The establishment of corridors can offset the negative effects of habitat fragmentation by connecting isolated habitat patches. However, the practical value of corridor planning is minimal if corridor identification is not based on reliable quantitative information about species-environment relationships. An example of this need for quantitative information is planning for giant panda conservation. Although the species has been the focus of intense conservation efforts for decades, most corridor projects remain hypothetical due to the lack of reliable quantitative researches at an appropriate spatial scale. In this paper, we evaluated a framework for giant panda forest corridor planning. We linked our field survey data with satellite imagery, and conducted species occupancy modelling to examine the habitat use of giant panda within the potential corridor area. We then conducted least-cost and circuit models to identify potential paths of dispersal across the landscape, and compared the predicted cost under current conditions and alternative conservation management options considered during corridor planning. We found that due to giant panda's association with areas of low elevation and flat terrain, human infrastructures in the same area have resulted in corridor fragmentation. We then identified areas with high potential to function as movement corridors, and our analysis of alternative conservation scenarios showed that both forest/bamboo restoration and automobile tunnel construction would significantly improve the effectiveness of corridor, while residence relocation would not significantly improve corridor effectiveness in comparison with the current condition. The framework has general value in any conservation activities that anticipate improving habitat connectivity in human modified landscapes. Specifically, our study suggested that, in this landscape, automobile tunnels are the best means to remove current barriers to giant panda movements caused by anthropogenic interferences.

Reproductive monitoring for captive breeding in giant pandas is based on behavioural observation and non-invasive hormone analysis. In urine, interpretation of results requires normalisation due to an animal’s changing hydration. Correction of urinary concentrations based on creatinine is the gold standard. In this study, a largely unexplored, easy-to-perform normalisation technique, based on urinary specific gravity (USpG), was examined and compared to creatinine. To this extent, six cycles from two female pandas (SB741(1) and SB569(5)) were monitored through urine analysis for oestrogen, progesterone, ceruloplasmin and 13,14-dihydro-15-keto-PGF2a (PGFM). The Pearson’s correlation between creatinine and USpG was high (r = 0.805–0.894; p < 0.01), indicative for a similar performance of both normalisation methods. However, generally lower values were observed during pro-oestrus and primary (progesterone) rise. This could be associated with huge shifts in appetite, monitored by faecal output (kg) with an averaged > 50% decrease during oestrus and >50% increase during primary progesterone rise. In parallel, respectively highest and lowest creatinine and USpG levels, were measured, with creatinine obviously more affected as a result of linkage with muscle tissue metabolism affected by reproductive hormones. As a consequence, metabolite levels were significantly different between both corrected datasets with significantly higher oestrogen peak levels during oestrus ranging from 2.13–86.93 and 31.61–306.45 ng/mL (USpG correction) versus 2.33–31.20 and 36.36–249.05 ng/mL Cr (creatinine correction) for SB569 and SB741 respectively, and significant lower progesterone levels during primary progesterone rise ranging from 0.35–3.21 and 0.85–6.80 ng/mL (USpG correction) versus 0.52–10.31 and 2.10–272.74 ng/mL Cr (creatinine correction) for SB569 and SB741 respectively. Consequently, USpG correction rendered unbiased profiles, less subject to variation and metabolic artefacts and therefore allowed a more straightforward identification of peak oestrogen and onset of secondary progesterone rise, being potentially advantageous for future studies unravelling key giant panda reproductive events, including (delayed) implantation. The alternative application of USpG as a normalisation factor was further supported by its easy application and environmental and technical robustness.

Non-traditional data signals from social media and employment platforms for 0553.HK stock analysis

Every model contains the same detection covariates (i.e. temperature and scent lure application).*Ele: elevation; Ftp: forest composition; Dtl: distance to large residence; Bam: bamboo distribution; Slo: slope; Dtr: distance to road; Ntr: type of nature reserve; Age: forest age; Dts: distance to small residence.

The giant panda (Ailuropoda melanoleuca) is one of the most famous flagship species for conservation, and its draft genome has recently been assembled. However, the transcriptome is not yet available. In this study, the blood transcriptomes of three pandas were characterized and about 160 million sequencing reads were generated using Illumina HiSeq 2000 paired-end sequencing technology. The assembly yielded 92 598 transcripts with an average length of 1626 bp and N50 length of 2842 bp. Based on a sequence similarity search against nonredundant (nr) protein database, a total of 38 522 (41.6%) transcripts were annotated. Of these annotated transcripts, 25 142 and 8272 transcripts were assigned to gene ontology terms and clusters of orthologous group, respectively. A search against the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) indicated that 9098 (9.83%) transcripts mapped to 324 KEGG pathways, and the best represented functional categories of pathways were signal transduction and immune system. We have also identified 23 460 microsatellites, 43 560 SNPs as well as 21 456 alternative splicing events in the assembly. Additionally, a total of 24 341 complete open reading frames (ORFs) were detected from the assembly where 1492 ORFs were found to be novel gene loci as these have not been annotated so far in any public database.

Our main question is to find out what San Francisco's road safety problems are and what the city is doing to fix them. Our first approach is to see if there is any correlation between specific populations by census tract and the collision rates. If the approach fails, the alternative is to look at how the collision rates are correlated with the public safety projects. By looking at how the projects have impacted road safety, we can assess whether the city is on the right track with the projects, or if the projects are a waste of time and money. Our original proposal was to analyze traffic in San Francisco. That was when we assumed we were able to use the data from Uber Movement. Due to certain constraints that will be mentioned in the Data Sources section, we were unable to perform such analysis. Hence, we switched to analyzing road safety instead.Notable Modules Used: Python: pandas, geopandas, shapely, matplotlib, scipy ArcGIS: aggregate_points

The first two rows of a pandas DataFrame ready to be used with GLAM.

The global market for Communication Panda Polarization-Maintaining Fiber is experiencing robust growth, driven by the increasing demand for high-bandwidth, high-speed communication networks. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of 5G and fiber-to-the-home (FTTH) deployments necessitates the use of polarization-maintaining fiber to ensure signal integrity and minimize signal loss over long distances. Furthermore, advancements in sensing technologies and the expanding application of polarization-maintaining fibers in various industries, including telecommunications, data centers, and scientific research, contribute significantly to market growth. The diverse range of applications, including polarization devices, deflection jumpers, and polarization-sensitive devices, further drives market expansion. While challenges such as high initial investment costs for infrastructure upgrades and potential competition from alternative technologies exist, the overall market outlook remains optimistic due to the compelling advantages of polarization-maintaining fibers in ensuring reliable and efficient data transmission. Segmentation analysis reveals that the 1550nm operating wavelength segment currently holds the largest market share, driven by its widespread adoption in long-haul optical communication systems. However, the 980nm and 1310nm segments are expected to witness substantial growth, propelled by increasing demand for shorter-reach applications and metropolitan area networks. Geographically, North America and Asia Pacific are expected to dominate the market, benefiting from advanced telecommunication infrastructure and significant investments in 5G and FTTH deployments. Key players such as Corning, Fujikura, and Yangtze Optical Fibre and Cable are strategically expanding their manufacturing capabilities and product portfolios to meet the rising global demand. The competitive landscape is characterized by both established players and emerging companies, fostering innovation and driving down costs. Overall, the Communication Panda Polarization-Maintaining Fiber market presents a lucrative investment opportunity, characterized by continuous technological advancements and escalating global demand for high-performance optical communication solutions.

How-To

# required imports
import numpy as np
import pandas as pd
import pyarrow

# load data
data = pd.read_feather(path_to_data)

where path_to_data represents the path to the .feather file you are trying to use.

Content

This dataset is approximately equivalent to the TPS Sep 2021 dataset, with the following exceptions:

• Where possible each datatype has been cast to it's lowest subtype
• Added an extra column kfold defining a cross-validation scheme using StratifiedKFold
• It has been saved in a .feather format for quicker loading