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If you're the proud owner of a Fire tablet, this guide helps you make the most of its capabilities. Muir will be with you as you take the Fire tablet out of the box, find out how to use the touchscreen and settings, buy and play music and movies, and get connected with social networking sites and your email account.

This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. The use of tablet computers for cephalometric analyses can be recommended.

This paper proposes a method for the point cloud data (PCD) map creation for the 3D LiDAR localization. The features of the method include the creation of a PCD map from a drawing of the buildings and partial scan of the not-existing object of the map by the tablet computer with the LiDAR. In the former, a map creation procedure, including the up- and down-sampling, as well as the processing, with voxel grid filter is established. In the latter, automatic position correction of the tablet scan data is introduced when they are placed to the current PCD map. Experiments are conducted to determine the size of the voxel grid filter and prove the effect of the tablet scan data in enhancing the matching level and the localization accuracy. Finally, the experiment with an autonomous mobile robot demonstrates that a map created using the proposed method is sufficient for autonomous driving without losing the localization.

Critical patient care information is often omitted or misunderstood during handoffs, which can lead to inefficiencies, delays, and sometimes patient harm. We implemented an aviation-style post-anesthesia care unit (PACU) handoff checklist displayed on a tablet computer to improve PACU handoff communication. We developed an aviation-style computerized checklist system for use in procedural rooms and adapted it for tablet computers to facilitate the performance of PACU handoffs. We then compared the proportion of PACU handoff items communicated before and after the implementation of the PACU handoff checklist on a tablet computer. A trained observer recorded the proportion of PACU handoff information items communicated, any resistance during the performance of the checklist, the type of provider participating in the handoff, and the time required to perform the handoff. We also obtained these patient outcomes: PACU length of stay, respiratory events, post-operative nausea and vomiting, and pain. A total of 209 PACU handoffs were observed before and 210 after the implementation of the tablet-based PACU handoff checklist. The average proportion of PACU handoff items communicated increased from 49.3% (95% CI 47.7–51.0%) before checklist implementation to 72.0% (95% CI 69.2–74.9%) after checklist implementation (p < 0.001). A tablet-based aviation-style handoff checklist resulted in an increase in PACU handoff items communicated, but did not have an effect on patient outcomes.

Whether you want to use your new Kindle Fire HDX for work, play, or a little of both, you'll do it all with this easy-to-read reference. From navigating the touchscreen display and getting to know the built-in apps, to accessibility features and the X-Ray feature, this guide has everything you need to know.

Many approaches have been developed to solve the hand–eye calibration problem. The traditional approach involves a precise mathematical model, which has advantages and disadvantages. For example, mathematical representations can provide numerical and quantitative results to users and researchers. Thus, it is possible to explain and understand the calibration results. However, information about the end-effector, such as the position attached to the robot and its dimensions, is not considered in the calibration process. If there is no CAD model, additional calibration is required for accurate manipulation, especially for a handmade end-effector. A neural network-based method is used as the solution to this problem. By training a neural network model using data created via the attached end-effector, additional calibration can be avoided. Moreover, it is not necessary to develop a precise and complex mathematical model. However, it is difficult to provide quantitative information because a neural network is a black box. Hence, a method with both advantages is proposed in this study. A mathematical model was developed and optimized using the data created by the attached end-effector. To acquire accurate data and evaluate the calibration results, a tablet computer was utilized. The established method achieved a mean positioning error of 1.0 mm.

Presents an introduction to Android development, with information on building interactive apps, creating the user interface, setting up databases, using action bars, and making apps fit in with Material Design.

Background The prolonged head forward and neck flexion posture associated with tablet computer use is a known risk factor for neck pain. Neck pain related dysfunction may cause proprioceptive errors and deficits in balance control. Thus, this study aimed to investigate the impact of prolonged tablet computer use with head forward and neck flexion posture on the pain intensity, cervical position sense and balance control in mechanical neck pain patients. The prolonged head forward and neck flexion posture associated with tablet computer use is a known risk factor for neck pain. Neck pain related dysfunction may cause proprioceptive errors and deficits in balance control. Thus, this study aimed to investigate the impact of prolonged tablet computer use with head forward and neck flexion posture on the pain intensity, cervical position sense and balance control in mechanical neck pain patients.The prolonged head forward and neck flexion posture associated with tablet computer use is a known risk factor for neck pain. Neck pain related dysfunction may cause proprioceptive errors and deficits in balance control. Thus, this study aimed to investigate the impact of prolonged tablet computer use with head forward and neck flexion posture on the pain intensity, cervical position sense and balance control in mechanical neck pain patients. Methods Twenty subjects with mechanical neck pain (24.1 ± 3.4 years) and 20 healthy controls (23.7 ± 3.3 years) were recruited from a university environment. A neck pain inducing protocol simulating prolonged tablet computer usage with neck flexion posture was performed by the mechanical neck pain subjects until the point of moderate pain. The neck pain related scores, cervical range of motion and neck tissue hardness were evaluated in both the healthy controls and the neck pain subjects before the neck pain inducing protocol. Differences in the cervical joint position sense, neck tissue hardness and static standing balance performance of the healthy controls and mechanical neck pain subjects were investigated after the neck pain inducing protocol. Results The mechanical neck pain subjects demonstrated a lower cervical range of motion and a harder neck tissue status than the control subjects. The pain intensity reported by the neck pain subjects after the neck pain inducing protocol was similar to that experienced by the subjects in previous pain attacks. Significant differences in the absolute neck joint positioning error were observed between the mechanical neck pain group and the control group. Prolonged tablet computer use with head forward and neck flexion posture did not significantly increase the joint positioning error in the mechanical neck pain group. However, prolonged tablet computer use with head forward and neck flexion posture resulted in a poorer static standing balance performance in the subjects with mechanical neck pain than in the healthy controls. Conclusion The subjects with mechanical neck pain exhibited a decreased cervical range of motion, a harder neck tissue status and greater neck joint position errors than the healthy controls. In addition, prolonged tablet computer use with head forward and neck flexion posture reproduced the neck pain severity and reduced the balance control ability of the mechanical neck pain subjects.