A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset.

Fatigue life prediction of Inconel 718 fabricated by laser powder bed fusion was investigated using a miniature specimen tests method and machine learning algorithms. A small dataset-based machine learning framework integrating thirteen kinds of algorithms was constructed to predict the pore-influenced fatigue life. The method of selecting random seeds was employed to evaluate the performance of the algorithms, and then the ranking of various machine learning algorithms for predicting pore-influenced fatigue life on small datasets was obtained by verifying the prediction model twenty or thirty times. The results showed that among the thirteen popular machine learning algorithms investigated, the adaptive boosting algorithm from the boosting category exhibited the best fitting accuracy for fatigue life prediction of the additively manufactured Inconel 718 using the small dataset, followed by the decision tree algorithm in the nonlinear category. The investigation also found that DT, RF, GBDT, and XGBOOST algorithms could effectively predict the fatigue life of the additively manufactured Inconel 718 within the range of 1 × 105 cycles on a small dataset compared to others. These results not only demonstrate the capability of using small dataset-based machine learning techniques to predict fatigue life but also may guide the selection of algorithms that minimize performance evaluation costs when predicting fatigue life.

Maximizing Performance of a Hybrid MnO2/Ni Electrochemical Actuator through Tailoring Lattice Tunnels and Cation Vacancies.

Electrochemical actuators play a key role in converting electrical energy to mechanical energy. However, a low actuation stress and an unsatisfied strain response rate strongly limit the extensive applications of the actuators. Here, we report hybrid manganese dioxide (MnO2) fabricated by introducing ramsdellite (R-MnO2) and Mn vacancies into birnessite (δ-MnO2) nanosheets, which in situ grew on the surface of a nickel (Ni) film, forming a hybrid MnO2/Ni actuator. The actuator demonstrated a rapid strain response of 0.88% s-1 (5.3% intrinsic strain in 6 s) and a large actuation stress of 244 MPa owing to the special R-MnO2 with a high density of sodium ion (Na+)-accessible lattice tunnels, Mn vacancies, and also a high Young's modulus of the hybrid MnO2/Ni composite. Besides, the cyclic stability of the actuator was realized after 1.2 × 104 cycles of electric stimulation under a frequency of 0.05 Hz. The finding of the novel hybrid MnO2/Ni actuator may provide a new strategy to maximize the actuating performance evidently through tailoring the lattice tunnel structure and introducing cation vacancies into electrochemical electrode materials.

Abnormal nocturnal behavior due to hypoglycemia: A case report.

RATIONALE: Hypoglycemia, which is characterized mainly by palpitations, dizziness, and sweating, is common and easy to identify. However, some other symptoms, such as mental disorder or abnormal behavior, are atypical, which may lead to a misdiagnosis of epilepsy, sleepwalking, infarction, or mental disorder, among others. PATIENT CONCERNS: We report a case of a patient with type 2 diabetes who presented with abnormal nocturnal behavior due to hypoglycemia. DIAGNOSIS: Hypoglycemia was diagnosed based on a blood glucose level of 2.1 mmol/L when the patient turned up disoriented unresponsive, unable to understand what was said to him, and producing nonsensical speech. After the patient ate a piece of chocolate, his consciousness returned to normal and all mental symptoms disappeared. Polysomnography (PSG) was synchronously performed. The results of the PSG did not show any signs of abnormality during nonrapid eye movement (NREM) or rapid eye movement (REM) sleep. INTERVENTIONS: We regulated his dose of insulin. OUTCOMES: No additional episodes occurred during the 3-month follow-up. Therefore, the abnormal nocturnal behavior of this patient was determined to be due to hypoglycemia, while the cause of the hypoglycemia was insulin overuse. LESSONS: For physicians, if the cause of abnormal behavior cannot be detected, hypoglycemia should be suspected. Long-term persistent hypoglycemia may cause brain dysfunction and even result in permanent brain damage.

A Polysomnography Study of Kleine-Levin Syndrome in a Single Center.

BACKGROUND: Kleine-Levin syndrome (KLS) is a rare sleep disorder characterized by recurrent episodes of hypersomnia. Polysomnographic (PSG) researches of KLS have been reported only in few publications in the past decades. This study aimed to investigate the characteristics of PSG of KLS. METHODS: This study, which was conducted from March 2010 to July 2014, included seven patients diagnosed with KLS in the Sleep and Wake Disorder Center of Huashan Hospital, Fudan University (Shanghai, China). PSG and multiple sleep latency tests (MSLT) were performed during their episodes and the results were evaluated. RESULTS: Five of the seven patients were males. The mean age at KLS onset was 15.6 ± 3.6 years. The number of episodes ranged from 2 to 7. The duration of episodes lasted from 4 to 11 days. The sleep architecture and proportion were normal in most of the patients. The average value of mean sleep latency was 6.9 ± 4.1 min. No sleep-onset rapid eye movement (SOREM) was detected in three of the patients, whereas one patient experienced one period of SOREM, and such episodes occurred twice in other two patients. CONCLUSIONS: We found that sleep architecture and proportion were normal in most KLS patients. However, the results of PSG and MSLT had no specificity for KLS patients.