2,144 research outputs found
Insect pests’ incidence and variations due to forest landscape degradation in the humid forest zone of Southern Cameroon: farmers’ perception and need for adopting an integrated pest management strategy
Group surveys were conducted in five villages in each of the three resource-use zones of the humid forest zone of Southern Cameroon, to assess insect pests’ incidence and the variation due to forest landscape degradation in the agricultural production systems. 389 farmers were interviewed. The results show that: (1) In annual crop systems, insect pests in general rank together with vertebrate pests and diseases amongst the most important agronomic constraints. No differences were found between the intensification zones, except for weeds, which appeared to be a greater constraint in the slightly degraded area. Within the insect pest, the greatest damage to crops according to farmers originated from borers and scales, followed by variegated grasshopper. Only the termites showed a difference between zones - the problem being greatest in the high-degraded areas. (2) In the young perennial crop systems, all the categories of agronomic constraints were equally important. Within the insect pest, mirids were identified as the greatest constraint, followed by borers and caterpillars. There were more variations in the responses according to zones compared to annual crops. Termites and scales were relatively more important in the high-degraded area. In term of control strategy, we recommended an integrated pest management.Keywords: Farmers, pest, insect, annual crop, perennial crop, integrated pest managemen
Optimizing Postprocessing of Range-Gated Viewing Data for Maritime Search and Rescue Operations at Night and in Bad Weather Conditions
At night and in bad weather conditions the detection of persons and objects floating in the sea represents a major challenge for search and rescue operations (SAR). If conventional searchlights are used, backscattering from rain, fog and snow decreases detection range. Therefore, a compact and inexpensive range-gated viewing system which significantly reduces atmospheric backscattering was developed. The instrument was designed for detection ranges of several hundred meters. In this study, different image processing techniques were analyzed in terms of improved object detectability for a human observer and for a machine learning-based object detector, based on a real-world image dataset. On the one hand, noise of the camera is reduced by performing a non-uniformity correction (NUC) and on the other, the dynamic range of the images is adjusted and dark objects are accentuated by equalizing (EQ). The aim of this field study with the subsequent post processing steps was to improve visibility for both human observers and machine learning-based object detectors with low computing power, based on real-world image datasets. The results show that processing requirements are different in both cases, mainly due to human eye perception, which an automated detector does not rely on and therefore the performance of the object detector before the equalizing step is slightly better. However, the NUC improves the image quality in any case
B2G4: A synthetic data pipeline for the integration of Blender models in Geant4 simulation toolkit
The correctness and precision of particle physics simulation software, such
as Geant4, is expected to yield results that closely align with real-world
observations or well-established theoretical predictions. Notably, the accuracy
of these simulated outcomes is contingent upon the software's capacity to
encapsulate detailed attributes, including its prowess in generating or
incorporating complex geometrical constructs. While the imperatives of
precision and accuracy are essential in these simulations, the need to manually
code highly detailed geometries emerges as a salient bottleneck in developing
software-driven physics simulations. This research proposes Blender-to-Geant4
(B2G4), a modular data workflow that utilizes Blender to create 3D scenes,
which can be exported as geometry input for Geant4. B2G4 offers a range of
tools to streamline the creation of simulation scenes with multiple complex
geometries and realistic material properties. Here, we demonstrate the use of
B2G4 in a muon scattering tomography application to image the interior of a
sealed steel structure. The modularity of B2G4 paves the way for the designed
scenes and tools to be embedded not only in Geant4, but in other scientific
applications or simulation software.Comment: submitted for review at the Muographers 2023 conference. Initial
version. 6 pages, 4 figure
Stand-alone cosmic-ray tomography with secondary particles
The imaging technique of cosmic-ray tomography is usually based on the measurement of muon transmission and muon scattering within the examined volume. Secondary particles produced from the interaction of air shower particles with the target material have been proven to carry complementary information directly related to the target material properties. However, this additional information has not been fully exploited so far. Previous work by the authors [Analysis of Secondary Particles as a Complement to Muon Scattering Measurements. Instruments 2022, 6] showed a novel approach utilizing only the information from secondary particles to successfully reconstruct and discriminate a variety of materials in the context of shipping container scanning with an optimal detector setup and background-free environment. This work builds on the previous results and methods, taking more realistic detector parameters into consideration and investigating their impact on material reconstruction and discrimination. A possible detector setup is discussed, allowing the reconstruction of muons and secondary particle tracks. Three key detector parameters are varied with the aim of validating the approach of the previous work in a more realistic scenario. These parameters are the detection efficiency, the spatial resolution, and the spacing between the detector layers
Analysis of Secondary Particles as a Compliment to Muon Scattering Measurements
Cosmic ray tomography is an emerging imaging technique utilizing an ambient source of radiation. One common tomography method is based on the measurement of muons scattered by the examined objects, which allows the reconstruction and discrimination of materials with different properties. From the interaction of air shower particles induced through cosmic rays with the material to be scanned, secondary particles, predominantly photons, neutrons and electrons, can be produced, which carry complementary information about the objects and their materials. However, this information is currently not fully exploited or only studied in coincidence with the incoming air shower particles. Therefore, this work presents a novel approach utilizing only the information from secondary particles to reconstruct and discriminate objects made out of a variety of materials. It also includes a detailed analysis of the kinematics of secondary particles and their dependency on material characteristics. In addition, a reconstruction algorithm to produce 3D maps of the examined volume from the measurement of secondary particles is introduced. This results in a successful reconstruction and differentiation of objects in various geometrical compositions
Ship segmentation and georeferencing from static oblique view images
Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22±10) m for ships detected within a 400 m range, and (53±24) m for ships over 400 m away from the camera
Experimental estimation: A comparison of methods for Corynebacterium glutamicum from lab to microfluidic scale
Knowledge about the specific affinity of whole cells toward a substrate, commonly referred to as , is a crucial parameter for characterizing growth within bioreactors. State-of-the-art methodologies measure either uptake or consumption rates at different initial substrate concentrations. Alternatively, cell dry weight or respiratory data like online oxygen and carbon dioxide transfer rates can be used to estimate . In this work, a recently developed substrate-limited microfluidic single-cell cultivation (sl-MSCC) method is applied for the estimation of values under defined environmental conditions. This method is benchmarked with two alternative microtiter plate methods, namely high-frequency biomass measurement (HFB) and substrate-limited respiratory activity monitoring (sl-RA). As a model system, the substrate affinity of Corynebacterium glutamicum ATCC 13032 regarding glucose was investigated assuming a Monod-type growth response. A of <70.7 mg/L (with 95% probability) with HFB, 8.55 ± 1.38 mg/L with sl-RA, and 2.66 ± 0.99 mg/L with sl-MSCC was obtained. Whereas HFB and sl-RA are suitable for a fast initial estimation, sl-MSCC allows an affinity estimation by determining at concentrations less or equal to the value. Thus, sl-MSCC lays the foundation for strain-specific estimations under defined environmental conditions with additional insights into cell-to-cell heterogeneity
Treatment of axillary hyperhidrosis
Background
Axillary hyperhidrosis characterized by excessive sweating in the axillary regions is a frustrating chronic autonomic disorder leading to social embarrassment, impaired quality of life and usually associated with palmoplantar hyperhidrosis. Identifying the condition and its cause is central to the management.
Aim
The aim of this article is to discuss treatment options for axillary hyperhidrosis.
Methods
Comprehensive literature search using PubMed and Google Scholar was performed to review relevant published articles related to diagnosis and treatment of axillary hyperhidrosis.
Results
Treatment modalities for axillary hyperhydrosis vary from topical and systemic agents to injectables, newer devices and surgical measures. None except for physical measures using devices or surgery, which destroys the sweat glands to remove them, is possibly permanent and most are associated with attendant side effects.
Conclusion
Several treatments including medical and surgical option are available for the treatment of axillary hyperhydrosis. Patient education is important component of its management. Individualized approach of management is necessary for optimal outcome of treatment
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