Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons. Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries. Results:The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristicarea under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.

In many important industries, such as aerial transportation, offshore wind turbine (OWT) structures, and nuclear power plants that reached or are near the end of their useful life, the structural conditions for continued usage are acceptable. Thus, safe continued operation with required modifications and assessment is more cost-effective than replacing them with a new system. To achieve this goal, many studies have been performed on predicting failure time and remaining useful life, especially in systems that require a very high level of reliability. The present review investigates the articles that predict the remaining useful life or failure time in aviation systems, from three perspectives: 1. Methods and algorithms, especially Machine Learning algorithms, which are growing in recent years in the field of Prognosis and Health Management. 2. Historical predictors such as working life history, environmental conditions, mechanical loads, failure records, asset age, maintenance information, or sensor variables and indicators that can be continuously controlled in each system, such as noise, temperature, vibration, and pressure.3. Challenges of researches on prediction of the failure time of flying systems. The literature assessment in this field shows that using diagnostic and prognostic outputs to identify possible defects and their origin, checking the system's health, and predicting the remaining useful life (RUL) is increasing due to market needs.

Background: Although several different studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports indicate that in many cases, the incidence of stroke in patients with COVID-19 leads to death. Objectives: The obtained mathematical equation in this study can help physicians’ decision-making about treatment and identification of influential clinical factors for early diagnosis. Methods: In this retrospective study, data of 128 patients between March and September 2020 including demographic information, clinical characteristics and laboratory parameters of patients were collected and analyzed statistically. A logistic regression (LR) model was developed to identify the significant variables for the prediction of stroke incidence in patients with COVID-19. Results: Clinical characteristics and laboratory parameters for 128 patients (including 76 males, 52 females; with mean age 57.109 ± 15.97years) were considered as the inputs that included: ventilator dependence, comorbidities and laboratory tests including WBC, Neutrophil, lymphocyte, platelet count, C-Reactive Protein, Blood Urea Nitrogen, Alanine transaminase (ALT), Aspartate transaminase (AST) and LDH. The indexes such as receiver operating characteristic–area under the curve (ROC-AUC) and accuracy, sensitivity, and specificity were considered to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve indicated 97.5% with a 95% confidence interval. Conclusion: The findings showed that ventilator dependence and Cardiac Ejection Fraction and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.

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