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13,276 results for “IT management”
Quantitative ethnobotany of multiple-use species and management of the Yangambi Biosphere Reserve in the Democratic Republic of the Congo
<p>The Yangambi Biosphere Reserve (YBR) is confronted to huge challenge for the livelihood of local communities and biodiversity or natural resources conservation. The lack of scientific information on the spatial distribution of useful woody species is a constraint to develop the sustainable management of forest resources. Hence the relevance of this study, carried out in the villages of Yaselia, Lilanda and Bagbanye, located on the outskirts of this protected area of YBR. It aims to identify the most useful woody species and analyse their socio-cultural use value, number of uses based on local community's commitments as well as to determine their abundance beyond village forests, to contribute to reforestation and conservation policies in the Yangambi landscape. To do this, we combined an ethnobotanical survey with a forest inventory. The results obtained showed that species such as Entandrophragma cylindricum, Petersianthus macrocarpus, Ricinodendron heudelotti, Scorodophloeus zenkeri, Pentaclethra macrophylla, Uapaca guineensis, Blighia welwitschii, Chrysophyllum lacourtianum, Dacryodes edulis, and Gilbertiondendron dewevrei, have high use and cultural value for local communities. Unfortunately, these species of high use and socio-cultural value are in low density in the village forests around the YBR. These results emphasize the necessity to implement as promptly as possible a management strategy including these useful species. This could be done through sustainable traditional agroforestry projects which will valorise existing resources and generate income for the local population to meet their livelihoods and avoid their incursion into the YBR which is the biodiversity sanctuary.</p>
Appendices for the paper "Decision Support Systems Adoption in Pesticide Management"
Open the record for dataset details and reuse information.
Fig. 1 in Pezothrips kellyanus (Thysanoptera: Thripidae) nymphs on orange fruit: importance of the second generation for its management
Fig. 1. Number of Pezothrips kellyanus adults collected with a vacuum device in a navel orchard located in Tavernes (mean ± SE). Trees were treated with chlorpyrifos, spinosad, or spirotetramat.
Fig. 2 in Pezothrips kellyanus (Thysanoptera: Thripidae) nymphs on orange fruit: importance of the second generation for its management
Fig. 2. Percentage (mean ± SE) of fruits slightly and severely damaged by Pezothrips kellyanus nymphs in every insecticide plot in orchards of Alzira (A) and Tavernes (B). Trees were treated with chlorpyrifos, spinosad, or spirotetramat. Different letters indicate significant differences (P <0.05) between treatments (1-way ANOVA followed by Tukey post hoc tests).
AProtocol for Managing Orthodontic Complications in Patients with Thalassemia and Haemoglobin Disorder; Article Review
<p>Hemoglobinopathies are known as haemoglobin production disorders. Familial disorders caused by thalassemia or sickle cell anaemia are autosomal recessive disorders affecting haemoglobin production and structure. Objectives:This article review aimed to clarify protocols for managing orthodontic complications in patients with thalassemia and haemoglobin disorder. The article explains how to deal with common dental and orthodontic issues that patients with these conditions may experience.It provides guidance on diagnosing and treating these issues to ensure the best possible outcomes for these patients. Conclusion:Hemoglobinopathies can cause dental diseases, which can be particularly concerning for children with this condition. While specialists are responsible for treating dental diseases, prevention is the best approach. Physicians with adequate knowledge of the diseases can address this issue safely and effectively. Paying attention to this matter is essential to ensure the best possible patient outcomes.</p>
Figure 4 in Herbicide-resistance management: a common pool resource problem?
Figure 4. Attributes of resource users associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 152).
Figure 3 in Herbicide-resistance management: a common pool resource problem?
Figure 3. Attributes of common pool resources associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 151–152).
Figure 7 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 7. Annual, perennial, dicot, and monocot weed biomass in each weed management treatment pooled across fields. Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 1 in Herbicide-resistance management: a common pool resource problem?
Figure 1. Diagram of pesticide resistance as common property resource based on Miranowski and Carlson (1986). In this conceptualization, the common property resource is pest susceptibility, which is composed of a stock variable and a flow variable. Pest resistance is initially a renewable resource but becomes depleted over time through repeated use of chemicals. Thus, the actions of certain individuals may deplete the resource stock for others.
Figure 6 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 6. Diversity indices of weed communities for all treatments. Weed by species biomass was pooled across fields.Similar letters above bars indicate no significant difference using separate Fisher's LSD tests (P> 0.05).Error bars are standard errors and treatments are abbreviated:NC,nontreated control; SR,seeding rate; IM, interrow mower;WZ,Weed Zapper™.
Figure 5 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 5. Weed biomass in each weed management treatment pooled across all site-years.Biomass was sampled in mid-August after all management tactics had been applied. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 4 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 4. Soybean density in August after all weed management treatments were applied. Data were pooled across all site-years. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 8 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 8. Soybean yield from each weed management treatment pooled across fields. Yield is dry weight corrected to 13% moisture. Similar letters above bars indicate no significant difference using Fisher's LSD test (P> 0.05). Error bars are standard errors, and treatments are abbreviated: NC, nontreated control; SR, seeding rate; IM, interrow mower; WZ, Weed Zapper™.
Figure 2 in Herbicide-resistance management: a common pool resource problem?
Figure 2. This diagram conceptualizes herbicide resistance as a common pool resource problem. Importantly, two conjoined common pool resources—herbicides and the weed gene pool—make up this resource system. Following common pool resource theory, this diagram illustrates the interconnectedness of four stock variables: (1) supply of a herbicide; (2) supply of a weed gene pool susceptible to a herbicide; (3) supply of a weed gene pool resistant to a herbicide; and (4) supply of herbicide efficacy on a weed gene pool. We have also diagramed corresponding flow variables or resource units (RU). In a generalized way, the use of a herbicide application (F1) influences the weed gene pool. However, the weed gene pool (S2 and S3) also acts independently of herbicide use and is influenced by both biological dynamics and social dynamics. Importantly, dynamics involving the weed gene pool are complex and include spatial and temporal variability in both the plant population and weed seedbank. The characteristics of the weed gene pool (S2 and S3) then affect the efficacy of the herbicide (S4) and whether its effectiveness is renewable or whether it becomes a finite stock resource. The quality of the herbicide (S4) may ultimately affect the supply of the herbicide (S1), if declining efficacy takes away from the herbicide's economic and chemical utility. In particular, the quality of these two common pool resources and not simply the quantity makes it a very complex resource arrangement. Factors adding complexity include that the weed gene pool is simultaneously both a pest and a resource. Furthermore, when the weed gene pool is characterized as a resource (its susceptibility to herbicides), the quality of this resource depends primarily upon provisioning practices of the common pool resource that keep the quality intact.In other words, following resource practices that do not allow internal or external resistance into the gene pool is key to maintaining its quality. The lack of quality from underprovisioning may result in a finite stock supply of the resource (i.e., weed gene pool susceptible to herbicides).Overappropriation (i.e., quantity or overharvesting of the resource) is a concern,in that it can be connected to poor provisioning practices.Aside from using a resource unit of herbicide in an application, the resource user does not directly appropriate or harvest from the system. This schematic only covers a generalized scenario, and more finescale analysis is needed to tease apart the complex relationships existing among herbicides and the weed gene pool.
Figure 1 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 1. The interrow mower used in this experiment, attached to a John DeereṜ 5100R tractor with a three-point hitch. The mower is powered with a hydraulic system and was custom made by IRM X4, R-Tech Industries (Homewood, MB, Canada).
Figure 2. The model 6R30 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 2. The model 6R30 Weed Zapper™ used in this experiment. The generator is attached to the back of a John DeereṜ 5100R tractor with a three-point hitch. The 4.6-m electric copper boom is attached to the front of the tractor with a three-point hitch. The Weed Zapper™ was purchased from Old School Manufacturing (Sedalia, MO, USA).
Figure 4 in New directions in weed management and research using 3D imaging
Figure 4. Three-dimensional point cloud reconstructions of soybean (A, top view; B, front view) and cereal rye (Secale cereale L.) (C, top view; D, front view). Note the voids in the soybean point cloud (B) caused by dense canopy cover. Such voids are largely absent in cereal rye (D) due to a more even canopy with greater light penetration.
Figure 3 in New directions in weed management and research using 3D imaging
Figure 3. Data pipeline for calculating canopy height and estimating biomass in the field using red, green, and blue (RGB) images and depth data.
Figure 7 in Interference and management of herbicide-resistant crop volunteers
Figure 7. Individual rows of weedy rice accessions or cultivated rice cultivars 8 d following a post-flood application of benzobicyclon at 371 g ai ha−1. Healthy rows are cultivated rice or resistant weedy rice accessions, whereas chlorotic rows are benzobicyclon-sensitive weedy rice accessions.
Figure 9 in Interference and management of herbicide-resistant crop volunteers
Figure 9. Field-scale evaluation of imidazolinone-resistant (ClearfieldṜ) wheat compared with non–herbicide resistant wheat (including volunteers the following year) in Saskatchewan, Canada, in the early 2000s (adapted from Beckie et al. 2011).
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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.