A Study on the Hysteresis Effect and Spectral Evolution in the Mini-Outbursts of Black Hole X-Ray Binary XTE J1550-564

One normal outburst and three mini-outbursts have been detected by Rossi X-ray Timing Explorer satellite after 2000 in the well-known black hole X-ray binary XTE J1550-564. In this work, we explore the hysteresis effect of the four outbursts, which is a phenomenon that a similar spectral state transition occurs at different luminosity in an outburst of black hole X-ray binary. A q-like track was found in the hardness-intensity diagram of the normal outburst in 2000 but not in the three mini-outbursts that only occur in the Low/Hard state. The results demonstrate that the hysteresis effect is not apparent in the three mini-outbursts and the X-ray spectra are harder than that of the normal outburst at the same photon count rate. Furthermore, the results of the correlation analysis show that the Γ − F2−10keV correlation of mini-outburst maintain negative in the Low/Hard state with the harder spectra than that of the normal outburst at the same X-ray flux. The X-ray spectral evolution can be well-explained by the state-transition model from the Shakura–Sunyaev disk to the advection-dominated accretion flow, which implies that the three mini-outbursts of XTE J1550-564 might originate from a smaller discrete accretion event.

State Transition Modeling in Ultimate Frisbee: Adaptation of a Promising Method for Performance Analysis in Invasion Sports

Although the body of literature in sport science is growing rapidly, certain sports have yet to benefit from this increased interest by the scientific community. One such sport is Ultimate Frisbee, officially known as Ultimate. Thus, the goal of this study was to describe the nature of the sport by identifying differences between winning and losing teams in elite-level competition. To do so, a customized observational system and a state transition model were developed and applied to 14 games from the 2017 American Ultimate Disc League season. The results reveal that, on average, 262.2 passes were completed by a team per game and 5.5 passes per possession. More than two-thirds of these passes were played from the mid zone (39.4 ± 6.57%) and the rear zone (35.2 ± 5.09%), nearest the team’s own end zone. Winning and losing teams do not differ in these general patterns, but winning teams played significantly fewer backward passes from the front zone to the mid zone, nearest the opponent’s end zone than losing teams (mean difference of −4.73%, t(13) = −4.980, p < 0.001, d = −1.16). Furthermore, losing teams scored fewer points when they started on defense, called breakpoints (mean difference of −5.57, t(13) = −6.365, p < 0.001, d = 2.30), and committed significantly more turnovers per game (mean difference of 5.64, t(13) = 5.85, p < 0.001, d = −1.18). Overall, this study provides the first empirical description of Ultimate and identifies relevant performance indicators to discriminate between winning and losing teams. We hope this article sheds light on the unique, but so far overlooked sport of Ultimate, and offers performance analysts the basis for future studies using state transition modeling in Ultimate as well as other invasion sports.

Effective Capacity and Interference Analysis in Multiband Dynamic Spectrum Sensing

In this paper, the performance of multichannel transmission in cognitive radio is studied. Both QoS constraints and interference limitations are considered. The activities of the primary users (PU)s are initially detected by cognitive users (CU)s who perform sensing process over multiple channels. They transmit in a single channel at variable power and rates depending on the channel sensing decisions and the fading environment. The cognitive operation is modeled as a state transition model in which all possible scenarios are studied. The QoS constraint of the cognitive users is investigated through statistical analysis. Analytical form for the effective capacity of the cognitive radio channel is found. Optimal power allocation and optimal channel selection criterion are obtained. Impact of several parameters on the transmission performance, as channel sensing parameters, number of available channels, fading and other, are identified through numerical example.

Effective Capacity and Interference Analysis in Multiband Dynamic Spectrum Sensing

In this paper, the performance of multichannel transmission in cognitive radio is studied. Both QoS constraints and interference limitations are considered. The activities of the primary users (PU)s are initially detected by cognitive users (CU)s who perform sensing process over multiple channels. They transmit in a single channel at variable power and rates depending on the channel sensing decisions and the fading environment. The cognitive operation is modeled as a state transition model in which all possible scenarios are studied. The QoS constraint of the cognitive users is investigated through statistical analysis. Analytical form for the effective capacity of the cognitive radio channel is found. Optimal power allocation and optimal channel selection criterion are obtained. Impact of several parameters on the transmission performance, as channel sensing parameters, number of available channels, fading and other, are identified through numerical example.

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in post-transcriptional regulation of gene expression and have been shown to hold prognostic value in a variety of settings, including acute myeloid leukemia (AML). However, the temporal dynamics of miRNA expression profiles as it relates to AML initiation and progression is poorly understood. Using serial samples from a mouse model of AML, we show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. The AML state-transition was visualized and modeled by constructing an AML state-space from singular value decomposition of the time-series miRNA sequencing data. Within the AML state-space, we identified critical points of AML development characterized by unique differentially expressed miRNAs compared to healthy controls at critical points of leukemogenesis (early, transition, and late). Interestingly, we observed that changes in miRNA expression during leukemogenesis followed two patterns: 1) a monotonic pattern with continuously increasing or decreasing expression; and 2) a non-monotonic pattern with a local maximum or minimum at the transition critical point which was the “point of no-return” from health to AML. We validated the AML state-space and dynamics in an independent cohort of mice and demonstrated the state-transition model accurately predicted time to AML. Of note, we show that the miRNA-derived state-transition model produced a state-space and critical points that were strikingly similar, but not identical to that produced by the coding (i.e., messenger [m]RNA-based) transcriptome. This indicates that while both miRNA and mRNA expression may provide similar information, they also capture independent features of AML state-transition.SignificanceWe show that the microRNA transcriptome undergoes a global state transition during the initiation and progression of acute myeloid leukemia, and accurately predicts time to disease development.

Validation Method to Improve Behavioral Flows on UML Requirements Analysis Model by Cross-Checking with State Transition Model

We propose a method to evaluate and improve the validity of required specifications by comparing models from different viewpoints. Inconsistencies are automatically extracted from the model in which the analyst defines the service procedure based on the initial requirement; thereafter, the analyst automatically compares it with a state transition model from the same initial requirement that has been created by an evaluator who is different from the analyst. The identified inconsistencies are reported to the analyst to enable the improvement of the required specifications. We develop a tool for extraction and comparison and then discuss its effectiveness by applying the method to a requirements specification example.

State-Transition Model for Malaria Symptoms

Aims/ objectives: To develop a state-transition model for malaria symptoms. Study design: Longitudinal study. Place and Duration of Study: Department of Mathematics Masinde Muliro University of Science and Technology between January 2015 and December 2015. Methodology: We included 300 students (patients) with liver malaria disease, with or without the medical history of malaria disease, physical examination for signs and symptoms for both specific and non-specific symptom, investigation of the disease through laboratory test (BS test) and diagnostic test results. the focus of this study was to develop state-transition model for malaria symptoms. Bayesian method using Markov Chain Monte Carlo via Gibbs sampling algorithm was implemented for obtaining the parameter estimates. Results: The results of the study showed a significant association between malaria disease and observed symptoms Conclusion: The study findings provides a useful information that can be used for predicting malaria disease in areas where Blood slide test and rapid diagnostic test for malaria disease is not possible.