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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
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Constructing a risk screen for attention difficulty in U.S. adults using six machine learning methods.

Ying Song1, Yansun Sun2, Zedan Guo1

  • 1Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China.

Frontiers in Artificial Intelligence
|January 28, 2026
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Summary
This summary is machine-generated.

Researchers developed a machine-learning model to identify risk factors for concentration difficulty in US adults. Logistic regression showed the highest clinical value in predicting concentration issues, aiding in management strategies.

Keywords:
NHANESconcentration difficultylogistic regressionmachine learningneuropsychiatric disorders

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Data Science

Background:

  • Concentration difficulty is a key symptom in many neurologic and neuropsychiatric conditions.
  • Epidemiological risk factors for concentration difficulty are not well understood.

Purpose of the Study:

  • To create an interpretable machine-learning model for predicting concentration difficulty risk factors in US adults.
  • To identify key predictors of concentration difficulty using advanced analytical methods.

Main Methods:

  • Utilized data from 9,971 participants in the 2015-2016 National Health and Nutrition Examination Survey (NHANES).
  • Applied and compared six machine-learning algorithms: Logistic Regression, ExtraTrees, Bagging, Gradient Boosting, XGBoost, and Random Forest.
  • Evaluated model performance using AUC, accuracy, precision, specificity, DCA, and calibration plots, constructing a nomogram from the best model.

Main Results:

  • Logistic Regression demonstrated superior predictive performance with AUCs of 0.881 (internal) and 0.818 (external).
  • Decision Curve Analysis indicated logistic regression offered the greatest net benefits in the internal cohort.
  • Random Forest provided the largest net benefits in the external cohort at specific thresholds (0.2-0.3).

Conclusions:

  • Logistic Regression is a highly valuable tool for predicting concentration difficulty.
  • Findings offer critical insights for recognizing, managing, and developing intervention strategies for concentration difficulty.