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670 results for “Influence factors”
Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
<p>The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province. </p>
Fig. 2 in A Pasture Of Big Ungulate Animals As Key Ecological Factor Influencing On The Fluctuation Of Natural Habitat Of Steppe Herbivorous Mammals
Fig. 2. The steppe marmot quantity dynamics in the 20–21th century (cattle vs the steppe marmot).
Fig. 2 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 2. Temporal distribution of numbers of ignitions in ATO zone in 2010–2014.
The simulation results of source distribution and influencing factors.
<p>This dataset contains simulation results of audible noise from UHV DC transmission lines based on digital twin. We provide six CSV files.</p> <p>Three source distributions are proposed and the ground audible noise under different distributions is calculated, which is compared with the BPA formula. The simulation data is stored in four files: (1)source_distribution_a.csv (2)source_distribution_b.csv (3) source_distribution_c.csv (4)source_distribution_formula.csv </p> <p>Two influencing factors including temperature and relative humidity in audible noise generation and propagation are analysed. The data is stored in two files: (1)temperature.csv (2)relative humidity.csv</p>
Factors influencing the sustainability of homestead vegetable production intervention in Rufiji, Tanzania: A cross-sectional mixed methods study
<p><strong>Background </strong></p> <p>There is growing evidence that home vegetable gardening interventions improve food security and nutrition outcomes at the family level. This study assessed factors influencing the sustainability of homestead vegetable production intervention, one year after the cessation of external support.</p> <p><strong>Methods</strong></p> <p>This was a cross-sectional study using both quantitative and qualitative data collection methods. A total of 247 randomly selected households that participated in the homestead vegetable intervention were interviewed using a structured questionnaire. The study held four focus group discussions with households that participated in the intervention, and four In-Depth interviews with two extension workers, one community health worker, and one agriculture district officer. Multiple logistic regression for quantitative data and thematic analysis for qualitative data was conducted.</p> <p><strong>Results</strong></p> <p>About 20.24% (50/247) of households sustained homestead vegetable production for one year after the intervention phased out. Lack of seeds (adjusted OR=1.26: CI=0.39-0.89) and either manure or fertilizers (adjusted OR=1.69: CI =1.08-2.63) were significant factors influencing the sustainability of homesteads vegetable production. In the Focus Group discussions (FGDs) and In-Depth Interview (IDIs), all participating women and extension workers reported high cost of water, destruction from free-grazing animals, agriculture pests and diseases, poor soil fertility, shortage of seeds, and lack of capital affected homestead vegetable production sustainability.</p> <p><strong>Conclusion</strong></p> <p>Existing individual, community, and system challenges influence the sustainability of external-funded agriculture and nutrition interventions. The study findings underscore the importance of community authorities, scientists, and policymakers in having a well-thought sustainability plan in all promising external-funded interventions.</p>
Data from: Empirically testing the influence of light regime on diel activity patterns in a marine predator reveals complex interacting factors shaping behaviour
<p>Diel cycles in marine predator diving behaviour centre around the light-mediated diel vertical migration (DVM) of prey, and are considered critical for optimizing foraging and limiting competition across global seascapes. Yet our understanding of predator diel behaviour is based primarily on examining relative depth usage between constant day/night cycles with no formal investigation of how varying light regimes interact with abiotic factors to shape diel activity. The extreme seasonal light regimes (midnight sun, polar night, day/night cycle) in the Arctic provide a unique natural experimental setting to empirically investigate the occurrence and intensity of diel behaviour in marine predators relative to changing light levels while concomitantly assessing interacting abiotic factors. Depth time series data from satellite-linked tags deployed on six belugas for up to 12 months were used to quantify diel behaviour by calculating dissimilarity in time-at-depth between periods of low and high solar altitude on each day. Generalized additive mixed effects models were used to examine the influence of hours of daylight across extreme light cycles, coupled with bathymetry and sea ice concentration; focal diel patterns were further examined relative to the thermal structure of the water column. As predicted, belugas exhibited cathemerality during the midnight sun, and initiated diel behaviour with the onset of the fall day/night cycle, with a marked increase in its intensity with the progression to equal day/night length. Occurrence of diel patterns, however, was complex; ceasing in regions with seafloor depths < 700 m, and occurring with greatest intensity when the water column was thermally homogeneous within the upper 150 m. Through empirical investigation, this study demonstrates that the onset of day/night light cycles and presumably associated prey DVM can modulate predator diel dive behaviour under certain circumstances, but highlights how the complex interaction of abiotic factors with light regime shape dynamic spatiotemporal patterns. These findings, building on a body of recent work, emphasize that the traditional view of the ubiquitous occurrence of diel behaviour tied to DVM at the base of the food web oversimplifies vertical predator-prey interactions, identifying the need for more structured investigation. </p>
Supplementary materials for ""Who makes corporates tell more about CSR?"-Research on the influencing factors of environmental information disclosure"
<p>These are supplementary materials and primary data for article“Who makes corporates tell more about CSR?”-Research on the influencing factors of environmental information disclosure.</p>
Supporting Information for 'in situ fire emission factors for Malaysian tropical peatlands with the first investigation of the influence of physicochemical controls on peat fire emission factor variability'
<p>Two files containing:</p> <p>1. Raw mole fraction retrievals from our OP-FTIR spectra (as described in the paper)</p> <p>2. Calculated emission ratios used for the calculation of emission factors (as published in the paper)</p>
Factors influencing survival of female eastern wild turkeys in northeastern South Dakota
<p>Population growth can be sensitive to changes in survival rates for many avian species. Understanding sources of mortality, and how to mitigate negative effects on survival, can give managers insight into factors contributing to population change. Harvest trends of eastern wild turkeys (<em>Meleagris gallopavo silvestris</em>) in northeastern South Dakota suggest a decline in abundance. We investigated factors influencing survival of wild turkeys to identify potential factors contributing to the decline. We monitored 122 female wild turkeys using VHF radio transmitters from February 2017 to April 2019. Annual survival was 0.52 (95% CI = 0.33–0.64) for juvenile and 0.49 (95% CI = 0.23–0.63) for adult females, respectively. Daily survival probability was significantly lower during the spring (log‐odds ratio [LOR] = −0.9; 95% CI = −1.5–−0.2) and while a juvenile female was incubating (LOR = −0.67; 95% CI = −1.23–−0.09) but not while an adult female was incubating (LOR = −0.35; 95% CI = −0.92–0.24). Mammalian predation was the leading cause of mortality, and female wild turkeys were most vulnerable to predation during the spring while engaging in nesting and rearing of young broods. Wild turkeys were at risk for additional sources of mortality while incubating nests that were not contributors to mortality during other periods of the year, as deaths caused by haying equipment—the second greatest cause of mortality—only occurred while a female was incubating. Reducing female mortality due to haying, by delaying cutting, installing flushing bars on haying equipment, or increasing availability of suitable nesting cover types to reduce the probability of nesting in hayfields, could improve female survival. Annual survival during our study was about 16–29% lower than survival estimates from northeastern South Dakota during the 1990s, suggesting that reduced female survival could be contributing to the apparent decline in wild turkey abundance.</p>
Fig.1 in Dynamics And Factors Influencing Zooplankton In The Lakes Svente, Riča, Dridzis And Geraņimovas-Ilzas (Eastern Latvia)
Fig.1. Location of the studied lakes.
Analysis files for MSC Marc finite element software, Article: The analysis of shrink-fit connection – the methods of heating and the factors influencing the distribution of residual stresses
<p>This archive contains model files for Finite Element Analysis of the shrink-fit connection in crankshaft and the files for charts in GNUPlot.</p>
Figure 1 in Influence of Climatic Factors on the Interannual Changes of Gonadosomatic Index of the Red Mullet Mullus barbatus ponticus in the Coastal Crimean Waters
Figure 1. Scheme of the study area and location of sampling points.
Fig. 1 in Biotic and abiotic factors influencing the prevalence, intensity and distribution of Eucoleus aerophilus and Crenosoma vulpis in red foxes, Vulpes vulpes from Romania
Fig. 1. Distribution of Crenosoma vulpis and Eucoleus aerophilus in Romania.
Fig. 1 in Influence of biotic and abiotic factors on flea species population dynamics on Lasiopodomys brandtii
Fig. 1. Map of the study area.
Data from: Factors Influencing Productivity of Eastern Wild Turkeys in Northeastern South Dakota
<p>Population growth is highly sensitive to changes in reproductive rates for many avian species. Understanding how reproductive rates are related to environmental conditions can give managers insight into factors contributing to population change. Harvest trends of eastern wild turkey (<i>Meleagris gallopavo silvestris</i>) in northeastern South Dakota suggest an apparent decline in abundance in recent years. We therefore investigate factors influencing reproductive success of this important game bird to identify potential factors contributing to this decline. We monitored nesting rate, nest survival, re-nesting rate, clutch size, hatchability, and poult survival of 116 eastern wild turkey hens using VHF radio-transmitters during the springs and summers of 2017 and 2018. Heavier hens were more likely to attempt to nest than lighter hens, and adult hens were more likely to re-nest than yearling hens. Nest survival probability was lowest in agricultural fields relative to all other cover types and positively related to horizontal visual obstruction and distance to the nearest road. Daily nest survival probability demonstrated an interaction between temperature and precipitation, such that nest survival probability was lower on warm, wet days, but lowest on dry days. Egg predation was the leading cause of nest failure, followed by haying of the nest bowl and death of the incubating hen. Poults reared by adult hens had a greater probability of survival than poults reared by yearling hens. Our estimate of survival probability of poults raised by yearling hens was low relative to other studies, which may be contributing to the apparent regional population decline. However, there is little managers can do to influence poult survival in yearling hens. Alternatively, we found nest survival probability was lowest for nests initiated in agricultural fields. Wildlife-friendly harvesting practices in the region such as delayed harvesting or installation of flushing bars could help increase productivity of eastern wild turkey in northeastern South Dakota.</p>
China's agricultural greenhouse gas emission intensity and its influencing factors
<p>This dataset includes the agricultural greenhouse gas emissions intensity of China and various factors that affect agricultural greenhouse gas emissions (agricultural patent intensity, agricultural per capita value added, urbanization rate, environmental investment intensity, and urban rural income gap.</p>
Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital
<p><strong>Figure S1:</strong> Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.<strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community services]. </p> <p><strong>Figure S2:</strong> Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. <strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community Services].</p>
Common predators and factors influencing their abundance in Anopheles funestus aquatic habitats in rural south-eastern Tanzania
<p>Background: The role of larval predators in regulating the malaria vectors population remains relatively unknown. This study aimed to investigate the common predators that co-exist with Anopheles funestus group larvae and evaluate factors that influence their abundance in rural south-eastern Tanzania.</p> <p>Methods: Mosquito larvae and predators were sampled concurrently using standard dipper (350 ml) or 10 L bucket in previously identified aquatic habitats in selected villages in southern Tanzania. Predators were identified using standard identification keys. All positive habitats were geo-located and their physical features characterized. Water physicochemical parameters such as dissolved oxygen (DO), pH, electrical conductivity (EC), total dissolved solids (TDS) and temperature were also recorded.</p> <p>Results: A total of 85 previously identified An. funestus aquatic habitats in nine villages were sampled for larvae and potential predators. A total of 8,295 predators were sampled. Of these Coenagrionidae 57.7% (n=4785), Corixidae 12.8% (n=1,060), Notonectidae 9.9% (n=822), Aeshnidae 4.9% (n=405), Amphibian 4.5% (n=370), Dytiscidae 3.8% (n=313) were common.</p> <p>A total of 5,260 mosquito larvae were sampled, whereby Anopheles funestus group were 60.3% (n= 3,170), Culex spp. 24.3% (n= 1,279), An. gambie s.l. 8.3% (n= 438) and other anophelines 7.1% (n= 373). Permanent and aquatic habitats larger than 100m2 were positively associated with An. funestus group larvae (P<0.05) and predator abundance (P<0.05). Habitats with submerged vegetation were negative associated with An. funestus group larvae (P<0.05). Only dissolved oxygen (DO) was positively and significantly affect the abundance of An. funestus group larvae (P<0.05). While predators' abundance was not impacted by all physicochemical parameters.</p> <p>Conclusion: Six potential predator families were common in aquatic habitats of An. funestus group larvae. Additional studies are needed to demonstrate the efficacy of different predators on larval density and adult fitness traits. Interventions leveraging the interaction between mosquitoes and predators can be established to disrupt the transmission potential and survival of the An. funestus mosquitoes.</p>
The influence of inherent soil factors and agricultural management on soil organic matter
<p>The accumulation of soil organic matter (SOM) is vital to the agronomic and environmental functioning of agroecosystems, yet the relative influence of inherent soil properties and agricultural management practices on SOM dynamics are not often addressed in individual studies. Using a network of 218 operating farm fields across Wisconsin and southern Minnesota, USA, this research employs single variable analysis (ANOVA and regression) and regression tree analysis to assess the effects of soil properties (texture, drainage class, pH) and management variables related to crop rotation, tillage, cover cropping, and manure application on SOM, as well as total organic carbon (TOC) and total nitrogen (TN) in the upper 15 cm. Single variable analysis revealed that greater SOM, TOC, and TN were associated with poorly drained soil, tile-drained fields, high-clay content soil, and high biomass crop rotations. Soil organic matter (SOM) and TOC were strongly related (R<sup>2</sup>=0.71), but different regression trees were produced; SOM was most influenced by clay content, while TOC was most influenced by drainage class. Future assessment for the building of SOM or TOC should be conducted with drainage and texture class categories and on a regional basis, given that these factors influence the practices that occur within landscapes. A rapid building of data sets through unstructured sampling, including an abundance of meta-data, should be a research priority in agricultural science to identify practices to build SOM on a regional basis.</p>
Incidence and influencing factors of occupational pneumoconiosis: A systematic review and meta-analysis
<p><span><strong>Objectives</strong>: </span><span>To determine the incidence of pneumoconiosis worldwide and its influencing factors. </span></p> <p><span><strong>Design</strong>:</span><span> Systematic review and meta-analysis. </span></p> <p><span><strong>Setting</strong>:</span><span> Cohort studies on occupational pneumoconiosis.</span></p> <p><span><strong>Participants</strong>:</span><span> PubMed, Embase, the Cochrane Library, and Web of Science were searched until November 2021. Studies were selected for meta-analysis if they involved at least one variable investigated as an influencing factor for the incidence of pneumoconiosis and reported either the parameters and 95% confidence intervals (CIs) of the risk fit to the data, or sufficient information to allow for the calculation of those values. </span></p> <p><span><strong>Primary outcome measures</strong>:</span><span> The </span><span>pooled incidence of pneumoconiosis and risk ratio (RR) and 95% CIs of influencing factors. </span></p> <p><span><strong>Results</strong>:</span><span> Our meta-analysis included 19 studies with a total of 335,424 participants, of whom 29,972 developed pneumoconiosis. The pooled incidence of pneumoconiosis was 0.093 (95% CI: 0.085~0.135). We identified the following influencing factors: (1) male (RR=3.74; 95%CI 1.31–10.64; P=0.01), (2) smoking (RR=1.80; 95%CI 1.34</span><span>–</span><span>2.43; P=0.0001), (3) tunneling category (RR=4.75; 95%CI 1.96</span><span>–</span><span>11.53; P<0.0001), (4) helping category (RR=0.07; 95%CI 0.13</span><span>–</span><span>0.16; P<0.0001), </span><span>(5) age (the highest incidence occurs between the ages of 50 and 60), </span><span>(6) duration of dust exposure ((RR=4.59, 95% CI 2.41</span><span>–</span><span>8.74, P<0.01), (7) cumulative total dust exposure (CTD) (RR=34.14, 95% CI 17.50</span><span>–</span><span>66.63, P<0.01). A dose-response analysis revealed a significant positive linear dose-response association between the risk of pneumoconiosis and duration of exposure and CTD (P-nonlinearity=0.10, P-nonlinearity=0.16; respectively). The Pearson correlation analysis revealed that silicosis incidence was highly correlated with CSE (r=0.794, P<0.001).</span></p> <p><span><strong>Conclusion</strong>:</span><span> The incidence of pneumoconiosis in occupational workers was 0.093 and seven factors were found to be associated with the incidence, providing some insight into the prevention of pneumoconiosis.</span></p> <p><span>PROSPERO registration number: CRD42022323233.</span></p> <p><span>Abbreviations:</span><span> NOS: Newcastle Ottawa Scale, CWP: Coal Worker's Pneumoconiosis, CTD: cumulative total dust exposure, CSE: cumulative silica exposure.</span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.