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255 results for “Problem solving”
FIGURE 23 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 23. Lectotype of Pristocera cambouei, ♂. A. Head, dorsal view. B. Mandible, latero-frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Forewing. F. Hind wing. G. Tarsal claws. H. Mesopleuron, lateral view. I. Hypopygium, inner view. J. Genitalia, dorsal view. K. Genitalia, ventral view. L. Labels. Scale bars: 100 µm, except 50 µm for B, G, H and 200 µm for E.
FIGURE 22 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 22. Pristocera levicollis, ♀. A. Head, dorsal view. B. Mandible, latero-frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Labels. Scale bars: 500 µm, except 200 µm for B.
FIGURE 21 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 21. Pristocera levicollis, ♂. A. Head, dorsal view. B. Mandible, frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Forewing. F. Hypopygium, inner view. G. Genitalia, dorsal view. H. Genitalia, ventral view. Scale bars: 100 µm, except 200 µm for A, C, D, and 500 µm for E.
FIGURE 18 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 18. Pristocera teetsii, ♀. A. Head, dorsal view. B. Mandible, latero-frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Labels. Scale bars: 100 µm.
FIGURE 17 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 17. Pristocera teetsii, ♂. A. Head, dorsal view. B. Mandible, frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Metasomal petiole, ventral view. F. Pronotum, lateral view. G. Mesopleuron, lateral view. H. Forewing. I. Hamuli, hind wing. J. First abdominal spiracle, dorso-lateral view. K. Hypopygium, inner view. L. Genitalia, dorsal view. M. Genitalia, ventral view. Scale bars: 500 µm, except 200 µm for J and 100 µm for C, D, H, K–M.
FIGURE 13 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 13. Pristocera mauricei, ♂. A. Head, dorsal view. B. Mandible, frontal view. C. Mesosoma, dorsal view. D. Mesosoma, ventral view. E. Metasomal petiole, ventral view. F. Pronotum, lateral view. G. Mesopleuron, lateral view. H. Forewing. I. Hamuli, hind wing. J. Hypopygium, inner view. K. Genitalia, dorsal view. L. Genitalia, ventral view. Scale bars: 100 µm.
FIGURE 14 in Pairs in copulation of the highly dimorphic genus Pristocera Klug (Hymenoptera, Bethylidae) from Madagascar solve taxonomic problems of male-female associations
FIGURE 14. Pristocera mauricei, ♀. A. Head, dorsal view. B. Mandible, latero-frontal view. C. Mesosoma, dorsal view. D. Metasomal petiole, ventral view. E. Labels. Scale bar: 100 µm.
Supplementary Movie of "Remarkable Problem-Solving Ability of Unicellular Amoeboid Organism and its Mechanism"
<p>Supplementary Movie of "Remarkable Problem-Solving Ability of Unicellular Amoeboid Organism and its Mechanism"</p>
Database for Research Projects to Solve the Inverse Heat Conduction Problem
<p>To achieve the optimal performance of an object to be heat treated, it is necessary to know the exact value of the Heat Transfer Coefficient (HTC) describing the amount of heat exchange between the work piece and the cooling medium. The prediction of the HTC is a typical Inverse Heat Transfer Problem (IHCP), which cannot be solved by direct numerical methods. There are numerous techniques used to solve the IHCP based on heuristic search algorithms having very high computational demand. As another approach, it would be possible to use machine-learning methods for the same purpose, which are capable of giving prompt estimations about the main characteristics of the HTC function. As known, a key requirement for all successful machine-learning projects is the availability of high quality training data. In this case, the amount of real-world measurements is far from satisfactory because of the high cost of these tests. As an alternative, it is possible to generate the necessary databases using simulations. This paper presents a novel model for random HTC function generation based on control points and advanced smoothing techniques. As an additional step, a GPU accelerated finite-element method was used to simulate the cooling process resulting in the required temporary data records. These datasets make it possible for researchers to develop and test their IHCP solver algorithms.</p>
Grow up, be persistent and stay focused: keys for solving foraging problems by free-ranging possums
<p><span>Individuals within a species often vary in both their problem-solving approach and ability, affecting their capacity to access novel food resources. Testing problem-solving in free-ranging individuals is crucial for understanding the fundamental ecological implications of problem-solving capacity. To examine the factors affecting problem-solving in free-ranging animals we presented three food-extraction tasks of increasing difficulty to urban common brushtail possums </span><span>(<em>Trichosurus</em> <em>vulpecula</em>)</span><span>. We quantified two measures of problem-solving performance: trial outcome (success/failure) and time to solve, and tested the influence of a range of potential drivers including individual traits (personality, body weight, sex, and age), mechanistic behaviors that quantify problem-solving approach (work time, functional behavior time, behavioral diversity, and flexibility), and prior experience with the puzzles. We found that mechanistic behaviors were key drivers of performance. Individuals displaying greater persistence (higher work and functional behavior time) were more likely to solve a food-extraction task on their first attempt. Individuals also solved problems faster if they were more persistent and had lower behavioral flexibility. Personality indirectly affected time to solve one of the three problems, by influencing time allocated to functional behaviors. Finally, adults solved the most difficult problem faster than juveniles. Overall, our study provides rare insight into the drivers underlying problem-solving performance of wild animals. Such insight could be used to improve management strategies and conservation efforts, such as food or bait deployment, tailored to suit the foraging innovative abilities of target individuals in new and changing environments. </span></p>
FIGURE 2 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 2. Morphochrysis gen. nov., third metasomal tergum, postero-lateral view. A. M. personata, ³; B. M. pulchella, ♀; C. M. diadema, ³; D. M. atechka, ♀; E. M. asahinai, ♀; F. M. urakensis, ³; G. M. mosulensis, ♀; H. M. tedshensis, ♀;
FIGURE 1 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 1. Morphochrysis gen. nov., habitus, dorsal view. A. M. asahinai, ♀; B. M. andradei, ♀; C. M. atechka, ♀; D. M. atechka, ³; E. M. pulchella, ³; F. M. pulchella, ♀; G. M. dives, ³; H. M. dives, ♀; I. M. personata, ³; J. M. rubicunda, ♀; K. M. trisinuata, ♀; L. M. turceyana, ♀;
FIGURE 3 in Solving nomenclatural problems of genus-group names of the cuckoo-wasps (Hymenoptera, Chrysididae): objectively invalid and unavailable names, new type-species designations, new names, a new genus and new synonymies
FIGURE 3. Morphochrysis gen. nov., fifth and sixth female metasomal terga. A–B. M. pulchella; C–D. M. calimorpha; E–F. M. goetheana.
Maternal Problem-Solving in Childhood Cancer
ClinicalTrials.gov study NCT00234793. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Effectiveness of Case Management Versus Case Management Plus Problem-solving Therapy to Treat Depression in Low-income Elders
ClinicalTrials.gov study NCT00540865. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Problem-Solving Education for Caregivers and Patients During Stem Cell Transplant
ClinicalTrials.gov study NCT00766883. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Effect of a Family Empowerment Program on Coping, Problem Solving in Parents, and Quality of Life in Children With Cystic Fibrosis
ClinicalTrials.gov study NCT03800459. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Internet-Based Treatment for Children With Traumatic Brain Injuries & Their Families: Counselor Assisted Problem Solving
ClinicalTrials.gov study NCT00409448. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Managed Problem Solving for ART Adherence and HIV Care Retention Delivered by Community Health Workers
ClinicalTrials.gov study NCT04560621. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pre-and-post Study With a Nested Randomized Trial of Digital Training to Teach Problem-solving Counselling in India
ClinicalTrials.gov study NCT05290142. IPD Sharing: YES. Countries: 1. Publications: 7.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.