Tavis-Cummings models and their quasi-exactly solvable Schrödinger Hamiltonians

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Calculation of the Performance of Communication Systems from Measured Oscillator Phase Noise

Oscillator phase noise (PN) is one of the major problems that affect the performance of communication systems. In this paper, a direct connection between oscillator measurements, in terms of measured single-side band PN spectrum, and the optimal communication system performance, in terms of the resulting error vector magnitude (EVM) due to PN, is mathematically derived and analyzed. First, a statistical model of the PN, considering the effect of white and colored noise sources, is derived. Then, we utilize this model to derive the modified Bayesian Cramer-Rao bound on PN estimation, and use it to find an EVM bound for the system performance. Based on our analysis, it is found that the influence from different noise regions strongly depends on the communication bandwidth, i.e., the symbol rate. For high symbol rate communication systems, cumulative PN that appears near carrier is of relatively low importance compared to the white PN far from carrier. Our results also show that 1/f^3 noise is more predictable compared to 1/f^2 noise and in a fair comparison it affects the performance less.Comment: Accepted in IEEE Transactions on Circuits and Systems-I: Regular Paper

S100B Protein as a Post-traumatic Biomarker for Prediction of Brain Death in Association With Patient Outcomes

Background: S100B is a calcium-binding protein, belonging to the S100 family proteins which are characterized by their high solubility and, currently, comprises 21 members which are expressed in a cell-specific manner. If we can predict the possibility of definite brain death after brain injury, we will rescue some organs of body to transplant proposes. Objectives: In this regard our study focused on the S100B protein value in predicting brain death after head trauma. In this study, the use of serum level of protein S100, 24 hours after trauma has been considered as a reliable index for predicting brain death. Patients and Methods: 72 patients (50 male and 22 female) aged 5 - 80 years old (median 40 ± 17.72 years) with severe head traumas (GCS ≤ 8) were recruited in this cross-sectional study. Glasgow Coma Scale (GCS) and computed tomography (CT) scan findings were recorded for all patients, and then a single 5mL blood sample was obtained from each patient on admission, after 48 hours and a week later or after brain death to determine the level of S100B protein. Results: Primary and the last GCS of patients had a predictive value in determining brain death (P < 0.0005), also there was a significant correlation between GCS and level of S100B protein. There was a significant correlation between CT scan findings and S100B protein only after 48 hours of trauma. Conclusions: Changes in S100B protein, especially the levels of this dimer 48 hours after trauma can be used as marker to predict brain death. Alongside other known prognostic factors such as age, GCS and diameters of the pupils, however, this factor individually can not conclusive predict the patient's clinical course and incidence of brain death. However, it is suitable to use GCS, CT scan, clinical symptoms and biomarkers together for a perfect prediction of brain death

Prediction of Load in Reverse Extrusion Process of Hollow Parts using Modern Artificial Intelligence Approaches

Extrusion is one of the important processes to manufacture and produce military and industrial components. Designing its tools is usually associated with trial and error and needs great expertise and adequate experience. Reverse extrusion process is known as one of the common processes for production of hollow parts with closed ends. The significant load required in formation of a workpiece is one of the existing constraints for the reverse extrusion process. This issue becomes rather difficult especially for the parts having thin walls since its analysis using finite element softwares is exposed to some limitations. In this regard, application of artificial intelligence for prediction of load in the reverse extrusion process will not only save time and money, but also improve quality features of the product. Based on the existing data and methods suggested for variations of punching force through the reverse extrusion process, the system is trained and then performance of the system is evaluated using the test data in this paper. Efficiency of the proposed method is also assessed via comparison with the results of others.DOI:http://dx.doi.org/10.11591/ijece.v4i3.535

Recent advances in non-enzymatic electrochemical detection of hydrophobic metabolites in biofluids

This review focuses on recent advances in non-enzymatic electrochemical biosensors for detection of hydrophobic metabolites. Electrochemical approaches have been widely applied in many established and emerging technologies and a large range of electrochemical biosensors have been used for detection of various hydrophobic metabolites. Despite the progress made in this field, some problems still exist, specifically, electrochemical detection of hydrophobic biomarkers can be challenging in complex biological fluids. In this review, we have highlighted some of the most representative surface modification technologies that have been employed in electrochemical biosensors to counter the problems of poor sensitivity and selectivity towards hydrophobic metabolites. The hydrophobic metabolites discussed in this review include uric acid, epinephrine, cortisol, cholesterol, tyrosine, adenine, guanine, cytosine, and thymine. This is followed by discussion on future research directions for electrochemical sensing of hydrophobic biomarkers