Imperfections Formation in Thin Layers of NiTi Triply Periodic Minimal Surface Lattices Fabricated Using Laser Powder Bed Fusion.

In this paper, thin layers of NiTi shape memory alloy (SMA) triply periodic minimal surface lattices (TPMS) are fabricated using laser powder bed fusion (LPBF), considering different laser scanning strategies and relative densities. The obtained architected samples are studied using experimental methods to characterize their microstructural features, including the formation of cracks and balling imperfections. It is observed that balling is not only affected by the parameters of the fabrication process but also by structural characteristics, including the effective densities of the fabricated samples. In particular, it is reported here that higher densities of the TPMS geometries considered are generally associated with increased dimensions of balling imperfections. Moreover, scanning strategies at 45° angle with respect to the principal axes of the samples resulted in increased balling.

Diagnosing Neurally Mediated Syncope Using Classification Techniques.

Syncope is a medical condition resulting in the spontaneous transient loss of consciousness and postural tone with spontaneous recovery. The diagnosis of syncope is a challenging task, as similar types of symptoms are observed in seizures, vertigo, stroke, coma, etc. The advent of Healthcare 4.0, which facilitates the usage of artificial intelligence and big data, has been widely used for diagnosing various diseases based on past historical data. In this paper, classification-based machine learning is used to diagnose syncope based on data collected through a head-up tilt test carried out in a purely clinical setting. This work is concerned with the use of classification techniques for diagnosing neurally mediated syncope triggered by a number of neurocardiogenic or cardiac-related factors. Experimental results show the effectiveness of using classification-based machine learning techniques for an early diagnosis and proactive treatment of neurally mediated syncope.

Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test.

Syncope is the medical condition of loss of consciousness triggered by the momentary cessation of blood flow to the brain. Machine learning techniques have been established to be very effective way to address such problems, where a class label is predicted for given input data. This work presents a Support Vector Machine (SVM) based classification of neuro-mediated syncope evaluated using train-test-split and K-fold cross-validation methods using the patient's physiological data collected through the Head-up Tilt Test in pure clinical settings. The performance of the model has been analyzed over standard statistical performance indices. The experimental results prove the effectiveness of using SVM-based classification for the proactive diagnosis of syncope.

Heart rate variability in early phase of acute myocardial infarction and convalescence.

OBJECTIVE: This study was done to evaluate the degree of change in autonomic activity, manifested as heart rate variability (HRV), from acute phase of MI to convalescent period of AMI. DESIGN: Single center, observational prospective study. PLACE AND DURATION OF STUDY: Department of Cardiology, Shaikh Zayed Hospital, Lahore. The study was completed in one year from June 2000 to July 2001. SUBJECTS AND METHODS: Thirty consecutive patients presenting within 24 hours of an ST segment elevation AMI with or without thrombolysis were included. First 24 hour Holter recording was done within 24-36 hours after AMI and the second was done before discharge. RESULTS: The mean hospital stay was 7.07 +/- 2.56 days. Mean duration between the two recordings was 5.47 +/- 2.36 days. The mean standard deviation of normal sinus interval (SDNN) was 65.07 +/- 25.11msec & 63-97 +/- 23.38msec; mean standard deviation of averaged sinus beats for 5 minutes segments of entire recording (SDANN) was 51.27 +/- 18.57msec and 55.83 +/- 19.65msec and mean SDDN index was 34.57 +/- 17.15msec and 30.57 +/- 14.89msec during early phase of acute MI & pre-discharge recordings respectively. There was no statistically significant difference in HRV between early phase of acute myocardial infarction and of pre-discharge recordings. CONCLUSION: HRV may be monitored for risk stratification at any time post AMI prior to discharge.