76 research outputs found
Cognitive-behavioral therapy for externalizing disorders: A meta-analysis of treatment effectiveness
No full textExternalizing disorders are the most common and persistent forms of maladjustment in childhood. The aim of this study was to conduct a meta-analysis evaluating the effectiveness of Cognitive Behavioral Therapy (CBT) to reduce externalizing symptoms in two disorders: Attention Deficit Hyperactivity Disorder (ADHD) and Oppositive Defiant Disorder (ODD). The efficacy of CBT to improve social competence and positive parenting and reduce internalizing behaviors, parent stress and maternal depression was also explored. The database PsycInfo, PsycARTICLES, Medline and PubMed were searched to identify relevant studies. Twenty-one trials met the inclusion criteria. Results showed that the biggest improvement, after CBT, was in ODD symptoms (-0.879) followed by parental stress (-0.607), externalizing symptoms (-0.52), parenting skills (-0.381), social competence (-0.390) and ADHD symptoms (-0.343). CBT was also associated with improved attention (-0.378), aggressive behaviors (-0.284), internalizing symptoms (-0.272) and maternal depressive symptoms (-0.231). Overall, CBT is an effective treatment option for externalizing disorders and is also associated with reduced parental distress and maternal depressive symptoms. Multimodal treatments targeting both children and caregivers' symptoms (e.g. maternal depressive symptoms) appear important to produce sustained and generalized benefits
Echo color doppler ultrasound : A valuable diagnostic tool in the assessment of arteriovenous fistula in hemodialysis patients
No full textA functioning vascular access is a critical requirement to improve the quality of life in hemodialysis patients, so monitoring and surveillance of vascular access play key roles in identifying all dysfunctions and reducing the huge economic cost as well as adequacy of dialysis. In our five-year experience, a study protocol has been used and improved with the help of ultrasonography. Doppler ultrasound is an excellent and sensitive modality for hemodialysis access evaluation, one of techniques employed for arteriovenous fistulae (AVF) study, not only as a preoperative tool, but also in post-operative monitoring of AVF maturation. In addition, the current guidelines recommend AVF surveillance by access blood flow measurement and the correction of hemodynamic stenosis in order to prolong access survival. Doppler ultrasound is readily available, directly used by nephrologists, non-invasive, safe, inexpensive, reproducible, although it requires more clinical skill and time to perform and proper equipment. Ultrasonography imaging can substantially reduce the number of subsequent invasive angiographic procedures. In our opinion, Doppler ultrasound should have a crucial place in the interdisciplinary cooperation in AVF monitoring and it should be included as part of an integrated vascular access management program
Reducing Object Detection Uncertainty from RGB and Thermal Data for UAV Outdoor Surveillance
No full textRecent advances in Unmanned Aerial Vehicles (UAVs) have resulted in their
quick adoption for wide a range of civilian applications, including precision
agriculture, biosecurity, disaster monitoring and surveillance. UAVs offer
low-cost platforms with flexible hardware configurations, as well as an
increasing number of autonomous capabilities, including take-off, landing,
object tracking and obstacle avoidance. However, little attention has been paid
to how UAVs deal with object detection uncertainties caused by false readings
from vision-based detectors, data noise, vibrations, and occlusion. In most
situations, the relevance and understanding of these detections are delegated
to human operators, as many UAVs have limited cognition power to interact
autonomously with the environment. This paper presents a framework for
autonomous navigation under uncertainty in outdoor scenarios for small UAVs
using a probabilistic-based motion planner. The framework is evaluated with
real flight tests using a sub 2 kg quadrotor UAV and illustrated in victim
finding Search and Rescue (SAR) case study in a forest/bushland. The navigation
problem is modelled using a Partially Observable Markov Decision Process
(POMDP), and solved in real time onboard the small UAV using Augmented Belief
Trees (ABT) and the TAPIR toolkit. Results from experiments using colour and
thermal imagery show that the proposed motion planner provides accurate victim
localisation coordinates, as the UAV has the flexibility to interact with the
environment and obtain clearer visualisations of any potential victims compared
to the baseline motion planner. Incorporating this system allows optimised UAV
surveillance operations by diminishing false positive readings from
vision-based object detectors
Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
No full textRecent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR
UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments
No full textResponse efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator
Accuracy of pelvic ultrasound in preoperative evaluation of uterine myomas: a prospective cohort study
No full textSalinity risk prediction using Landsat TM and DEM-derived data
No full textThis paper presents a method for predicting areas at risk from dryland salinity using information derived from multi-temporal Landsat TM satellite images combined with landform data derived from high-quality digital elevation models. The method is applied in the south west agricultural region of Western Australia to predict areas at risk from dryland salinity. This paper presents modifications to previous methods suggested by the use of high-quality elevation data previously unavailable in WA.
The method aims to reproduce expert opinion about the future extent of salinity by using decision trees to determine the relationship between salinity risk and variables that describe various aspects of the landscape. Feature selection procedures are used to determine the optimal subset of variables for predicting risk areas. Preliminary studies were conducted in five subcatchments and the model extrapolated over 30 000 km2 to produce maps of those areas expected to become saline under current management practices
Equipment selection for surface mining: a review
No full textOne of the challenging problems for surface mining operation optimization is choosing the optimal truck and loader fleet. We refer to this problem as the equipment selection problem (ESP). In this paper, we describe the ESP in the context of surface mining and discuss related problems and applications. Within the scope of both the ESP and related problems, we outline modeling and solution approaches. Using operations research literature as a guide, we conclude by pointing to future research directions to improve both the modeling and solution outcomes for practical applications of this problem
On mixed ramsey numbers
No full textAbstractFor positive integers m and n the classical ramsey number r(m, n) is the least positive integer p such that if G is any graph of order p then either G contains a subgraph isomorphic to Km or the complement G of G contains a subgraph isomorphic to Kn. Some authors have considered the concept of mixed ramsey numbers. Given a graph theoretic parameter f, an integer m and a graph H, the mixed ramsey number v(f; m; H) is defined as the least positive integer p such that if G is any graph of order p, then either f(G) ⩾ m or G contains a subgraph isomorphic to H. In this paper we consider the problem of determining the mixed ramsey numbers for vertex linear arboricity and some other generalizations of chromatic number. We discuss the above problem for various structures H such as the complete graph, the claw, the path and the tree. Further, we study the generalized mixed ramsey number v(f;m1, m2,…, m1; Hl + 1, Hl + 2,…, Hk), where the edge set of the complete graph is partitioned into k sets
- …
