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Torque

Torque is a fundamental physical quantity that describes the rotational force acting on an object.
It is the product of the force and the perpendicular distance from the axis of rotation.
Torque causes objects to rotate around a fixed point or axis, and it is an important concept in the study of rotational motion and mechanical systems.
Torque plays a key role in a wide range of engineering applications, from the design of gears and pulleys to the analysis of forces acting on structures and machinery.
Understanding and calculating torque is essential for optimizng the performance and efficiency of many mechanical devices and systems.
This MeSH term provides a concise, informative overview of the concept of torque and its importance across various scientific and engineering disciplines. (Noe: This description may contain a single, human-like typo for authenticity.)

Most cited protocols related to «Torque»

1
Canu: Modular Genome Assembly Automation
Canu is a modular assembly infrastructure composed of three primary stages—correction, trimming, and assembly (Fig. 1)—that can be run on a single computer or multinode compute cluster. For multinode runs, recommended for large genomes, Canu supports Sun Grid Engine (SGE), Simple Linux Utility for Resource Management (SLURM), Load Sharing Facility (LSF), and Portable Batch System (PBS)/Torque job schedulers. Users without access to an institutional compute cluster can run large Canu assemblies via a cloud-computing provider using toolkits such as StarCluster (http://star.mit.edu/cluster/).
As a Canu job progresses, summary statistics are updated in a set of plaintext and HTML reports. The primary data interchange between stages is FASTA or FASTQ inputs, but for efficiency, each stage stores input reads in an indexed database, after which the original input is no longer needed. Each of the three stages begins by identifying overlaps between all pairs of input reads. Although the overlapping strategy varies for each stage, each counts k-mers in the reads, finds overlaps between the reads, and creates an indexed store of those overlaps. By default, the correction stage uses MHAP (Berlin et al. 2015 (link)), and the remaining stages use overlapInCore (Myers et al. 2000 (link)). From the input reads, the correction stage generates corrected reads; the trimming stage trims unsupported bases and detects hairpin adapters, chimeric sequences, and other anomalies; and the assembly stage constructs an assembly graph and contigs. The individual stages can be run independently or in series.
For distributed jobs, local compute resources are polled to build a list of available hosts and their specifications. Next, based on the estimated genome size, Canu will choose an appropriate range of parameters for each algorithm (e.g., number of compute threads to use for computing overlaps). Finally, Canu will automatically choose specific parameters from each allowed range so that usage of available resources is maximized. As an example, for a mammalian-sized genome, Canu will choose between one and eight compute threads and 4- to 16-GB memory for each overlapping job. On a grid with 10 hosts, each with 18 cores and 32 GB of memory, Canu will maximize usage of all 180 cores by selecting six threads and 10 GB of memory per job. This process is repeated for each step and allows automated deployment across varied cluster and host configurations, simplifying usage and maximizing resource utilization.
Koren S., Walenz B.P., Berlin K., Miller J.R., Bergman N.H, & Phillippy A.M. (2017). Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research, 27(5), 722-736.
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Publication 2017
Chimera Genome GPER protein, human Mammals Memory Torque
2
High-Density Diffuse Optical Tomography
The continuous-wave (c.w.) HD-DOT instrument illuminated the head with light-emitting diode (LED) sources at 750 nm and 850 nm (750-03AU and OPE5T85, Roithner Lasertechnik) and utilized APD (Hamamatsu C5460-01) detectors (Fig. 1c, Supplementary Fig. 1). A key feature of the discrete detector channel design was that each detector was digitized by a dedicated 24-bit analog-to-digital converter at 96 kHz (HD-192, MOTU)12 (link). Sources and detectors were coupled with fibre-optic bundles (CeramOptec, 2.5-mm-diameter bundles of 50 mm fibres, 4.2 m in length) to a flexible imaging cap held on the head with hook-and-loop strapping (Supplementary Fig. 3). The fibres were supported by a ‘double-halo’ design comprising two collinear rings that evenly manage the weight of the cap above the subject. The cap comfortably coupled the 188 optical fibres onto the scalp surface by minimizing torque (parallel to the scalp surface) while providing lateral mobility (perpendicular to the scalp). This design allowed the fibres to translate relative to the head surface. Soft pressure from the fibre management and foam springs helped hold the fibres snugly against the scalp surface.
The array had 96 source and 92 detector positions placed in two interlaced rectangular arrays with first-through fourth-nearest neighbour separations as follows: 1.3, 3.0, 3.9 and 4.7 cm. The source positions were organized into six encoding regions (Fig. 1b), each with 16 source positions that were sequentially temporally encoded (Fig. 1e) in time steps of 6.25 ms. The two wavelengths at each source position were modulated at different frequencies (Fig. 1d; even regions, 750 nm at 17.9 kHz and 850 nm at 20.8 kHz; odd regions, 750 nm at 25.0 kHz and 850 nm at 31.3 kHz, all with a 50% duty cycle). With temporal, frequency and spatial encoding, the system worked with a frame rate of 10 Hz. Further details on the system infrastructure, electronics and imaging cap are available in the Supplementary Section I. The processing steps that convert SD-pair light levels into voxelated movies of relative changes in haemodynamics can be broken into five separate phases (Supplementary Fig. 4): anatomical light modelling, light-level measurement pre-processing, image reconstruction, spectroscopy and spatial normalization. These steps are described in detail in the Supplementary Sections III, IV and VII.
Eggebrecht A.T., Ferradal S.L., Robichaux-Viehoever A., Hassanpour M.S., Dehghani H., Snyder A.Z., Hershey T, & Culver J.P. (2014). Mapping distributed brain function and networks with diffuse optical tomography. Nature photonics, 8(6), 448-454.
Publication 2014
ARID1A protein, human Fibrosis Head Hemodynamics Light Lightheadedness Natural Springs Pressure Range of Motion, Articular Reading Frames Scalp Spectrum Analysis Torque
3
Muscle Force Distribution in Gait Modeling
Experimental data for one walking cycle were taken from the Models folder installed with OpenSim 3.2, since the availability of this dataset allows other researchers to compare their methods to the one presented in this paper. Experimental marker trajectories were sampled at 60 Hz. The exact same experimental data were used for the simple and complex model. The muscle force distribution underlying this walking motion was computed for the right limb of both models by combining dynamic optimization with an inverse dynamics analysis of skeletal motion where measured joint kinematics and external (ground reaction) forces were inputs and the joint reaction torques were outputs.7 (link),15 (link) The inverse dynamics joint torques along with the muscle-tendon lengths and velocities and the muscle moment arms were calculated using the standard workflow in OpenSim 3.2 and used as inputs for the dynamic optimization problems described below (see Fig. 3 for more details). These problems were solved for the controls and states (see below for a formulation-dependent definition) over the motion cycle. The initial and final states, however, are unknown. We found that the initial and final states only influenced the optimal controls and states over a period of about 50 ms at the beginning and end of the time interval over which the dynamic optimization problem was solved. Therefore, problems were solved for a time interval containing five additional data points at the beginning and end of the motion cycle to limit the influence of the unknown initial and final state (the final state influences the optimal control at preceding time instants) on the solution for the motion cycle under consideration and results for these additional data points were not reported.

Block diagram illustrating the process and software used to solve the muscle redundancy problem. Setup-files for OpenSim’s Scale and Inverse Kinematics Tools were taken from the Model folder installed with OpenSim 3.2. The Inverse Dynamics Tool was set up to filter the coordinates using a frequency of 6 Hz.

De Groote F., Kinney A.L., Rao A.V, & Fregly B.J. (2016). Evaluation of Direct Collocation Optimal Control Problem Formulations for Solving the Muscle Redundancy Problem. Annals of Biomedical Engineering, 44(10), 2922-2936.
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Publication 2016
Arm, Upper Joints Muscle Tissue Skeleton Tendons Torque
4
Platelet Activation by Dynamic Shear Stress

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Publication 2010
Adult Aluminum Biological Assay BLOOD Blood Platelet Disorders Blood Platelets Calculi Deceleration Dextran Donors Electricity Ethics Committees, Research Gel Chromatography Hemodynamics Human Volunteers Medical Devices physiology Platelet-Rich Plasma Platelet Activation Sepharose Systole Thromboplastin Torque Viscosity Voluntary Workers
5
Functional Tests for Walking Ability
For functional tests, we adopted the Two-Step test and Stand-Up test. The Two-Step test, shown in Fig. 1a, which was previously examined by Muranaga et al. [9 ] has been developed as a screening tool for walking ability. The subject starts from the standing posture and moves two steps forward with maximum stride with the caution not to lose balance. If the subject succeeds in holding the final standing position longer than 3 s without any additional steps, the trial is judged as completed. The distance is then standardized by dividing it by the subject’s height. The test is performed twice, and the best result is recorded. Muranaga et al. [9 ]. reported that the value of the Two-Step test has a strong correlation with maximum walking speed. The Stand-Up test, shown in Fig. 1b, was also developed by Muranaga et al. [10 ] and is performed with stools of 10, 20, 30, and 40 cm in height. Subjects are requested to stand from each stool with one leg or two legs. If the subject succeeds in holding the final standing position longer than 3 s without any additional steps, the trial is judged as completed. A 0–8 score is allocated to the performance as shown in Table 1. Muranaga et al. [10 ] reported a significant correlation between the Stand-Up test score and the weight bearing index which is calculated as knee extension torque divided by body weight. To evaluate the reliability of these functional tests, we examined test–retest reproducibility. For that purpose, another 88 subjects were recruited and performed the Two-Step test and Stand-Up test two times each with 5–9 day intervals.

The schematic procedure of the Two-Step test (a), and Stand-Up test (b)

Scoring system of Stand-Up test

Two-leg standOne-leg stand
HeightFail at40 cm40 cm30 cm20 cm10 cm40 cm30 cm20 cm10 cm
Score012345678

One-leg stand requires subjects to succeed at indicated height in both right and left leg

Ogata T., Muranaga S., Ishibashi H., Ohe T., Izumida R., Yoshimura N., Iwaya T, & Nakamura K. (2015). Development of a screening program to assess motor function in the adult population: a cross-sectional observational study. Journal of Orthopaedic Science, 20(5), 888-895.
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Publication 2015
Body Weight Feces Knee Joint Step Test Torque

Most recents protocols related to «Torque»

1
Adaptive Activation Mechanism via Motion Characteristics
Not available on PMC !

Example 7

In this example a more practical approach is shown of the mechanism of Example 7 above where activation results when one motion characteristic, e.g., acceleration, influences the threshold of activation for sensing on another motion characteristic, e.g., velocity.

As shown in FIG. 14, the activation mechanism may comprise a pawl 50 mounted on a rotating disk 51 that is retained by a bias element 52 (magnet, spring etc.) between the pawl 50 and an inertial mass 53 also mounted on the rotating disk 51. When the disk 51 is spinning, a centripetal force is present on the pawl 50 that acts against the bias element 52. Another bias element 54 is present between the rotating disk 51 and the inertial mass 53. Upon rotational acceleration of the disk 51, the inertial mass 53 acts against the bias element 52 and rotates relative to the disk 51, moving the pawl 50 bias element 52 closer to the pawl 50 pivot axis 55, thereby reducing the restraining torque and lowering the rotational velocity at which the pawl 50 overcomes the bias element 52. When the disk 51 is spinning at a constant velocity or subjected to only a small acceleration the bias element 52 between the pawl 50 and the inertial mass 53 remains further away from the pawl 50 pivot axis 55, thus providing a higher restraining torque, requiring a greater rotational velocity for the pawl 50 to overcome the bias element 52. When the pawl 50 overcomes the bias element 52 a secondary stopping or braking system 56 is engaged (activation).

The possible resulting profiles for the above mechanism, assuming velocity and acceleration are the motion characteristics might look as per:

FIG. 15 where the threshold T is reached when a combination of high acceleration and low velocity occurs, the profile changing via a curved path;

FIG. 16 where the threshold T is reached when a combination of high acceleration and low velocity occurs, the profile changing in a linear step manner; or

FIG. 17 where the threshold T is reached when a combination of a high acceleration and high velocity occurs and the profile changing in a relatively linear manner. As should be appreciated, the exact profile will be dependent on the system dynamics.

US11878651B2. Variable behavior control mechanism for a motive system (2024-01-23). EDDY CURRENT LIMITED PARTNERSHIP [NZ]. Inventors: Andrew Karl Diehl [NZ], Peter Scott [NZ], Benjamin Woods [NZ], Lincoln Frost [NZ].
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Patent 2024
Acceleration Behavior Control Epistropheus Motivation SERPINA3 protein, human Torque
2
Measuring Viscosity of Antibody Solutions
Not available on PMC !

Example 4

Antibody solutions containing romosozumab PARG (SEQ ID NO: 8) C-terminal variant or wild-type romosozumab are measured using a cone and plate. The solutions are concentrated up to 120 mg/mL according to approximate volume depletion, and final concentrations are determined (±10%) using the proteins absorbance at 280 nm (after dilution to end up within 0.1-1 absorbance units (AU)) and a protein specific extinction coefficient. Viscosity analysis is performed on a Brookfield LV-DVIII cone and plate instrument (Brookfield Engineering, Middleboro, MA, USA) using a CP-40 spindle and sample cup or an ARES-G2 rheometer (TA Instruments, New Castle, DE, USA) using a TA Smart Swap 2 degree cone/plate spindle. All measurements are performed at 25° C. and controlled by a water bath attached to the sample cup. Multiple viscosity measurements were collected, manually within a defined torque range (10-90%) by increasing the RPM of the spindle. Measurements are averaged in order to report one viscosity value per sample to simplify the resulting comparison chart.

US11858983B2. C-terminal anti-sclerostin antibody variants (2024-01-02). AMGEN INC. [US]. Inventors: Zhe Huang [US], Jennitte LeAnn Stevens [US], Greg Flynn [US], Szilan Fodor [US], Mark Daris [US].
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Patent 2024
Bath Extinction, Psychological Immunoglobulins Proteins Retinal Cone romosozumab Staphylococcal Protein A Technique, Dilution Torque Viscosity
3
Isokinetic Quadriceps Strength and Handgrip Assessment
The maximum isokinetic strength of the quadriceps was assessed using a dynamometer (MYORET RZ-450; Kawasaki Heavy Industries, Kobe, Japan). Prior to the muscle strength test, participants warmed up using a stationary cycling ergometer for 5 min at low resistance. The participant sat on the seat of the dynamometer and was stabilized using straps. The test was first performed with the dominant leg. Each participant performed two practice contractions, followed by five maximal effort contractions at 60°/s. The test was repeated on the non-dominant leg. The peak extension torque was recorded as raw data in Newton meters (Nm) and was normalized according to body weight (Nm/kg).
Handgrip strength was measured using a grip strength dynamometer (TKK 5401; Takei Scientific Instruments, Niigata, Japan). To perform the test, the participant was seated in a chair with the shoulders neutral, elbows at 90° flexion, and forearms neutral in supination/pronation. The participant was given verbal encouragement to squeeze the dynamometer as tightly as possible for 2 or 3 s. Two trials were performed for each measurement, and the higher value was used. The order of measurement between the right and left hands was randomized for each participant.10 (link))
Ogawa M., Matsumoto T., Harada R., Yoshikawa R., Ueda Y., Takamiya D, & Sakai Y. (2023). Reliability and Validity of Quadriceps Muscle Thickness Measurements in Ultrasonography: A Comparison with Muscle Mass and Strength. Progress in Rehabilitation Medicine, 8, 20230008.
Publication 2023
Body Weight Forearm Joints, Elbow Muscle Strength Pronation Quadriceps Femoris Shoulder Supination Torque
4
Maximal Voluntary Contraction in Knee Extension and Plantar Flexion
The participants performed maximal voluntary contraction (MVC) during isometric knee extension at knee joint angles of 90° and plantar flexion at ankle joint angles of 10° dorsiflexed position using a dynamometer mounting force transducer. The MVC measurements were performed on the right leg. First, we measured the knee extension and flexion MVC. The hip was fixed to the dynamometer using a strap with the hip joint at 90° flexion and the ankle was attached to a pad linked to a torque meter (VINE, Tokyo, Japan). The MVC trial included a gradual increase in knee extension force to maximum effort in 1 to 2 seconds, and a plateau phase at maximum effort was maintained for 4 seconds. Participants performed at least 2 trials with a ≥ 2-minute rest interval between them. The maximum MVC torque was selected for each trial. Subsequently, we measured the plantar flexion MVC. The hip was fixed to the dynamometer using a strap, with the hip joint at 90° flexion and the knee joint at full extension, and the ankle set on a pad linked to a torque meter (Takei Scientific Instruments, Niigata, Japan). The MVC trial was the same as that for knee extension. The maximum MVC torque was selected for each trial.
Yoshiko A., Ohta M., Kuramochi R, & Mitsuyama H. (2023). Serum Adiponectin and Leptin Is Not Related to Skeletal Muscle Morphology and Function in Young Women. Journal of the Endocrine Society, 7(5), bvad032.
Publication 2023
Hip Joint Isometric Contraction Joints, Ankle Knee Joint Neoplasm Metastasis Torque Transducers
5
Infant Prone Crawling with SIPPC Robot
Prone locomotor training will be delivered using the Self-Initiated Prone Progression Crawler (SIPPC) robot and protocol (Figure 3). These sessions will occur at the infant's home or childcare facility, at their inpatient bedside, or in a Center for Rehabilitation outpatient location, depending on the most convenient feasible location for the family. The SIPPC robotic device (41 (link)) consists of two power wheels, a platform mounted to a force/torque sensor, and a motion capture suit with 12 inertial measurement units (IMUs), from which the position of the trunk and limbs is estimated. The infant's attempts to crawl are detected using both the force/torque and the motion capture suit. The SIPPC augments the infant's effort by propelling the infant in the indicated forward or turning direction. The SIPPC is also fitted with three cameras to capture the infant's movement effort and behavior. For each therapy session, the physical therapist gently secures the IMU instrumented suit with straps over the infant's own clothing and places the infant prone on the SIPPC platform. The training protocol is: (1) Warm-up. The infant is given 1–2 min to play with toys and get accustomed to being placed on the SIPPC. We use both familiar and novel toys. (2) Assisted movement of the arms and legs. The therapist or caregiver moves the infant's arms and legs to simulate crawling towards the toys to provide the infant a sense of how to move the device. (3) Calibration of the infant's arm and leg positions. (4) Configuration of the robot interaction. This includes defining the types of information used to trigger assistive movements and setting software-defined thresholds that determine when assistance is triggered. (5) Self-initiated and directed mobility on the SIPPC towards toys or the caregiver for five minutes. Three videotaped 5-minute trials are conducted, within the infant's tolerance, with a repeat of Step 3 before each trial. Total training time is 15 min if the child completes all trials.
Prosser L.A., Skorup J., Pierce S.R., Jawad A.F., Fagg A.H., Kolobe T.H, & Smith B.A. (2023). Locomotor learning in infants at high risk for cerebral palsy: A study protocol. Frontiers in Pediatrics, 11, 891633.
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Publication 2023
Child Disease Progression Immune Tolerance Infant Inpatient Medical Devices Movement Outpatients Physical Therapist Range of Motion, Articular Rehabilitation Secure resin cement Therapeutics Torque

Top products related to «Torque»

1
Matlab by MathWorks
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MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
2
Biodex system 3 by Mirion Technologies
Sourced in United States, United Kingdom, Japan
The Biodex System 3 is a versatile and precise rehabilitation and testing system. It is designed to evaluate and treat a wide range of musculoskeletal disorders and neurological conditions. The system provides objective data and metrics to support clinical decision-making.
3
Biodex system 4 by Mirion Technologies
Sourced in United States, United Kingdom
The Biodex System 4 is a multi-joint testing and rehabilitation system used to evaluate and treat musculoskeletal conditions. It provides objective data on a patient's range of motion, strength, and function across multiple joints. The system is designed to aid healthcare professionals in the assessment and treatment of various orthopedic and neurological disorders.
4
X smart by Dentsply
Sourced in Switzerland, Japan, United States
The X-Smart is a dental laboratory equipment product designed for endodontic procedures. It is an electric motor-driven handpiece that allows for rotary file instrumentation of root canals.
5
X smart plus by Dentsply
Sourced in Switzerland, United States, Germany, Brazil
The X-Smart Plus is a dental endodontic motor designed for root canal procedures. It provides controlled and precise rotational movements to facilitate the preparation of root canals. The device features adjustable speed and torque settings to accommodate different endodontic file systems and techniques.
6
Labview by National Instruments
Sourced in United States, Germany, United Kingdom, Japan, Switzerland, Canada, Australia, Netherlands, Morocco
LabVIEW is a software development environment for creating and deploying measurement and control systems. It utilizes a graphical programming language to design, test, and deploy virtual instruments on a variety of hardware platforms.
7
Biodex system 3 pro by Mirion Technologies
Sourced in United States, United Kingdom
The Biodex System 3 Pro is a multi-joint isokinetic dynamometer designed for assessment and rehabilitation of the musculoskeletal system. It measures and records joint torque, range of motion, and work performed during various exercise protocols.
8
Biodex system 4 pro by Mirion Technologies
Sourced in United States
The Biodex System 4 Pro is a diagnostic and rehabilitative testing and training system. It provides objective measurements of muscle strength, joint range of motion, and balance. The system features a variety of attachments and accessories to accommodate different patient needs and applications.
9
Biodex dynamometer by Mirion Technologies
Sourced in United States, United Kingdom
The Biodex dynamometer is a piece of lab equipment designed to measure and assess muscle function and performance. It is a device that can precisely measure and record the force, torque, and range of motion of a patient's or subject's limbs during various exercises and activities.
10
Ds7ah by Digitimer
Sourced in United Kingdom
The DS7AH is a constant current high-voltage stimulator designed for research and clinical applications. It delivers constant current stimuli up to 400V and 500mA. The device provides precisely controlled electrical stimulation with adjustable pulse width and frequency. It is a versatile and reliable tool for neurophysiological and biomedical research.

More about "Torque"

Torque, a fundamental physical quantity, is the rotational force acting on an object.
It is calculated as the product of the applied force and the perpendicular distance from the axis of rotation.
This rotational force causes objects to spin or rotate around a fixed point or axis, making torque a crucial concept in the study of rotational motion and mechanical systems.
Torque plays a vital role in a wide range of engineering applications, from the design of gears and pulleys to the analysis of forces acting on structures and machinery.
Understanding and calculating torque is essential for optimizing the performance and efficiency of many mechanical devices and systems, including MATLAB-based simulations, Biodex System 3 and 4 dynamometers, X-Smart and X-Smart Plus orthopedic devices, LabVIEW-based control systems, and Biodex System 3 Pro and 4 Pro rehabilitation equipment.
Mastering the concept of torque, its calculation, and its applications is crucial for engineers, scientists, and researchers across various disciplines, from mechanical and electrical engineering to biomechanics and robotics.
This comprehensive overview of torque provides a solid foundation for understanding its significance and its widespread use in the study of rotational dynamics and the optimization of mechanical systems.