslide sample datasets. Second, four machine learning models (MLs)—namely logistic regression (LRG), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF)—were used to verify the effects of four non-landslide sample datasets on LSM. From the region along the Sichuan-Tibet Highway, we selected 11 conditioning factors and 1186 investigated landslides to perform the aforementioned experiments. The results show that the SOM-OCSVM method achieves the highest AUC (>0.94) and minimum standard deviation (<0.081) compared with other methods. Moreover, RF achieves the best performance in different datasets compared with other ML models. The aforementioned results prove that the proposed method can enhance the performance of ML models to produce more reliable LSM

Short-term dietary choline supplementation alters the gut microbiota and liver metabolism of finishing pigs

Choline is an essential nutrient for pig development and plays a role in the animal's growth performance, carcass characteristics, and reproduction aspects in weaned pigs and sows. However, the effect of choline on finishing pigs and its potential regulatory mechanism remains unclear. Here, we feed finishing pigs with 1% of the hydrochloride salt of choline, such as choline chloride (CHC), under a basic diet condition for a short period of time (14 days). A 14-day supplementation of CHC significantly increased final weight and carcass weight while having no effect on carcass length, average backfat, or eye muscle area compared with control pigs. Mechanically, CHC resulted in a significant alteration of gut microbiota composition in finishing pigs and a remarkably increased relative abundance of bacteria contributing to growth performance and health, including Prevotella, Ruminococcaceae, and Eubacterium. In addition, untargeted metabolomics analysis identified 84 differently abundant metabolites in the liver between CHC pigs and control pigs, of which most metabolites were mainly enriched in signaling pathways related to the improvement of growth, development, and health. Notably, there was no significant difference in the ability of oxidative stress resistance between the two groups, although increased bacteria and metabolites keeping balance in reactive oxygen species showed in finishing pigs after CHC supplementation. Taken together, our results suggest that a short-term supplementation of CHC contributes to increased body weight gain and carcass weight of finishing pigs, which may be involved in the regulation of gut microbiota and alterations of liver metabolism, providing new insights into the potential of choline-mediated gut microbiota/metabolites in improving growth performance, carcass characteristics, and health

Effects of dietary L-Citrulline supplementation on growth performance, meat quality, and fecal microbial composition in finishing pigs

Gut microbiota play an important role in the gut ecology and development of pigs, which is always regulated by nutrients. This study investigated the effect of L-Citrulline on growth performance, carcass characteristics, and its potential regulatory mechanism. The results showed that 1% dietary L-Citrulline supplementation for 52 days significantly increased final weight, liveweight gain, carcass weight, and average backfat and markedly decreased drip loss (p < 0.05) of finishing pigs compared with the control group. Microbial analysis of fecal samples revealed a marked increase in α-diversity and significantly altered composition of gut microbiota in finishing pigs in response to L-Citrulline. In particular, these altered gut microbiota at the phylum and genus level may be mainly involved in the metabolic process of carbohydrate, energy, and amino acid, and exhibited a significant association with final weight, carcass weight, and backfat thickness. Taken together, our data revealed the potential role of L-Citrulline in the modulation of growth performance, carcass characteristics, and the meat quality of finishing pigs, which is most likely associated with gut microbiota

Investigating the Influences of Tree Coverage and Road Density on Property Crime

With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal’s selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver

Morphology, DNA Phylogeny, and Pathogenicity of Wilsonomyces carpophilus Isolate Causing Shot-Hole Disease of Prunus divaricata and Prunus armeniaca in Wild-Fruit Forest of Western Tianshan Mountains, China

Prunus divaricata and Prunus armeniaca are important wild fruit trees that grow in part of the Western Tianshan Mountains in Central Asia, and they have been listed as endangered species in China. Shot-hole disease of stone fruits has become a major threat in the wild-fruit forest of the Western Tianshan Mountains. Twenty-five isolates were selected from diseased P. divaricata and P. armeniaca. According to the morphological characteristics of the culture, the 25 isolates were divided into eight morphological groups. Conidia were spindle-shaped, with ovate apical cells and truncated basal cells, with the majority of conidia comprising 3–4 septa, and the conidia had the same shape and color in morphological groups. Based on morphological and cultural characteristics and multilocus analysis using the internal transcribed spacer (ITS) region, partial large subunit (LSU) nuclear ribosomal RNA (nrRNA) gene, and the translation elongation factor 1-alpha (tef1) gene, the fungus was identified as Wilsonomyces carpophilus. The 25 W. carpophilus isolates had high genetic diversity in phylogenetic analysis, and the morphological groups did not correspond to phylogenetic groups. The pathogenicity of all W. carpophilus isolates was confirmed by inoculating healthy P. divaricata and P. armeniaca leaves and fruits. The pathogen was re-isolated from all inoculated tissues, thereby fulfilling Koch’s postulates. There were no significant differences in the pathogenicity of different isolates inoculated on P. armeniaca and P. divaricata leaves (p > 0.05). On fruit, G053 7m3 and G052 5m2 showed significant differences in inoculation on P. armeniaca, and G010 5m2 showed extremely significant differences with G004 7m2 and G004 5m2 on P. divaricata (p < 0.05). This is the first report on shot-hole disease of P. armeniaca (wild apricot) leaves and P. divaricata induced by W. carpophilus in China

Diamond with nitrogen: states, control, and applications

The burgeoning multi-field applications of diamond concurrently bring up a foremost consideration associated with nitrogen. Ubiquitous nitrogen in both natural and artificial diamond in most cases as disruptive impurity is undesirable for diamond material properties, eg deterioration in electrical performance. However, the feat of this most common element-nitrogen, can change diamond growth evolution, endow diamond fancy colors and even give quantum technology a solid boost. This perspective reviews the understanding and progress of nitrogen in diamond including natural occurring gemstones and their synthetic counterparts formed by high temperature high pressure (HPHT) and chemical vapor deposition (CVD) methods. The review paper covers a variety of topics ranging from the basis of physical state of nitrogen and its related defects as well as the resulting effects in diamond (including nitrogen termination on diamond surface), to precise control of nitrogen incorporation associated with selective post-treatments and finally to the practical utilization. Among the multitudinous potential nitrogen related centers, the nitrogen-vacancy (NV) defects in diamond have attracted particular interest and are still ceaselessly drawing extensive attentions for quantum frontiers advance

Characterization of Combined Effects of Urban Built-Up and Vegetated Areas on Long-Term Urban Heat Islands in Beijing

With the development of urbanization and industrialization, megacities have experienced more severe surface urban heat island (SUHI) effects. Land surface temperatures (LSTs) are retrieved; spatial distribution of temperature is characterized, and the relationship among temperatures or SUHIs and land-use and land cover (LULC) in Beijing City are discussed. The changing LSTs in Beijing, from 1990 to 2017, were calculated by a radiative transfer equation and mono-window algorithm. To estimate the effect of SUHI, Landsat-8 Thermal Infrared Sensor (TRIS) and Landsat-5 Thematic Mapper (TM) data were selected. There is an increasing trend toward high LSTs for different LULC types. The connection with building and vegetation density is analyzed. Results indicate that for every 1% increase in the density of buildings, the increase in amplitude of temperature in 2017 was twice as large as it was in 1995 for the study area. In terms of normalized difference vegetation index (NDVI) values, the decrease in amplitude of LST was 10 times that of the year 1995, where there is only a slight increase in the NDVI values of the area

Combining spatial response features and machine learning classifiers for landslide susceptibility mapping

Reliable landslide susceptibility mapping (LSM) is essential for disaster prevention and mitigation. This study develops a deep learning framework that integrates spatial response features and machine learning classifiers (SR-ML). The method has three steps. First, depthwise separable convolution (DSC) extracts spatial features to prevent confusion of multi-factor features. Second, spatial pyramid pooling (SPP) extracts response features to obtain features under different scales. Third, the high-level features are fused into prepared ML classifiers for more effective feature classification. This framework effectively extracts and uses different-dimension features of samples, explores ML classifiers for beneficial feature classification, and breaks through the limitation of fixed input sample sizes. In the Yarlung Zangbo Grand Canyon region, data on 203 landslides and 11 conditioning factors were prepared for availability verification and LSM. The evaluation indicated that the area under the receiver operating characteristic curve (AUC) for the proposed SR and SR-ML achieved 0.920 and 0.910, which were 6.6% and 5.6% higher than the random forest (RF, with the highest AUC in ML group) method, respectively. Furthermore, the framework using 64×64 size inputs had the lowest mean error of 0.01, revealing that samples considering landslide scales could improve performance for LSM